JOURNAL OF ACOUSTIC EMISSION
VOLUME 25, 2007
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CONTENTS
25-001STRUCTURAL INTEGRITY EVALUATION USING ACOUSTIC EMISSION
KANJI ONO

25-021

ACOUSTIC EMISSION TECHNIQUES STANDARDIZED FOR CONCRETESTRUCTURES
MASAYASU OHTSU, TOSHIRO ISODA and YUICHI TOMODA

25-033

ACOUSTIC EMISSION MONITORING OF REINFORCED CONCRETE
FRAME DURING SEISMIC LOADING
A. ANASTASOPOULOS, S. BOUSIAS and T. TOUTOUNTZAKIS

25-042

ACOUSTIC EMISSION LEAK TESTING OF PIPES FOR PRESSURIZEDGAS USING ACTIVE FIBER COMPOSITE ELEMENTS AS SENSORSANDREAS J. BRUNNER and MICHEL BARBEZAT

25-051

ACOUSTIC EMISSION TECHNIQUE APPLIED FOR MONITORING AND
INSPECTION OF CEMENTITIOUS STRUCTURES ENCAPSULATINGALUMINIUM
L. M. SPASOVA, M. I. OJOVAN and C. R. SCALES

25-069EVALUATION OF REPAIR EFFECT FOR DETERIORATED CONCRETE
PIERS OF INTAKE DAM USING AE ACTIVITYTOMOKI SHIOTANI and DIMITRIOS G. AGGELIS

25-080

ACOUSTIC EMISSION MONITORING OF FLEXURALLY LOADEDARAMID/EPOXY COMPOSITES BY EMBEDDED PVDF SENSORSCLAUDIO CANEVA, IGOR MARIA DE ROSA and FABRIZIO SARASINI

25-092

ACOUSTIC EMISSION SIGNALS GENERATED BY MONOPOLE (PENCILLEAD BREAK) VERSUS DIPOLE SOURCES: FINITE ELEMENTMODELING AND EXPERIMENTS
M. A. HAMSTADISSN 0730-0050 Coden JACEDO

I-1

Copyright©2007Acoustic Emission Group

25-107

HIGH-TEMPERATURE ACOUSTIC EMISSION SENSING USINGALUMINUM NITRIDE SENSORHIROAKI NOMA, TATSUO TABARU, MORITO AKIYAMA, NORIKOMIYOSHI, TOMOHARU HAYANO and HIDEO CHO

25-115DAMPING, NOISE, AND IN-PLANE RESPONSE OF MEMS ACOUSTICEMISSION SENSORSAMELIA P. WRIGHT, WEI WU, IRVING J. OPPENHEIM and DAVID W.
GREVE

25-124IMMERSION-TYPE QUADRIDIRECTIONAL OPTICAL FIBERAE SENSOR FOR LIQUID-BORNE AE
TAKUMA MATSUO, HIDEO CHO, TAKESHI OGAWA and MIKIOTAKEMOTO

25-132A SIMPLE METHOD TO COMPARE THE SENSITIVITY OF DIFFERENTAE SENSORS FOR TANK FLOOR TESTING
HARTMUT VALLEN, JOCHEN VALLEN and JENS FORKER

25-140DAMAGE IN CARBON FIBRE COMPOSITES: THE DISCRIMINATION OF
ACOUSTIC EMISSION SIGNALS USING FREQUENCYMARK EATON, KAREN HOLFORD, CAROL FEATHERSTON and RHYS
PULLIN

25-149CHARACTERISTICS OFACOUSTIC EMISSIONS FROM DEHYDRATINGWOOD RELATED TO SHRINKAGE PROCESSESSABINE ROSNER

25-157CHARACTERIZATION OF TITANIUM HYDRIDES USING A HYBRIDTECHNIQUE OF AE AND FEM DURING INDENTATION TESTYOSHIHIRO TANIYAMA, HIDEO CHO, MIKIO TAKEMOTO and GEN
NAKAYAMA

25-166ANALYSIS OF THE HYDROGEN DEGRADATION OF LOW-ALLOYSTEEL BY ACOUSTIC EMISSIONKRYSTIAN PARADOWSKI, WOJCIECH SPYCHALSKI, KRYSTYNALUBLINSKA and KRZYSZTOF J. KURZYDLOWSKI

25-172

HYDROGEN RELATED BRITTLE CRACKING OF METASTABLE TYPE304 STAINLESS STEELHIDEO CHO and MIKIO TAKEMOTO

25-179ANALYSIS OF ACOUSTIC EMISSION FROM IMPACT AND FRACTURE
OF CFRP LAMINATESKANJI ONO, YOSHIHIRO MIZUTANI AND MIKIO TAKEMOTO

25-187NEURAL NETWORK BURST PRESSURE PREDICTION IN COMPOSITEOVERWRAPPED PRESSURE VESSELSERIC v. K. HILL, SETH-ANDREW T. DION, JUSTIN O. KARL, NICHOLAS
S. SPIVEY and JAMES L. WALKER II
I-2

25-194

ACOUSTIC EMISSION SOURCE LOCATION IN A THICK STEEL PLATEBY LAMB MODES
M. A. HAMSTAD

25-215NOVEL ACOUSTIC EMISSION SOURCE LOCATION
RHYS PULLIN, MATTHEW BAXTER, MARK EATON, KAREN HOLFORD and
SAM EVANS

25-224

ACOUSTIC EMISSION SOURCE LOCATION ON AN ARBITRARYSURFACE BY GEODESIC CURVE EVOLUTION
G. PRASANNA, M. R. BHAT and C. R. L. MURTHY

25-231PROBABILITY OF DETECTION FOR ACOUSTIC EMISSIONADRIAN POLLOCK

25-238PLASTIC-REGION TIGHTENING OF BOLTS CONTROLLED BY
ACOUSTIC EMISSION METHODYOSHIHIRO MIZUTANI, TADASHI ONISHI and MASAMI MAYUZUMI

25-247

REAL-TIME DENOISING OF AE SIGNALS BY SHORT TIME FOURIER
TRANSFORM AND WAVELET TRANSFORM
KAITA ITO and MANABU ENOKI

25-252

MONITORING THE EVOLUTION OF INDIVIDUAL AE SOURCES INCYCLICALLY LOADED FRP COMPOSITES RUNAR UNNTHORSSON,
THOMAS P. RUNARSSON AND MAGNUS T. JONSSON

25-260ON USING AE-HIT PATTERNS FOR MONITORING CYCLICALLYLOADED CFRPRUNAR UNNTHORSSON, THOMAS P. RUNARSSON
and MAGNUS T. JONSSON

25-267

AE MONITORING OF SOIL CORROSION OF BURIED PIPEHIDEO CHO and MIKIO TAKEMOTO

25-276

THIRTY YEARS EXPERIENCE OF INDUSTRIAL APPLICATIONS OF
ACOUSTIC EMISSION TESTING AT TÜV AUSTRIAPETER TSCHELIESNIG

25-286

ACOUSTIC EMISSION TESTING OF SEAM-WELDED HIGH ENERGY PIPINGSYSTEMS IN FOSSIL POWER PLANTSJOHN M. RODGERS

25-294

ACOUSTIC EMISSION AND X-RAY TOMOGRAPHY IMAGING OF SHEAR
FRACTURE FORMATION IN CONCRETE
TATYANA KATSAGA and R. PAUL YOUNG

25-308

GLOBAL MONITORING OF CONCRETE BRIDGE USING ACOUSTICEMISSION
T. SHIOTANI, D. G. AGGELIS and O. MAKISHIMA
I-3

25-316

DEMAND ON FLEXURAL TENSION STEEL REINFORCEMENTANCHORAGE ZONES IN FULL-SCALE BRIDGE BENT CAPSQUANTIFIED BY MEANS OF ACOUSTIC EMISSION
THOMAS SCHUMACHER, CHRISTOPHER HIGGINS, STEVEN GLASER
and CHRISTIAN GROSSE

25-324DAMAGE EVALUATION OF POST-TENSIONED CONCRETE VIADUCTBY AE DURING PROOF LOADINGEDOARDO PROVERBIO,
GIUSEPPE CAMPANELLA and VINCENZO VENTURI

25-331EARLY FAULT DETECTION AT GEAR UNITS BY ACOUSTIC EMISSION
AND WAVELET ANALYSISCHRISTIAN SCHEER, WILFRIED REIMCHE
and FRIEDRICH-WILHELM BACH

25-341APPLICATION OF ACOUSTIC EMISSION IN MONITORING OF FAILURE
IN SLIDE BEARINGSIRENEUSZ BARAN, MAREK NOWAK and WOJCIECH DARSKI

25-348

MAPPING OF WHEEL FLANGE RUBBING ON RAIL USING AE:
LABORATORY TEST
N. A. THAKKAR, R. L. REUBEN and J. A. STEEL

25-355DAMAGE ASSESSMENT OF GEARBOX OPERATING IN HIGH NOISYENVIRONMENT USING WAVEFORM STREAMING APPROACH
DIDEM OZEVIN, JASON DONG, VALERY GODINEZ and MARK CARLOS

25-364

CLUSTERING ANALYSIS OF AE IN ROCK
N. IVERSON, C-S. KAO and J.F. LABUZ

25-373IMPACT IMAGING METHOD TO MAP DAMAGE IN CONCRETE BRIDGEDECK SLABSSTEPHEN D. BUTT, VIDYADHAR LIMAYE and JOHN P. NEWHOOK

Contents25
AUindex25
AusNotes
In Memoriam
EWGAE27

Contents of Volume 25 (2007)
Authors Index of Volume 25 /e-mail addresses of authors
Policy/Author’s Notes/Meeting Calendar/Subscription Information
Professor Reginald Hardy, Jr.
Note on expanded contributions from Cardiff Conference.

JAE Index Folder*

Cumulative Indices of J. of Acoustic Emission, 1982 - 2007

* indicates the availability in CD-ROM only.

Cover photograph is from 25-294 by TATYANA KATSAGA and R. PAUL YOUNG.

I-4

I-1 – I-4
I-5 – I-7
I-8 – I-10
I-11
I-12

Volume 25 (2007) AUTHORS INDEXDIMITRIOS G. AGGELIS,MORITO AKIYAMA,
A. ANASTASOPOULOS,

25-069, 25-308
25-10725-033

FRIEDRICH-WILHELM BACH,
IRENEUSZ BARAN,
MICHEL BARBEZAT,
MATTHEW BAXTER,
M. R. BHAT,
S. BOUSIAS,
ANDREAS J. BRUNNER,
STEPHEN D. BUTT,

25-33125-34125-04225-21525-22425-03325-04225-373

GIUSEPPE CAMPANELLA,
CLAUDIO CANEVA,MARK CARLOS,HIDEO CHO,WOJCIECH DARSKI,
IGOR MARIA DE ROSA,SETH-ANDREW T. DION,
JASON DONG,

25-32425-08025-35525-107, 25-124, 25-157, 25-172, 25-267
25-34125-08025-18725-355

MARK EATON,
MANABU ENOKI,
SAM EVANS,CAROL FEATHERSTON,JENS FORKER,
STEVEN GLASER,
VALERY GODINEZ,DAVID W. GREVE,CHRISTIAN GROSSE,

25-140, 25-215
25-24725-21525-14025-13225-31625-35525-11525-316

M. A. HAMSTAD,
TOMOHARU HAYANO,CHRISTOPHER HIGGINS,ERIC v. K. HILL,
KAREN HOLFORD,
N. IVERSON,

25-092, 25-194
25-10725-31625-18725-140, 25-215
25-364TOSHIRO ISODA,
KAITA ITO,MAGNUS T. JONSSON,
C-S. KAO,JUSTIN O. KARL,TATYANA KATSAGA,KRZYSZTOF J. KURZYDLOWSKI

25-02125-24725-252, 25-260
25-36425-18725-29425-166
I-5

J.F. LABUZ,VIDYADHAR LIMAYE,
KRYSTYNA LUBLINSKA,
O. MAKISHIMA,
TAKUMA MATSUO,MASAMI MAYUZUMI,NORIKO MIYOSHI,YOSHIHIRO MIZUTANI
C. R. L. MURTHY,
GEN NAKAYAMA,JOHN P. NEWHOOKHIROAKI NOMA,MAREK NOWAK,

25-36425-37325-16625-30825-12425-23825-10725-179, 25-238
25-22425-15725-37325-10725-341TADASHI ONISHI,
TAKESHI OGAWA,
MASAYASU OHTSU,
M. I. OJOVAN,
KANJI ONO,
IRVING J. OPPENHEIM,DIDEM OZEVIN,

25-23825-12425-02125-05125-001, 25-179
25-11525-355KRYSTIAN PARADOWSKI,ADRIAN POLLOCK,
G. PRASANNA,EDOARDO PROVERBIO,
RHYS PULLIN,

25-16625-23125-22425-32425-140, 25-215

WILFRIED REIMCHE,
R. L. REUBEN,
JOHN M. RODGERS,IGOR MARIA DE ROSA,SABINE ROSNER,THOMAS P. RUNARSSON,

25-33125-34825-28625-08025-14925-252, 25-260

FABRIZIO SARASINI,
C. R. SCALES,CHRISTIAN SCHEER,THOMAS SCHUMACHER,TOMOKI SHIOTANI,
L. M. SPASOVA,NICHOLAS S. SPIVEY,WOJCIECH SPYCHALSKI,
J. A. STEEL,

25-08025-05125-33125-31625-069, 25-308
25-051
25-18725-16625-348TATSUO TABARU,MIKIO TAKEMOTO,YOSHIHIRO TANIYAMA,
N. A. THAKKAR,

25-10725-124, 25-157, 25-172, 25-179, 25-267
25-15725-348
I-6

YUICHI TOMODA,
T. TOUTOUNTZAKIS,PETER TSCHELIESNIG,

25-02125-03325-276

RUNAR UNNTHORSSON,
HARTMUT VALLEN,JOCHEN VALLEN,
VINCENZO VENTURI,JAMES L. WALKER II,
AMELIA P. WRIGHT,
WEI WU,
R. PAUL YOUNG,

25-252, 25-260
25-13225-13225-32425-18725-11525-11525-294

I-7

JOURNAL OF ACOUSTIC EMISSION
Editor: Kanji Ono
Associate Editors: A. G. Beattie, T. F. Drouillard, M. Ohtsu and W. H. Prosser
1. Aims and Scope of the Journal
Journal of Acoustic Emission is an international journal
designed to be of broad interest and use to both researcher andpractitioner of acoustic emission. It will publish original contributions of all aspects of research and significant engineering
advances in the sciences and applications of acoustic emission.
The journal will also publish reviews, the abstracts of papers
presented at meetings, technical notes, communications andsummaries of reports. Current news of interest to the acoustic
emission communities, announcements of future conferences andworking group meetings and new products will also be included.
Journal of Acoustic Emission includes the following classesof subject matters;
A. Research Articles: Manuscripts should represent completed original work embodying the results of extensive investigation. These will be judged for scientific and technical merit.
B. Applications: Articles must present significant advancesin the engineering applications of acoustic emission. Material will
be subject to reviews for adequate description of procedures,substantial database and objective interpretation.
C. Technical Notes and Communications: These allow publications of short items of current interest, new or improved experimental techniques and procedures, discussion of published articles
and relevant applications.
D. AE Program and Data Files: Original program files anddata files that can be read by others and analyzed will be
distributed in CD-ROM.
Reviews, Tutorial Articles and Special Contributions willaddress the subjects of general interest. Nontechnical part will
cover book reviews, significant personal and technical accomplishments, current news and new products.

policies and other aspects. The Board presently includes thefollowing members:
A. Anastasopoulos
F.C. Beall
J. Bohse
P. ColeL. Golaski
M.A. Hamstad
R. HayK.M. Holford
O.Y. Kwon
J.C. Lenain
G. Manthei
P. Mazal
C.R.L. Murthy
A.A. Pollock
F. Rauscher
I. RomanT. Shiotani
P. Tschliesnig
H. Vallen
M. Wevers
B.R.A. Wood
4. Publication
Journal of Acoustic Emission is published annually in CDROM by Acoustic Emission Group, PMB 409, 4924 Balboa Blvd,
Encino, CA 91316. It may also be reached at 2121H, Engr. V,
University of California, Los Angeles, California 90095-1595(USA). tel. 310-825-5233. FAX 818-990-1686. e-mail:aegroup7@gmail.com or mailto:ono@ucla.edu

2. Endorsement
Acoustic Emission Working Group (AEWG), European
Working Group on Acoustic Emission (EWGAE), have endorsed
the publication of Journal of Acoustic Emission.
3. Governing Body
The Editor and Associate Editors will implement the editorial
policies described above. The Editorial Board will advise the editors on any major change. The Editor, Professor Kanji Ono, has
the general responsibility for all the matters. Associate Editors
assist the review processes as lead reviewers. The members of the
Editorial Board are selected for their knowledge and experienceon AE and will advise and assist the editors on the publication

ISSN 0730-0050 Copyright©2007 Acoustic Emission Group(Greece)(USA)(Germany)(UK)(Poland)(USA)(Canada)(UK)(Korea)(France)(Germany)(Czech Republic)(India)(USA)(Austria)(Israel)(Japan)(Austria)(Germany)(Belgium)(Australia)

5. Subscription
Subscription should be sent to Acoustic Emission Group.
Annual rate for 2008 is US $111.00 including CD-ROM delivery,
by priority mail in the U.S. and by air for Canada and elsewhere.
For additional print copy, add $40. Overseas orders must be paidin US currencies with a check drawn on a US bank. PayPal
payment accepted. Inquire for individual (with institutional order)
and bookseller discounts. See also page I-8.
6. Advertisement
No advertisement will be accepted, but announcements forbooks, training courses and future meetings on AE will be
included without charge.

I-8

CODEN: JACEDO All rights reserved.

Notes for Contributors
at the end of the paper in alphabetical and chronologicalorder. Journal references should be arranged as below.
Titles for journal or book articles are helpful for readers,
but may be omitted.

1. General
The Journal will publish contributions from all parts of theworld and manuscripts for publication should be submitted
to the Editor. Send to:Professor Kanji Ono, Editor - JAE
Rm. 2121H, Engr. V, MSE Dept.
University of California
420 Westwood Plaza,
Los Angeles, California 90095-1595 USA
FAX (1) 818-990-1686
e-mail: ono@ucla.edu

H.L. Dunegan, D.O. Harris and C.A. Tatro (1968), Eng.
Fract. Mech., 1, 105-122.
Y. Krampfner, A. Kawamoto, K. Ono and A.T. Green(1975), "Acoustic Emission Characteristics of Cu Alloys
under Low-Cycle Fatigue Conditions," NASA CR-134766,
University of California, Los Angeles and Acoustic Emission Tech. Corp., Sacramento, April.

Authors of any AE related publications are encouraged tosend a copy to:
Mr. T.F. Drouillard, Associate Editor - JAE
11791 Spruce Canyon Circle
Golden, Colorado 80403 USA

A.E. Lord, Jr. (1975), Physical Acoustics: Principles andMethods, vol. 11, eds. W. P. Mason and R. N. Thurston,Academic Press, New York, pp. 289-353.
Abbreviations of journal titles should follow those used in
the ASM Metals Abstracts. In every case, authors' initials,
appropriate volume and page numbers should be included.
The title of the cited journal reference is optional.

Only papers not previously published will be accepted.
Authors must agree to transfer the copyright to the Journal
and not to publish elsewhere, the same paper submitted toand accepted by the Journal. A paper is acceptable if it is arevision of a governmental or organizational report, or if it
is based on a paper published with limited distribution.
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The language of the Journal is English. All papers should
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All manuscripts will be judged by qualified reviewer(s).
Each paper is reviewed by one of the editors and may besent for review by members of the Editorial Board. The
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This Journal will be primarily distributed electronically by
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We can process color illustration needed to enhance thetechnical content of an article. With the new format,
authors are encouraged to use them.

References to published literature should be quoted in the
text citing authors and the year of publication orconsecutive numbers. These are to be grouped together

I-9

MEETING CALENDAR:EWGAE2008
28th European Conference on Acoustic Emission Testing will be held at Krakow, Poland on Sep.
17-19, 2008. It is organized by Dr. Jerzy Schmidt of Faculty of Mechanical Engineering, Crakow
University of Technology, Krakow. Over 75 papers will be presented. Meeting details can be found at:
http://www.ewgae.eu/
AEWG-51
51st Meeting of AEWG will be held at Fogelman Center, University of Memphis, Memphis,
Tennessee, USA, on October 13-15, 2008. Abstracts should be sent to Dr. Gang Qi,(gangqi@memphis.edu) before July 16, 2008. http://www.aewg.org/
IAES-19, The 19th International AE Symposium
The IAES-19 will be held at Kyoto on Dec. 8-12, 2008. It is organized by Dr. T. Shiotani of Kyoto
University and is sponsored by Japan Society for Non-Destructive Inspection. Abstracts are due Apr. 30,
2008. Details can be found at: http://www.19iaes-kyoto.com/

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For inquiry through Internet, use the following address:
Editor-Publisher
Publication Date of This Issue (Volume 25):
I-10

aegroup7@gmail.com
Kanji Ono Tel. (818) 849-9190
25 March 2008.

In Memoriam

H. Reginald Hardy, Jr. (1931 – 2008)
H. Reginald Hardy Jr., Professor Emeritus of Mining Engineering, Department of Energy andMineral Engineering, Penn State University, 76, of State College, PA, passed away Jan. 22,
2008. Born Aug. 19, 1931, in Ottawa, Canada, Reg was the loyal, loving husband of Margaret
M. (Lytle) married in 1954, survived also by his two sons and a granddaughter.
After getting BSc. from McGill University (1953), MSc. from Ottawa University (1962), both inphysics, and Ph.D. in engineering mechanics from Virginia Polytechnic Institute in 1965, Regjoined the faculty at Penn State in 1966, following 13 years with Canada Department of Energy,
Mines and Resources in Ottawa. During the past 40 years, Dr. Hardy had a passion for his
scientific work in application of acoustic emission/microseismic (AE/MS) techniques to thegeotechnical field. He conducted a number of major field and laboratory research projects anddirected some 40 Ph.D. and Masters students, published numerous research papers ingeotechnical and mining journals, four technical monographs, and 11 conference proceedings.
Dr. Hardy organized six "Conferences on Acoustic Emission/Microseismic Activity in Geologic
Structures and Materials," (1975-1996) and was active on the editorial review committee of the
Journal of Acoustic Emission. His last major work was his book, Acoustic Emission
Microseismic Activity, Vol. 1: Principles, Techniques and Geotechnical Applications (2003). He
was the recipient of the ASTM Hogentogler and Templin Awards, the AEWG Achievement andGold Medal Awards for "Pioneering work in the geologic applications of acoustic emission."
He was working to his last day, perhaps on the second volume of his book. Reg will be sincerely
missed by his family, friends and colleagues. (http://www.legacy.com/Link.asp?I=LS000101786354X)
Kanji Ono
I-11

JOURNAL OF ACOUSTIC EMISSION, VOLUME 25, 2007
Expanded Articles from EWGAE2006 Cardiff Conference
The following eight articles were expanded from the papers originally given at EWGAE27. This
collection has been organized by the Topical Editors, Professor Karen Holford and Dr. Rhys Pullin,
Cardiff University, who also hosted EWGAE27. Their efforts are much appreciated.
25-001STRUCTURAL INTEGRITY EVALUATION USING ACOUSTIC EMISSION
KANJI ONO

25-021

ACOUSTIC EMISSION TECHNIQUES STANDARDIZED FOR CONCRETESTRUCTURES
MASAYASU OHTSU, TOSHIRO ISODA and YUICHI TOMODA

25-033

ACOUSTIC EMISSION MONITORING OF REINFORCED CONCRETE
FRAME DURING SEISMIC LOADING
A. ANASTASOPOULOS, S. BOUSIAS and T. TOUTOUNTZAKIS

25-042

ACOUSTIC EMISSION LEAK TESTING OF PIPES FOR PRESSURIZEDGAS USING ACTIVE FIBER COMPOSITE ELEMENTS AS SENSORSANDREAS J. BRUNNER and MICHEL BARBEZAT

25-051

ACOUSTIC EMISSION TECHNIQUE APPLIED FOR MONITORING AND
INSPECTION OF CEMENTITIOUS STRUCTURES ENCAPSULATINGALUMINIUM
L. M. SPASOVA, M. I. OJOVAN and C. R. SCALES

25-069EVALUATION OF REPAIR EFFECT FOR DETERIORATED CONCRETE
PIERS OF INTAKE DAM USING AE ACTIVITYTOMOKI SHIOTANI and DIMITRIOS G. AGGELIS

25-080

ACOUSTIC EMISSION MONITORING OF FLEXURALLY LOADEDARAMID/EPOXY COMPOSITES BY EMBEDDED PVDF SENSORSCLAUDIO CANEVA, IGOR MARIA DE ROSA and FABRIZIO SARASINI

25-092

ACOUSTIC EMISSION SIGNALS GENERATED BY MONOPOLE (PENCILLEAD BREAK) VERSUS DIPOLE SOURCES: FINITE ELEMENTMODELING AND EXPERIMENTS
M. A. HAMSTAD

Karen Holford and Rhys Pullin
Topical Editors

I-12

STRUCTURAL INTEGRITY EVALUATION USING
ACOUSTIC EMISSION
KANJI ONO
Department of Materials Science and Engineering
University of California, Los Angeles, California 90095-1595
Abstract
This paper reviews various approaches used in acoustic emission (AE) testing of structures
so that further improvements can be realized in this important application of AE technology.
Since the late 1950s, many successful AE tests of structures have been completed. Yet, thespread of AE technology is underwhelming. To achieve a wider use of AE, we attempt to organize and reexamine the AE methodology applied to structural integrity monitoring in four steps.
1. AE Sources: Primary sources of brittle fracture, micro- or macro-cracks in contrast to secondary sources of friction or fretting, rust, etc. 2. Evaluation Tools: Kaiser effects, arising from theirreversibility of AE, allow the detection of prior loading level and of damage states. Related indices are also useful. 3. Source Location: This approach identifies the area of integrity loss. 4.
Source Characterization: Combined AE parameters give good insight to the flaw types, but manyother methods, including signal amplitude, signal frequency, waveform and wave propagation
analysis and moment tensor analysis, can be useful. Avenues for better AE technology utilization
are suggested. These include: to accumulate basic data on structures with standardized procedures, to devise combinatorial approach between localized damage evaluation and long-range
detection and to develop regional or global database under international cooperation. The final
goal of determining the structural integrity is attainable only with systematic approach with damage quantification via NDT and with the use of fracture mechanics.
Keywords: Structural Integrity; AE Methodology, AE Sources, Source Characterization
1. Introduction
Acoustic emission (AE) testing of structures began in California in the midst of the spacerace [1]. From the early years, R&D activity was high; 400+ papers on this topic were compiledfor 1970-72 period [2]. By now the methodology has well-established commercial basis in mostindustrialized countries. Yet, we are far from the goal of providing quantitative answers regarding the integrity and remaining lifetime of structures. Current status of AE testing of structures isreviewed here to advance this important application of AE technology.
The basic AE testing strategy is to detect AE signals globally from the entire structure under
test using above-normal loading (or pressure), to identify their locations, to group them (clustering) and to rank the groups comparing the collective AE characteristics with empirical database[3-5]. The key issue is the presence or absence of critical intense sources in the structure. During
the past 40+ years, many AE tests of structures have successfully utilized this strategy. Most often, AE has been used as an alternative to internal inspection of tanks and pressure vessels. However, details remained inaccessible in proprietary reports and meaningful assessment has been
unavailable. Some recent articles give valuable insight to industrial AE practices [6-8]. The
strategy of AE testing relies on the presumed principle that active defects produce AE in asymptotically increasing number with rising load. We also assume larger defects give stronger AE.
J. Acoustic Emission, 25 (2007)1© 2007 Acoustic Emission GroupBoth are probably true, although we lack the definitive evidence. Complications arise because
non-defects can also generate AE, often quite vigorously. These sometimes aid in finding crackpresence, but are impediment to identifying a critical source. AE’s main advantage is the global
inspection capability, while flaw sizing is beyond its scope, requiring complementary nondestructive evaluation methods.
Here, we examine the AE methodology in four steps. 1. AE Sources: Primary sources of brittle fracture, micro- or macro-cracks vs. secondary sources of friction or fretting, rust, etc. 2.
Evaluation Tools: Kaiser effect, arising from the irreversibility of AE, allows the detection ofprior loading level and of damage states. 3. Source Location: Using Zone, 2D- and 3D-source
location methods or via embedded waveguides, this approach identifies the area of integrity loss.
4. Source Characterization: Many methods are practiced here, including combined AE parameters, attenuation-corrected signal amplitude, signal frequency, waveform analysis and moment
tensor analysis. Finally, possible avenues of improvement are discussed.
2. AE Sources
Primary AE
Primary AE sources of interest in structural testing originate from brittle fracture and otherfast cracking fracture [9]. Brittle fracture of structural steels generates AE with low b-values andhigh amplitude. This type also comes from low-energy tear, intergranular and inclusion fracture,various embrittlement modes (hydrogen, stress corrosion), weld flaws and heat-affected zone
failure. Generally, plastic deformation plays a minor role as its continuous-type AE is usually
weak and is hard to separate from noise. Ductile (dimple) fracture of typical structural alloys isalso weak emitters. Some AE from ductile cracking is often related to nonmetallic inclusions [9].
Concrete is another material class producing brittle cracking [10]. In the strong AE emitting
materials, the sign of imminent structural failure comes from rapidly increasing numbers of AE
signals from a few concentrated sources. The approach we use in finding if the failure of a structure is about to happen from a rapid increase in AE is embodied in Fig. 1. This is the AE generating behavior of a sound concrete, moisture-cured for 28 days, re-plotted in log-log scale from thedata of Suzuki et al. [11]. Stress was normalized to the fracture strength, while AE hits werenormalized at 80% stress. This kind of power-law behavior between AE events and stress orstress-intensity factor [N ∝ σn or N ∝ KIn] has been known for many years [12] and was first correlated experimentally to the stress dependence of microcrack formation in steels at low temperatures [13].
In microseismic testing, seismic magnitude has been correlated to crack sizes [14]. Distinction of AE from micro-cracks and macro-cracks is shown as changing b-values and amplitude.
For concrete, see Fig. 2 [15]. In this graph, improved b-values (or Ib-values) are used, which areevaluated over the amplitude range of the mean amplitude ± standard deviation [15]. This eliminates the uncertainty from arbitrarily selecting a linear region in the amplitude distribution curve.
Unfortunately, they omitted a factor of 20 used in the definition of decibels, so Ib-values needthis factor to make them comparable to traditional b-values. It is hard to identify convincing evidence of cracking processes on large-scale structures from AE alone, but a recent comprehensive
study of concrete fracture [16] using computed tomography imaging with AE traced crack development and associated AE behavior; initially, cracks are tensile, changing to shear-type toward final fracture. In composite structures like tanks, vessels and piping, matrix cracks, interlaminar failure and fiber failures are of main interest [17, 18].
2

The power-law AE behavior was recently given a stochastic basis [19]. Re-plotting the final
parts of composite tank AE data, the data was found to obey critical phenomena (CP)-based
power laws. Another formulation refers to the fracture (critical) time τ’ and cumulative AE “energy” E ∝ [(τ’ – t)/τ’]–γ , with time t and an universal critical exhibitor without dimension, γ [20].
Figure 3 includes such CP curves with γ–values of 0.025 to 1. A theoretical value is 0.27 [20],but obviously the curves fit a power law only locally. Thus, the current CP approach needs refinements in analyzing fracture. Practical benefit from the CP approach is also unclear, but better
insight may be expected eventually from CP physics.

Fig. 1 A log-log plot of AE hits (ref. at 80%
stress) vs. stress normalized to the fracture
strength. A sound concrete, moisture-cured
for 28 days. From the data of Suzuki et al.
[11].

Fig. 2 Loading curves of concrete L-shape withcorresponding improved-b (I-b) values. Stage VI
nears yielding with an increase in I-b, stages VII
and VIII show I-b below 0.05 indicating macrodamages beyond yielding. Shiotani et al. [15].

Secondary AE
Secondary AE sources arising in structural tests can be useful in locating harmful discontinuities, but others interfere with proper AE testing. Structural noise, such as that originated frombearings, can be loud and annoying and its elimination required AE analysis [21]. Structural
joints also emit many AE. Friction and fretting of crack faces are widely used to locate otherwise quiet cracks. In concrete structures, the presence of crack networks is common and crackface friction is a major source of AE. Such AE has different characteristics that can be used to
identify them. In Fig, 2, for example, friction-AE during unload at 1750 to 1900 s has high I-b
values [15]. Rust in steel cracks acts in similar manner. Rust, oxidation and corrosion products
can be active emitters, as demonstrated in [22]. Finding gas and fluid leaks is by itself an impor3

tant goal of AE testing [6, 23]. Weather-induced noise can be a serious obstacle in field tests. A
recent study is a good reference on this topic [24].

Fig. 3 The power-law AE correlation of N ∝ KIn for the case of n = 4 and the CP formulation of
AE “energy” E ∝ [(τ’ – t)/τ’]–γ , with the fracture (critical) time τ’, time t and an universal criticalexhibitor without dimension, γ (1, 0.25, 0.1 and 0.025 shown).
Materials Effects
Structural materials affect the attenuation of AE waves and determine the effective range of
detection frequency and sensor spacing. In typical metallic alloys, the attenuation is low and primary AE signals at 100 kHz-2 MHz can be routinely utilized. Buttle and Scruby showed thedistinction among fatigue cracks, fretting and noise requires MHz-signals, using their special
conical sensors extending to 7 MHz [25]. It is often found that secondary AE signals have theirmain contents below 100 kHz, allowing frequency-based discrimination [26]. In large metallic
tanks, pressure vessels and pipes filled with liquids, the usable range is reduced to 30-100 kHz.
This comes from the loss of wave energy dissipated into liquid via dispersion and absorption.
Heterogeneous concrete, bricks and rocks have high attenuation and the detection frequency is at
10-60 kHz [27]. Even at this range, Shiotani et al. [28] showed the frequency-based discrimination is possible between cracking and fretting signals.
Geological materials have high attenuation and their typically large sizes further limit the usable frequency to sub-kHz range. In the low-frequency range, however, 3-axis sensing becomespractical and this allows detailed evaluation of sources [29]. In geological cases, the large size of
propagation media allows the use of body waves. This is in contrast to industrial structures,
where guided waves are common and boundaries wield strong effects on the signals reaching
sensors. Until the work of Gorman and Prosser [30], the guided waves were long ignored in AE
studies, especially in field AE testing, in part due to the difficulties of analyzing them. By now,
Lamb (plate) waves have proven the utility in field inspection as well and even cylindrical waves
are beginning to be used [31]. Expanded uses of guided-wave analysis should be seriously considered in AE testing.
Wide-frequency signals in low-attenuation materials can provide more detailed waveform information, allowing better source characterization [25]. Reverberation presents a challenge in4

sorting out the original signals, especially in smaller shapes. By combining with source location,
exact waveform analysis is possible and source parameters can be determined (Suzuki et al.
[32]). Such laboratory-scale scheme is difficult to scale up to the industrial level, but increased
capability of Lamb-wave simulation should enable this to happen. In structures with highattenuation materials, limited sound transmission forces the use of lower detection frequency.
Low frequency implies wavelength larger than the scale of inhomogeneity, reducing attenuation.
While longer transmission becomes possible, low-frequency waveforms carry less information
regarding the sources and limit the capability of source characterization. In principle, this can becompensated by increasing the number of sensors, as demonstrated in laboratory studies [14, 16].

Fig. 4 Two classes of AE signal observed during fatigue of riveted coupon. High frequency AE(attributed to crack advance) (a) AE waveform; (b) Fourier transform. Fretting-AE signal observed periodically; (c) AE waveform; (d) Fourier transform. Buttle and Scruby [25].
3. Evaluation Tools
Kaiser effect enables the detection of the prior loading level and in assessing the state ofstructural damages. AE emitted below the prior load (Felicity effect) implies the presence ofdamages, although other measures are needed to verify the damages. In metallic structures, aclear re-start of AE is usually indicated in the absence of flaws. Crack-face cohesion and fretting
provide under-preload AE indication of the crack presence, especially under cyclic loading.
5

Figure 4 shows the comparison of crack advance signal during fatigue cracking of Al sheet
and that due to fretting [25]. High-frequency components above 500 kHz signify the cracking
signals that generally have low amplitude. The fretting signals are much stronger and stronger
low-frequency components. Oxidation and corrosion products amplify this effect.
Successful use of Kaiser effect by Kanagawa and Nakasa in determining the pre-existing
stresses in under-sea rocks 30 years ago pioneered this AE application [33]. Large background
AE is always present and careful test procedures are required in correct stress determination [34].
See an example of background subtraction in Fig. 5. Refinement of this method continues to present [35].

Fig. 5 Rock stress estimation. AE count rates vs. applied stress of first (filled circle) and second(open) reloading. Arrow at 27 MPa indicates the prior load level. Yoshikawa and Mogi [34].

Fig. 6 The structural integrity index from continuous AE monitoring of a defective area during
plant operation (Wood et al. [36]).
6

Wood [36] in Australia has been using Kaiser effect (by defining “structural integrity index”
or SII as percent of the prior load where AE starts) in the evaluation of various structures. He
reported successful predictions of failure based on expected time to reach the critical value of thestructural integrity index (80 for metals, 90 for FRP and geologic/concrete materials and 100 being the sound condition). Figure 6 shows an example, where this area is predicted to fail at 250
days upon reaching SII of 90. Global definition of such an index must take proper account ofcritical components, and its validity is difficult to confirm. However, this approach is worthy offurther consideration in integrity assessment.

Fig. 7(a) Zone intensity plot of a thermal loading (left) and (b) intensity chart of the first pressurization test (right) of a hydrocracker reactor. Zone 20 and 22 are active. Miller et al. [17].
In AE tests of composites, Felicity effect has played the central role in identifying the damaged states [17, 18].
Felicity ratio is defined as the ratio of the load at AE start in reloading to the prior load.
This parameter was important along with two well-known indices;
Severity Index: a measure of the magnitude of strong emissions or average signal intensityof 50 strongest AE events;
Historic Index: rates of change in AE intensity or characterizing signal intensity of recent(20%) AE events over the average.
The cross plot of Severity Index and Historic Index is called Zone Intensity plot and an example is given in Fig. 7a [17]. This is used to designate from level A (minor emissions) to level
E (major defects present). In conjunction with zone location scheme, the test evaluation procedure known as MONPAC® identifies the zone intensity of each zone from A to E. When higher
level of zone intensity is found in a tested tank, immediate further testing via non-AE methods isrecommended. Zone intensity level is characterized for each monitoring zone and is shown on aschematic chart, as in Fig. 7(b). Results shown here are from tests on a hydrocracker reactor andZones 20 and 22 contain significant damages (levels D and C) [17].
7In concrete testing, other parameters have been defined for damage evaluation [11]. For repeating loading tests of concrete, “Load ratio” was defined (as in the case of Felicity ratio):
Load ratio = load at the onset of AE activity under the repeated loading / previous load.
Another term, “Calm ratio” was defined as
Calm ratio = the number of cumulative AE activity during unloading / total AE activity at
the previous maximum loading cycle.
In concrete, crack face rubbing generates unload emissions, which are indicators of the presenceof cracks in a structure. By cross-plotting these two ratios, three levels of concrete damage(Heavy, Intermediate and Minor) can be identified [11]. This procedure was incorporated in a
Japanese industrial standard, NDIS2421. This standard also defined another index, RA ratio, as
RA ratio = rise time of an AE signal / amplitude in V.
This parameter is essentially the inverse of rise-time slope, except the latter typically uses dBscale so these two are not directly invertible. Ohtsu and coworkers [37] originally developed RA
ratio and utilized it in conjunction with average frequency to correlate the tensile vs. shear natureof AE sources. Recently, Takuma et al. [38] used RA ratio for the evaluation of tool wear successfully, so this parameter should be considered in many other applications.
Mizutani and coworkers [39] recently introduced another parameter for evaluating AE observations, i.e., the inverse of Maharanobis distance correlating AE hit rates and b-values. It wasapplied to detect the onset of plastic deformation of bolts, but may well be applicable to various
signal types. Here, Maharanobis distance of AE signals detected is defined as follows [40]:((x " x ) ! ) # R # ((x " x ) ! )
T

D=

"1

p

where x = [x rate , x Ib ] is a two-dimensional vector consisting of the AE hit rate and the b value of thecomparison data and x = [x rate , x Ib ] is a two-dimensional vector, consisting of the averages of thereference AE parameters.
In the analysis of individual AE waveforms, fast Fourier transform has played a major roleover the years. In the past decade, wavelet transform (WT) also contributed to our understandingof AE signals [41-43]. WT allows the visualization of frequency contents as a function of time,
especially with the use of AGU-Vallen freeware [43]. An example of WT result is shown in Fig.
8 (right), where a simulated fiber-splitting in a CFRP was generated by a line-focused laser anddetected by a Pico sensor [44]. First-arriving So-mode Lamb waves at >300 kHz are followed byslower Ao-mode waves below 250 kHz.

Fig. 8 Laser simulation of matrix cracking on the bottom surface using focused laser beam parallel to the 0º fibers. Mizutani et al. [44].
8One needs to appreciate the difficulty of obtaining wideband waveforms of AE signals ingeneral structural testing, where guided-wave propagation is the norm and one may have to deploy waveguides. Still, we should strive for getting more than basic AE parameters. In typical
AE sensor selection, high sensitivity has taken precedence over wideband detection capability.
Thus, while wave propagation modes have dominant effects on the waveforms received, any information on a source or the propagation path is lost when a narrow-band sensor is used. In orderto improve the source characterization capability, we need a comprehensive approach. One
should note that some commonly used resonant AE sensors respond at multiple frequency bands.
Newer wideband sensors also have increased sensitivity. These can work with higher functionality of modern AE processing equipment and provide us with a better data set for new AE parameters. For example, it is routine now to obtain processed data from multiple frequency bands.
Recognizing different modes of guided waves in thin-wall structures is another avenue for vast
improvement in interpreting observed AE data. This may lead to the nature of AE sources evenunder limited frequency band detection.

Fig. 9 Total AE event locations seen in the fatigue test of a 9-m-long wind-turbine blade. Photograph shows damages on the blade surface and nearby PAC R6I sensors. Beattie [45].
94. Source Location
Source location identifies the area of integrity loss. At present, most AE equipment incorporates the software for source location and clustering scheme and displays results in various userinterfaces. High AE activity at a given area of a structure, or a cluster, implies the possible presence of flaw. The criteria of “high” activity always need careful attention; AE hit counts andrates, amplitude, and energy have initially formed the basis for the grading of clusters. An example of 2D-source location on a cyclically loaded wind-turbine blade is shown in Fig. 9, alongwith the photograph of damage location with sensors (Beattie [45]). Each AE event source was
located in different triangles formed by the three nearest sensors. Damage development is clearly
displayed on the source-location map of the fatigue-tested blade after 3.7+ million cycles. An
excellent correspondence is displayed between observed damage and source locations.
Source location in AE evolved from the procedures used in seismology. The original electromechanical system at Aerojet [1] developed into current PC-based systems. Recently, Ge [46]reviewed available source location algorithms thoroughly with theoretical background. These
are applicable to both 2D-source location and 3D-source location. A simpler Zone-location
scheme has been used in structures with high attenuation materials. This method works when theattenuation of AE signals is high and triangulation techniques require too many sensors (and attendant electronics). AE activities at the first-arriving sensor are associated with this sensor location and the “zone” surrounding it. While not as accurate, it identifies the regions of high AE activity. Figure 7b shown earlier is an example of zone location.

Fig. 10 The AE locations of a 2-m thick depth interval between the floor of the intermediate level
and the roof of the lower level (data of one year, 72,902 events). Spies and Eisenblätter [48].
10In geotechnical applications, the prediction of rock burst, and coal and gas outburst was historically important from the safety consideration [29, 47]. Increasing AE rates and spatial focusing were carefully evaluated to anticipate impending bursts. The stability of highway and railway
slopes has also been much worked AE applications. The stability of excavation of undergroundcavities and openings is more recently examined, especially in connection with nuclear waste
disposal. Microcracking changes the microstructure of the rock so that permeability might increase. Macrofracturing can lead to stability problems. Detected locations of microcracks in aradioactive waste disposal facility are shown in a plan view in Fig. 10 [48]. Geothermal energy
extraction is another area AE is actively used for studying and monitoring the distribution of reservoirs, hydraulic fracturing, reservoir volume and hydro-circulation. Similar methods are now
being applied to gas and oil production.
In examining the evolution and distribution of AE sources in a cluster, fractal analysis has
been used. A fractal dimension of three represents random events occurring in a volume and afractal dimension of two signifies that the events have localized on a failure plane [49, 50]. An
example of decreasing fractal dimension with the progress of fracturing in a rock sample is given
in Fig. 11 [50]. Introducing fractal modeling processes, crack distribution is constructed for areal coalmine starting from AE data set and laboratory tests [51].
400

3.5

350

3

300

Load (kN)

250
2
200
1.5
150

Fractal Dimension

2.5

1

100

0.5

50

Load
Fractal Dimension

0
0.00

0.02

0.04

0.06

0.08

0.10

0
0.12

Displacement (mm)Fig. 11 Load history and fractal dimension for the biaxial test of a rock. Iverson et al. [50].
Source location in an anisotropic or a dispersive propagation medium requires special consideration. Applications of wavelet transform and affine transformation are examples of such effort [52, 53]. The use of wavelet transform defines the arrival times at a given frequency with
precision. See Fig. 12 for arrival time determination of L-mode and F-mode cylindrical waves at
190 kHz. The propagation of waves in a large pipeline has been of practical importance for many
years, but it should be noted that different wave modes have different attenuation characteristics,
as shown in Fig. 13 [54]. Note that fastest moving wave (presumably L(0,1)-mode cylindricalwaves) and the second fastest attenuated more than slower moving waves of likely flexuralmodes. The affine transformation speeds up the source location algorithm in orthotropic plates
11

by three orders of magnitude [53]. Hamstad and co-workers have conducted extensive finiteelement modeling analysis of Lamb wave propagation (see [55] and references included). His
most recent study shows among others the value of using pencil-lead break (PLB) sources onedge to represent buried dipole sources and the danger of surface, out-of-plane PLB to assess realistic wave modes.
For very high attenuation media, embedded waveguides provide an effective means of narrowing the zone of AE activity. Waveguides are also used in tests under extreme environment,but often cause severe waveform distortion [56].

Fig. 12 An AE signal in a brass tube detected at 20 cm from the source (left). Its wavelet coefficient at 190 kHz showing two cylindrical wave modes. Yamada et al. [52].

Fig. 13 Wave attenuation in a water-filled buried pipeline. Vahaviolos et al. [54].
5. Source Characterization
Many methods of source characterization are used. The most rigorous one relies on signals
from several sensors on a small sample and conducts deconvolution or simulation analysis to obtain the source rise time and magnitude for Mode-I cracks [9]. This applies to idealized cases
only and is of little use for large structures. More practical and appropriate approach must be selected. This also means that identification of AE sources relies on complementary knowledgebase on possible failure mechanisms from fracture mechanics and from experience. Typically,materials study accompanies AE examination and empirical deduction leads to AE characterization (although some people are prone to proclaim mechanisms by intuition). The AE analysis
12methods include the use of combined AE parameters, signal amplitude and distribution, signal
frequency, waveform analysis and moment tensor analysis. However, selected AE parameters(hit rates, amplitude, and their time history, being the typical) are the most common approach
used in combination with a source location scheme [3-8]. Various evaluation tools discussed in
Section 3 are employed in combination. These have been combined with empirical database of
post-test inspection and used in grading tested structures. The highly successful CARP procedures, developed initially for composite vessels, combined AE cluster grading with zone location[5, 17]; cf. Fig. 7. Here, grading utilized severity and historic indices representing cumulative
intensity and sudden AE activity jump. A similar (but almost unknown outside Japan) system
was actually operational in 1978 [4]. Some have since been packaged into commercial inspection
technology products, e.g., MONPAC® and TANKPAC®.
Signal frequency remains controversial as to its effectiveness in source characterization, but
it has been used with much success in (high-pass) filtering out background/frictional noise fromhigher frequency cracking AE signals [26]. This has a firm support from a rigorous experiment(see Fig. 4 and [25]). It is tempting to assign an AE mechanism to its characteristic frequency. Itshould be pointed out that the direct signal characterization methods have clearly shown thatsource rise times of a given type of cracking vary by a factor of ten or more so that a single“characteristic frequency” of cracking does not exist [9].
Moment tensor analysis (MTA) is useful in characterizing the nature of AE sources. This
identifies the magnitude and orientation of displacement vector of an AE source. From its geological origin, Ohtsu and Ono [57] set forth this method in AE context initially using theoreticalsimulation. They set a framework for deducing the crack characteristics using only surface AE
observations. Ohtsu [58] developed the MTA further, applying to real AE observations in

Fig. 14 Loading curve of a center-notched concrete beam, and results of MT analysis within yellow shading. Double arrows are tensile crack and X are shear cracks. Shiotani et al. [59].
13concrete structures with great success. MTA software has been available with a major AE system. The MTA provides the classification of AE signals due to tension (crack-opening) and shear
modes of fracture. However, this analysis requires the detection of weak, initial P-wave arrivals
and sensor spacing cannot be large. It is expected that the MTA of localized sources is the firststep of integrating this valuable technique into large-scale structural AE tests.
Practical MT analysis relies on the first arrival P-wave motions with calculated Green’s function, resulting in crack types: tensile, shear and mixed. For the case of a center-notched concretebeam, left insert in the loading curve of Fig. 14 shows observed crack types of AE sources
around the crack starting from the notch root [58]. This shows crack type is mostly tensile before
reaching the maximum load (Stage 1). Beyond the peak in Stage 2, more shear cracks are distributed around the crack tip. The MT analysis method requires 6 sensors receiving clear signals
for crack type determination, limiting the sample size and applicable geometry. However, it provides valuable insight to failure processes.
The next stage in AE testing of structures is wider use of pattern recognition analysis (PRA).
PRA has long been studied in evaluating AE observations [60], but recent improvements in dataprocessing speed allow this to be integrated with multi-channel location software [61]. Godinez
et al. [62] used a supervised PRA scheme to detect flaw in helicopter tail gearbox in real time.
The energy content of different frequency bands was used as the basis for classification features.
K-nearest-neighbor and neural-network classifiers are often used in PRA to cluster signals into
separate groups based on combinations of “features”. Since various schemes of artificial intelligence compare and deduce matching patterns, independent validation is needed to provide appropriate AE context to PRA results. This is especially keen for neural-network results. These
are obtained from a “black-box”, which was trained by feeding inputs and outputs.
6. Structural Integrity Assessment
Acoustic emission has been used widely in the US since the 1970s as a means of monitoring
the structural integrity of large pressurized vessels and structures. This statement describes eventhe current state reasonably well in that AE has successfully evaluated thousands of structures
and alerted conditions of potential failure. Further refining this approach is prudent for AE technology in the near future. Yet, AE is not seriously contributing to “structural integrity assessment” of critical structures. Here, the “assessment” implies far more ambitious goals of predicting remaining lifetime and calculating failure probability. For such goals, the size of flaws has tobe estimated, but AE is not a flaw-sizing method.
The procedures for structural integrity assessment require three main groups of input data: 1.
material: constitutive (stress-strain) behavior with time and temperature dependence, fracture
toughness and fatigue parameters; 2. load: loading regime of primary stresses, residual stresses;
3. geometry: geometry of structure, geometry of flaw. The last item is the most difficult to obtain unless its size is large enough for ultrasonic or radiographic testing or surface-breaking andaccessible. Improvement of NDE technology is most urgent in structural integrity assessment.
Here, AE can contribute in identifying the positions of active flaws, but flaw (or crack) sizing isprobably beyond AE techniques. Digesting the input with various rounds of analysis via finiteelement codes, fracture mechanics, material damage and loading schemes, probabilistic lifetime
prediction emerges. Thus, unless AE can address AE source characterization issue in structural
testing, AE cannot hope for assuming a key role in structural integrity assessment. As suggested
earlier, combining global flaw evaluation with localized AE source study is the first step we
14

should take. Past attempts for structural integrity assessment by AE have been reported, e.g., [63,64]. However, meaningful progress has been difficult.
Another front is standardization, where many “international” standards appeared instead of
consolidation of existing documents. Brunner and Bohse [65] assessed them and concluded thatirrespective of the area of AE test procedures, well-documented evaluation criteria must be
available for the success of the procedure. From the US-side, ISO seems to have become an
irrelevant regional organization. On the other side, ASTM tries to prevail over the whole world.
The competitive atmosphere certainly is less than ideal.
In order to fulfill the promises of AE technology, we must continue to accumulate basic dataon structures with standardized procedures, to devise combinatorial approach between localized
damage evaluation and long-range detection and to develop regional or global database underinternational cooperation. The needs for more basic data on structures are obvious, but funding
for such activity has been low. Mutually beneficial cooperation of laboratory researchers andfield practitioners may bring forth a break-through. For example, waveform data may be taken
during routine in-service tests and provided for post-analysis by graduate students working foradvanced degrees. Seeking the standardization of sensors, procedures, and analysis methods, etc.
is likely to stimulate discussion of best possible approach for various test objectives.
Another goal worth pursuing is to devise combinatorial approach between detailed localized
evaluation and long-range detection for global flaw location. Waveform data after extended
propagation is of lesser value than those collected close to sources. Wavelet and moment tensor
analysis methods are best applied near the sources, while CARP procedures and Felicity/Calmratios can be evaluated on a large scale. It is of critical importance that guided-wave propagation
modes are incorporated into any AE analysis of thin-walled structures. A recent work by
Takemoto et al. [66] shows the benefit of Lamb-wave analysis in the corrosion detection of large
oil tanks. Various simulation and modeling techniques are also beneficial.
Structural tests are expensive and we need wide international cooperation to establish workable and effective standards of AE testing. We should start thinking about developing regional
databases for this endeavor that can be expanded to global scale.
7. Concluding Remarks
To raise AE technology into the next level, we must improve it from several fronts. Only
then AE can become a serious player in structural integrity assessment. We need to better characterize AE sources; to extract more from AE signals reaching sensors; to devise effective AE parameters; to integrate new analysis methods, such as WT, MTA, and PRA; to include simulation
tools in analyses; to accumulate basic data on structures with standardized procedures; to devise
combinatorial approach between localized damage evaluation and long-range detection and todevelop regional or global database under international cooperation. Using systematic approach
along with NDT, fracture mechanics, etc., improved AE can offer more value to industrial users
and to contribute substantially to structural integrity assessment.
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15

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References
1. Green, A.T., Advanced Materials Research, 13-14, 2006, 3-14. See also Green, A.T., Lockman, C.S., and Steele, R.K., Modern Plastics, 41(11), 1964, 137-139, 178, 180.
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16

11. Suzuki, T., Ohtsu, M., Aoki, M., and Nakamura R., Damage Identification of a Concrete Water-Channel in Service by Acoustic Emission, Proc. ICAE-6, Lake Tahoe, 2007, pp. 46-51.
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17

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39. Mizutani, Y., Onishi, T. and Mayuzumi, M., Plastic-region tightening of bolts controlled byacoustic emission method, Proc. ICAE-6, Lake Tahoe, 2007, pp. 120-125. Also see: J. of Acoustic Emission, 25, 2007, 239-246.
18

40. http://en.wikipedia.org/wiki/Mahalanobis_distance.
41. Suzuki, H., Kinjo, T., Hayashi, Y., and Ono, K., with Appendix by Hayashi Y., Wavelet
transform of acoustic emission signals, J. of Acoustic Emission, 14, 1996, 69-84.
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analysis, Acoustic Emission – Beyond the Millennium, Elsevier, 2000, pp. 35-56.
43. AGU-Vallen Wavelet transform software, version R2007.0309, Vallen-Systeme GmbH, Icking, Germany (2007). Available at http://www.vallen.de/wavelet/index.html
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45. Beattie, A.G., Acoustic emission monitoring of a fatigue test of a TX wind turbine blade. (tobe published). See also Rumsey M.A., Paquette J., White J., Werlink, R.J., Beattie A.G., Pitchford C.W. and van Dam, J., Experimental results of structural health monitoring of wind turbine
blades, Proc. 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Jan 7-10, 2008. AIAA2008-1348, Rumsey.
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49. Hirata, T., Satoh, T., and Ito, K., Geophysical Journal International, 90, 1987, 369-374.
50. Iverson, N., Kao, C.S. and Labuz, J.L., Clustering analysis of AE in rock, Proc. ICAE-6, Lake
Tahoe, 2007, pp. 294-299. Also see: J. of Acoustic Emission, 25, 2007, 364-372.
51. Watanabe, Y., Itakura, K.I., Sato, K., Fujii, Y., Balusu, R., Guo, H. and Luo, X., A modelingmethod on fractal distribution of cracks in rocks using AE monitoring, J. of Acoustic Emission,
23, 2005, 119.
52. Yamada, H., Mizutani, Y., Nishino, H., Takemoto, M. and Ono, K. Lamb-wave source location of impact on anisotropic plates, J. of Acoustic Emission, 18, 2000, 51.
53. Kurokawa, Y., Mizutani, Y. and Mayuzumi, M., Real-time executing source location systemapplicable to anisotropic thin structures, J. of Acoustic Emission, 23, 2005, 224.

19

54. Vahaviolos, S.J., Miller, R.K., Watts, D.J., Shemyakin, V.V., and Strizkov, S.A., detection andlocation of cracks and leaks in buried pipelines using acoustic emission, J. of Acoustic Emission,
19, 2001, 172-183.
55. Hamstad, M.A., Acoustic emission signals generated by monopole (pencil-lead break) versus
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56. Ono, K. and Cho, H., Rods and tubes as AE waveguides, J. of Acoustic Emission, 22, 2004,
243-253.
57. Ohtsu M. and Ono K., Crack orientations and moment tensor solutions in acoustic emission,
Progress in Acoustic Emission III, pp. 771-778, JSNDI, 1986. See also Ohtsu, M. and Ono, K.,
AE source location and orientation determination of tensile cracks from surface observation,
NDT International, 21, 1988, 143-152.
58. Ohtsu, M., Determination of crack orientation by acoustic emission, Materials Evaluation,
45, 1987 1070-1075. See also Ohtsu, M., Moment tensor analysis, Acoustic Emission – Beyond
the Millenium, Elsevier, 2000, pp. 19-34 and Ohtsu, M., Isoda, T. and Tomoda, Y., Acoustic
emission techniques standardized for concrete structures J. of Acoustic Emission, 25, 2007, 2132.
59. Shiotani, T., Yuyama, S., Li, Z.W. and Ohtsu, M., Application of AE improved b-value to quantitative evaluation of fracture process in concrete materials ”, J. of Acoustic Emission, 19, 2001, 118133.
60. Ohtsu, M. and Ono, K., Pattern recognition analysis of magneto-mechanical acoustic emission signals, J. of Acoustic Emission, 3, 1984, 69.
61. Anastasopoulos, A., Pattern recognition techniques for acoustic emission based conditionassessment of unfired pressure vessels, J. of Acoustic Emission, 23, 2005, 318.
62. Godinez, V., Shu, F., Anastasopoulos, A., Finlayson, R., and O’Donnell, B., Real time classification of acoustic emission signals for drive system coupling crack detection, Progress in
Acoustic Emission XII, JSNDI, 2004, pp. 7-14.
63. Runow, P., The use of acoustic emission methods as aids to the structural integrity assessment of nuclear power plants, Defense Technical Information Center, AD D320141, 51 p., 1985.
64. Jeong H.D., Takahashi H., Murakami Y., On-line evaluation procedure for structural integrity by acoustic emission frequency analysis, Progress in Acoustic Emission IV, JSDNI, 1986,
pp. 428-438.
65. Brunner, A.J. and Bohse, J., Acoustic emission standards and guidelines 2002: a comparative
assessment and perspectives, NDT.net - 7 (09), September 2002;
http://www.ndt.net/article/v07n09/21/21.htm
66. Takemoto, M., Cho, H. and Suzuki, H., Lamb-wave acoustic emission for condition monitoring of tank bottom plates, J. of Acoustic Emission, 24, 2006, 12-21.
20

ACOUSTIC EMISSION TECHNIQUES STANDARDIZED FOR
CONCRETE STRUCTURES
MASAYASU OHTSU, TOSHIRO ISODA and YUICHI TOMODA*
Graduate School of Science & Technology and *Faculty of Engineering, Kumamoto
University, Kumamoto, Japan
Abstract
Acoustic emission (AE) techniques have been extensively applied to concrete structures.
Results achieved are currently going to be standardized for the inspection and evaluation of infrastructures in service. Test results associated with these standardization activities for concrete
structures are discussed. In order to assess the damage levels of the structures, the Japanese Society for Non-Destructive Inspection has established the recommended practice (NDIS 2421).
Two AE indices of load ratio and calm ratio are defined for qualification of the damages. It isdemonstrated that damages qualified are in good agreement with actual damages of reinforced
concrete members. AE behavior of concrete under compression could be analyzed, applying therate process analysis. By evaluating intact moduli of elasticity from AE database, relative damages of concrete cores taken from a road bridge are quantitatively estimated.
The Federation of Construction Materials Industries, Japan, has recently published new standards on estimation of concrete properties by the elastic-wave methods. The standards contain amonitoring method for active cracks in concrete by AE (JCMS-III B5706). The classification of
cracks is successfully applied to investigate the corrosion process of reinforcing steel-bars inconcrete. The moment tensor analysis of AE can identify cracking kinematics of location,
crack-type and crack orientation, which has been implemented as SiGMA procedure. Nucleationof micro-cracking due to the expansion of corrosion product is studied. It is found that themechanisms of corrosion cracking at the meso-scale are really made up of tensile, shear and mixed-mode cracks, while the surface crack and the diagonal cracks of mode I are observed at themacro-scale. These results will form the basis for proposing RILEM recommendations.
Keywords: Concrete, damage, corrosion, moment tensor analysis.
1. Introduction
Concrete structures in service deteriorate due to heavy traffic loads, fatigue, aging, chemical
reactions and natural disasters. It is now recognized in concrete engineering that concrete structures are no longer considered maintenance-free, and a number of the structures are going toreach their service-life limit. Accordingly, diagnostic inspections on the current state of deteriorated structures have been carried out all over the world. However, the assessment of damage or
structural integrity in existing structures is neither an easy task nor fully standardized yet. In thisregard, the recommended practice by acoustic emission (AE) is currently published (NDIS 2421,
2000), prescribing one criterion to assess the damage of reinforced concrete in service. Here,results applied to bending tests of reinforced concrete beams (Ohtsu et al., 2002) are discussed.
Damage evaluation in concrete by AE activity of a core sample in a compression test is underinvestigation (Ohtsu and Watanabe, 2001). AE activity is analyzed as the rate process, and thedamage parameter is evaluated by applying damage mechanics. Correlating AE rate with thedamage parameter, a database has been so successfully created that relative damages of concrete
J. Acoustic Emission, 25 (2007)21© 2007 Acoustic Emission Groupsamples are quantitatively estimated. The procedure is applied to concrete cores taken from aroad bridge.
The corrosion of reinforcing steel-bars in concrete can occur due to chloride attack. So far it
has been referred to as the most serious deterioration of reinforced concrete. An applicability of
AE technique to corrosion monitoring is discussed, applying the code by the Federation of Construction Materials Industries, Japan (JCMS-III B5706, 2003).
The moment tensor analysis of AE is available for identifying crack kinematics of location,
crack-type and crack orientation (Ohtsu, 2000). The analysis is implemented as SiGMA (Simplified Green’s functions for Moment tensnor Analysis) procedure. 3-D visualization has beendeveloped by using VRML (Virtual Reality Modeling Language) (Shigeishi and Ohtsu, 2003).
Mechanisms of corrosion cracking in concrete at the meso-scale identified by SiGMA are compared with those at the macro-scale.
Currently, another standardizing action is in progress. RILEM TC212-ACD (TechnicalCommittee on Acoustic emission and related NDE for Crack detection and Damage evaluation in
concrete) has been established for proposing RILEM recommendations. All results discussed areclosely associated with this committee activity.
2. AE Techniques under Standardized Action(1) Damage Qualification
The concrete structure is structurally stable with high redundancy, as AE activity is low in asound structure. Since the Kaiser effect is closely associated with structural stability, ratios toqualify the damage are defined in the recommended practice (NDIS 2421), as follow;(a) Ratio of load at the onset of AE activity to previous load:
Load ratio = load at the onset of AE activity in the subsequent loading / the previous load.
(b) Ratio of cumulative AE activity during the unloading to that of the maximum loading cycle:
Calm ratio = the number of cumulative AE activity during the unloading / total AE activity
during the whole cycle.
The load ratio can become larger than 1.0 in a sound structure. Due to damage accumulation,
the ratio decreases to below 1.0, generating AE counts at lower loading levels than before. AE
activity during unloading is another indication of structural instability. In the case of the soundstructure, AE activity is seldom observed and the calm ratio is small. In heavily damaged structures, load ratio is below 1.0 and calm ratio is large. In the recommendation, the damage is defined as minor, intermediate and heavy as shown in Fig. 1.
(2) AE Rate Process Analysis
AE behavior of a concrete sample under compression is closely associated with nucleation of
micro-cracks. These cracks are gradually accumulated until final fracture, as the number of AE
counts due to nucleation of these cracks increases. Since the process could be referred to as stochastic, the rate process theory has been introduced (Ohtsu and Watanabe, 2001). The following
equation of the rate process is derived to formulate the number of AE events, dN, due to the increment of stress from V to V + dV,
dN = f (V )dV ,(1)
N
where N is the total number of AE events and ƒ(V) is the probability function of AE activity atstress level V(%). Here, a hyperbolic function ƒ (V) is assumed,
22

Fig. 1 Damage qualification by the load ratio and calm ratio.

Fig. 2 Result of AE rate process analysis.

Fig. 3 Relation between rate ‘a’ and freeze-thaw
cycles.

f (V ) = a

(2)
+b,
V
where a and b are empirical constants. ‘a’ is here called the rate, since it reflects AE activity at agiven stress level. It is found that the rate could increase due to accumulation of the micro-cracks in concrete. Substituting Eq. 2 into Eq. 1, a relationship between total number of AE
events N and stress level V is obtained as,(3)
N = CV a exp(bV ) ,where C is the integration constant. We have applied Eq. 3 to AE generating behavior of a concrete sample in a compression test. Approximating the probability function by Eq. 2, AE generating behavior is modeled. A relation between total AE counts and compressive stresses in astandard sample (10-cm diameter and 20-cm height) is shown in Fig. 2. The sample was damaged with 50 cycles of freeze-thaw action. It is demonstrated that AE generating behavior observed in the test is in good agreement with the relation approximated by Eq. 3. We focused on
the rate ‘a’, because the value could reflect the damage degree. During the freezing and thawingof concrete samples, where temperature was varied from 5oC to -15oC for 3 hours, a relation between the rate “a” and the number of cycles was examined. Results are given in Fig. 3. It isclearly observed that the rate ‘a’ increases from a negative value to a positive value with the increase in freeze-thaw cycles. Thus, it is confirmed that the rate ‘a’ is a sensitive parameter to thedamage degree.
23

Fig. 4 (a) Stress-strain relation and (b) the corresponding damage-evolution process.

Fig. 5 Database on the damage evolution and the rate ‘a’.
A damage parameter  in continuum damage mechanics is defined as a relative ratio of themodulus of elasticity,(4) =1 EE*
where E is the modulus of elasticity of concrete and E* is the intact modulus of elasticity. In thecompression test of a concrete sample, a relation between stress and strain is obtained as shownin Fig. 4(a). The modulus varies from initial modulus E0 to final modulus Ec. The former is atangential modulus and the latter is a secant modulus. Then, the initial damage o is defined as,E.
(5)o = 1  0
E*Corresponding to the stress-strain relation, the damage parameter  increases from o to c as
shown in Fig. 4(b). In order to estimate the initial damage o in Eq. 5, it is essential to obtain
the intact modulus E*. Because the rate ‘a’ is sensitive to the damage degree, a database on acorrelation between the damage evolution during the compression test, ln(EoEc), and the rate‘a ‘ has been constructed as shown in Fig. 5. Most of the data are based on results of the freezing and thawing tests of concrete samples. Assuming a linear correlation, it is derived as,ln(Eo  Ec) = ln[E * (c  o)] = Da + Y .
24(6)

After adding the data of a tested sample to the database, a new linear correlation of Eq. 6 is determined. Then, it is assumed that E0 = E* when a = 0. This allows us to estimate the intact
modulus E* of the sample from,

E* = Ec + exp(Y ) .
Then a relative damage of the sample is estimated as Eo/E*.

(7)(3) AE Parameter Analysis
AE parameters obtained by a conventional system are AE count, AE hit, AE event, peak amplitude, AE energy, rise time, duration, arrival time differences in AE sensor array and so forth.
In order to classify active cracks, AE indices of RA value, and the averaged frequency Fa aredefined from parameters as (JCMS-III B5706),
RA = the rise time / the maximum amplitude.
(8)
Fa = AE ringdown-count / the duration time,(9)
By means of these two indices, cracks could be classified into tensile and shear cracks as referring to Fig. 6. Tests were conducted in tensile tests and direct-shear tests of concrete samples, byemploying three AE sensors of different resonances. It is observed that crack types are reasonably classified. Here, the classification shall be based on the moving average of more than 50
counts.

Fig. 6 Relationship between average frequency Fa and RA values for crack classification.
(4) SiGMA Analysis
In order to determine the moment tensor of an AE source, a simplified procedure has beendeveloped. This is suitable for a PC-based processor as it is robust in computation. The
procedure is implemented as SiGMA (Simplified Green's functions for Moment tensor Analysis)(Ohtsu, 2000). Displaying AE waveform on CRT screen, two parameters of the arrival time andthe amplitude of the first motion are determined. In the location procedure, source location isdetermined from the arrival time differences. Then, distance and its direction vector are determined. From the amplitudes of the first motions at more than 6 channels, the moment tensor isdetermined. Since the SiGMA code requires only relative values of the moment tensor components, the relative calibration of the sensors is adequate. Then, the classification of a crack is
performed by the eigenvalue analysis of the moment tensor. Setting the ratio of the maximum
shear contribution as X, three eigenvalues for the shear crack become X, 0, -X. Likewise, the25

ratio of the maximum deviatoric tensile component is set as Y and the isotropic tensile component as Z. Three eigenvalues are normalized and decomposed;
Normalization
1.0 = X + Y + Z,
the intermediate eigenvalue/the maximum eigenvalue= 0 - Y/2 + Z,
(10)
the minimum eigenvalue/the maximum eigenvalue= -X - Y/2 + Z,
where X, Y, and Z denote the shear ratio, the deviatoric tensile ratio, and the isotropic tensile
ratio, respectively. In the present SiGMA code, AE sources of which the shear ratios are lessthan 40% are classified into tensile cracks. The sources of X >60% are classified into shear
cracks. In between 40% and 60%, cracks are referred to as mixed mode. In the eigenvalue analysis, three eigenvectors e1, e2, and e3,
e1 = l + n
e2 = l x n(11)e3 = l – n,are also determined. Vectors l and n, which are interchangeable, are recovered. In order to visualize these kinematical information of AE source (crack), VRML is introduced (Shigeishi and
Ohtsu, 2003). Crack modes of tensile, shear and mixed-mode cracks are given in Fig. 7. Here,an arrow vector indicates a crack motion vector l, and a circular plate corresponds to a crack
surface, which is perpendicular to a crack normal vector n.

(a) tensile crack(b) shear crack(c) mixed-mode
Fig. 7 3-D display models for tensile, shear, and mixed-mode cracks.
3. Results and Discussion(1) Qualified Damage in Reinforced Concrete
For damage qualification, reinforced concrete beams of 3.2 m length were tested. Compressive strength of concrete after 28-day moisture curing was 31.1 MPa. These beams were madewithout lateral reinforcement. Two-point loading spans were varied as 0.65 m and 1 m with 2.84
supporting-span shown in Fig. 8. AE sensor of 150-kHz resonance frequency (PAC: R15) was
selected. Frequency range was set from 10 kHz to 1 MHz, and the total amplification was 80
dB. For event-counting, a threshold level was set to 50 dB. The measuring system was a MISTRAS-AE system (PAC). After cracks were nucleated, clip gauges were attached to the specimen and the crack-mouth opening displacements (CMOD) were recorded.

26

Fig. 8 Sketch of a reinforced concrete beam.

Fig. 9 Qualified damages by the load ratio and the calm ratio.
For each loading cycle, the load ratio and the calm ratio were determined. Results are shownin Fig. 9. Based on the maximum CMOD observed in the loaded beams, classification limits are
set as 0.9 for the load ratio and 0.05 for the calm ratio. This is because the serviceability limit of
the CMOD in the concrete structure is referred to as less than 0.1 mm in the standard specification and the Kaiser effect was not observed in the case of the CMOD over 0.1 – 0.2 mm (Ohtsu,
1995). Into three zones of the minor, intermediate, and heavy damage, results of the maximum
CMODs are classified reasonably well. It demonstrates that the damage levels of reinforced
concrete beams can be qualified from the values of the load ratio and the calm ratio by monitoring AE activity under cyclic loading or traffic loads.
(2) Relative Damage of Concrete in a Road Bridge
Cylindrical samples of 10 cm in diameter were core-drilled from a concrete block (3.0 m3.0
m0.68 m), which was taken from an arch fragment of a road bridge shown in Fig. 10(a). The
bridge had been in service for more than 50 years, and was recently replaced because of road
expansion. After core-drilling, 12 cylindrical samples of 20-cm height were made by sawing andend-polished.
AE measurement in a compression test was conducted as shown in Fig 10(b). Silicone
grease was pasted on the top and the bottom of the sample, and a Teflon sheet was inserted toreduce AE counts generated due to friction. MISTRAS-AE system (manufactured by PAC) was
employed to count AE hits. AE hits were detected by using an AE sensor (PAC UT-1000 resonance frequency: approx. 1 MHz). The frequency range was from 60 kHz to 1 MHz. For event
counting, the dead time was set to 2 ms. It should be noted that AE measurement was conducted27

at two channels as well as the measurement of axial strains. AE hits and strains of the twochannels were averaged as a function of stress level.
From AE generating behavior, the rate ‘a‘ was estimated as the probability function f(V) wasapproximated by Eq. 2, where the least-square method was applied. Then, relative damages
Eo/E* were estimated in all samples. Results are compared with the compressive strengths in
Fig. 11. It is observed that relative damages estimated show a similar trend to the compressive
strengths. The compressive strength specified is to be over 25 MPa. About a half of the cores
seem to satisfy the specification, but the relative damages are lower than 1.0 except core Nos. 4
and 6. This suggests that most concrete cores are slightly damaged as the relative damages are
obtained in the range from 0.9 to 0.7. It suggests that the concrete block might have withstoodtraffic loads as their strengths are over the designed value, but the deterioration has alreadystarted as low values of the relative damage. Thus, the damage degree of concrete cores arequantitatively estimated by AE rate process analysis.

Fig. 10 (a) Arch fragment of a road bridge and (b) AE measurement system in compression test.

Fig. 11 Relative damages and compressive strengths.
28

(3) Corrosion Process of Reinforced ConcreteContinuous monitoring of AE events was conducted in an accelerated corrosion test and in acyclic wet-dry test. A reinforced concrete slab of dimensions 10 cm x 25 cm x 40 cm was cast.
The compressive strength at 28 days after moisture-curing in the standard room was 34.6 MPa.
The slab specimen was soaked in a tank. A copper plate was placed at the bottom, and then 100
mA electric current was charged between rebars and the copper plate as shown in Fig. 12. To
keep electrical conductivity good, the tank was filled with 3% NaCl solution. In a cyclic wet-dry
test, the specimen was cyclically soaked into the tank in Fig. 12 without charge for a week, andsubsequently taken out of the tank to dry under ambient temperature for another week. In bothtests, when the measured potentials became lower than -350 mV (C.S.E.), which is prescribed by
ASTM C876 (1991), the tests were terminated. AE sensors of 50-kHz resonance (RA5) wereplaced on the top surface of the samples. Amplification was 40 dB in total and the frequency
range was set from 10 kHz to 1 MHz. After the tests, core samples were taken, sliced andcrashed. Then, chloride contents at depth were measured by the potentiometric titration.

Fig. 12 Set-up for accelerated corrosion test.

Fig. 13 AE activities and the half-cell potentials in the accelerated corrosion test.
29

A relation between AE activity and half-cell potentials in the accelerated corrosion test is
given in Fig. 13. Two periods of high AE activity (hits) are observed. At the first period, thehalf-cell potentials start to decrease, where an abrupt increase in AE hits is observed at aroundthree days. The potentials reach lower than –350 mV (C.S. E) after the second period. Becausesuch two periods as the onset of corrosion in reinforcement and the nucleation of cracking in
concrete due to the expansion of corrosion products are defined in the standards, the two highAE activities are quite suggestive in the deterioration process due to the corrosion of reinforced
concrete.
To classify AE sources (micro-cracks) in the corrosion process, AE parameter analysis was
performed. Variations of RA values and the average frequencies in the cyclic wet-dry test are
given in Fig. 14. Two stages are reasonably identified from the figure. During the first period ofaround 40 days elapsed, RA value is high and the average frequency is low. From Fig. 6, thissuggests that shear cracks are actively generated. It is considered that shear cracks occur in reinforcement due to the onset of corrosion or rust breakage. During the second period around 100
days elapsed, RA value in Fig. 14 is lower than the first period and the average frequency ishigh. This implies the occurrence of tensile cracks. The expansion due to corrosion products
should result in the nucleation of tensile cracks in concrete. Consequently, it is reasonable toconsider that the second high AE activity corresponds to the nucleation of concrete cracking.

Fig. 14 Variations of AE parameters.
Chloride concentrations at the cover-thickness were also investigated to compare with AE
results. It was found that chloride concentrations at reinforcement were higher than 0.3 kg/m3
after the first period and became higher than 1.2 kg/m3 after the second. These results are in good
agreement with the values prescribed as the lower bound and the upper bound for the corrosionin the specification.
(4) Mechanisms of Corrosion Cracking
A concrete specimen of dimensions 25 cm x 25 cm x 10 cm with a hole of 3-cm diameter
was tested. The hole corresponds to rebar location with 4-cm cover-thickness. The compressive
strength of concrete at 28-day standard curing was 37.9 MPa. The velocity of P wave was 4730
m/s and the modulus of elasticity was 29.7 GPa. P-wave velocity was applied to SiGMA
30

analysis. Corrosion cracking was simulated by casting an expansive agent (dolomite paste) into
the hole. After one day, three cracks were macroscopically observed as shown in Fig. 15. These
are surface crack and two diagonal cracks. Results of SiGMA analysis are plotted at locations of
AE sources with crack types and orientations in the same figure. At the meso-scale, all the typesof micro-cracks are observed as AE sources in SiGMA analysis. This implies that micro-cracks
are accumulated and macroscopically the cracks are nucleated as the surface crack and the diagonal cracks. In the photo, the crack, which propagated from the hole to the bottom, corresponds to the surface crack. Around this crack, numerous tensile cracks of AE sources are observed, which are almost oriented as vertical to the surface-crack plane. During the test, the surface crack was observed first, and then two diagonal cracks propagated. Around these cracks,
AE sources are composed of tensile, mixed-mode and shear cracks. It is noted that the final
cracks of the surface crack and the diagonal cracks consist of one crack surface at themacro-scale, but actually many micro-cracks are nucleated around them. This is a typical cracking mechanism in concrete. Mechanisms of corrosion cracking in concrete are of crack-opening
failure, but the cracks follow zigzag paths, thus explaining nearly equal contributions of
mixed-mode and shear cracks at the meso-scale.

Fig. 15 Observed cracks (left) and visualized results of SiGMA analysis (right).
4. Conclusion(1) The recommended practice (NDIS 2421) is applied to cyclic bending tests of reinforced
concrete beams. The applicability of the criterion to qualifying the damage is confirmed. Thus,
it is demonstrated that the criterion to assess the damage is useful for inspection of beams inservice.
(2) Applying AE rate process analysis to compression tests of concrete cores, it is demonstrated that relative damages estimated are in reasonable agreement with actual damages. The
procedure is promising to estimate the damage of concrete quantitatively without knowing initialproperties at construction.
(3) It is found that AE monitoring could provide earlier warning of the corrosion than thehalf-cell potentials. By continuously monitoring AE activity, both the onset of corrosion in reinforcement and the nucleation of cracking in concrete could be identified nondestructively.
31

This suggests that AE monitoring is practically applicable to in situ monitoring for the corrosion
damage in reinforced concrete structures.
(4) Crack propagation due to corrosion of reinforcement in concrete is studied experimentally. Cracking mechanisms are investigated by SiGMA with 3-D VRML display. The surface
crack and the diagonal cracks are observed at the macro-scale, while many micro-cracks are
identified by SiGMA analysis around them. The results show a promise to apply SiGMA procedure to investigate micro-scale or meso-scale mechanisms of cracking in concrete.
All results discussed are closely associated with standardization actions in the committee,
RILEM TC212-ACD, for proposing RILEM recommendations.
References
ASTM C876 (1991), Standard Test Method for Half-Cell Potentials of Uncoated Reinforcing
Steel in Concrete, ASTM.
JCMS-III B5706 (2003), Monitoring Method for Active Cracks in Concrete by Acoustic Emission, Federation of Construction Materials Industries, Japan.
NDIS 2421 (2000), Recommended Practice for In-Situ Monitoring of Concrete Structures by
AE, Japanese Society for Nondestructive Inspection.
Ohtsu, M. (1995),”The History and Development of Acoustic Emission in Concrete Engineering,” Concrete Library of JSCE, 25, 121-134.
Ohtsu, M. (2000),”Moment Tensor Analysis and SiGMA Code,” Acoustic Emission-Beyond theMillenium, Elsevier, Amsterdam, pp. 19-34.
Ohtsu, M. and Watanabe, H. (2001),”Quantitative Damage Estimation of Concrete by Acoustic
Emission,” Construction and Building Materials, 15(5-6), 217-224.
Ohtsu, M., Uchida, M., Okamoto, T. and Yuyama, S. (2002),”Damage Assessment of Reinforced
Concrete Beams qualified by AE,” ACI Structural Journal, 99(4), 411-417.
Shigeishi, M. and Ohtsu, M. (2003),”Virtual Reality Presentation of Moment Tensor Analysis by
SiGMA”, J. Korean Society for NDT, 23(3), 189-199.

32

ACOUSTIC EMISSION MONITORING OF REINFORCED CONCRETE
FRAME DURING SEISMIC LOADING
A. ANASTASOPOULOS, S. BOUSIAS* and T. TOUTOUNTZAKIS
Envirocoustics SA, El. Venizelou 7 & Delfon, 14452 Athens, GREECE;*
Structures Laboratory, Dept. of Civil Eng. Univ. of Patras, 26500, Patras, Greece
Abstract
Acoustic emission (AE) monitoring was performed during pseudo-dynamic testing of an old,
two-storey, one-by-one bay reinforced concrete frame structure. The structure represented a 0.7scale model of a real-size frame structure designed and detailed according to the standards
prevailing in Southern Europe in the 60's without engineered earthquake resistance. Real-timemonitoring of AE activity versus the complex applied load resulted in semi-quantitative damage
characterization as well as comparative evaluation of the damage evolution of the different size
columns. Evolution of the AE energy rate per channel, as revealed from zonal location, and theenergy rate of linearly located sources enabled the identification of damage areas and the forecastof crack locations before cracks were visible with naked eye. In addition to that, the results ofpost-processing evaluation allowed for the verification of the witnessed damaged areas andformed the basis for quantitative assessment of damage criticality.
Keywords: Reinforced concrete structures, earthquake damage assessment, pseudo dynamic
loading, real-time monitoring.
Introduction
Old, substandard RC buildings designed in the 60’s on the basis of vertical loads only areoften characterized by irregular distribution of strength and stiffness in-plan. These structuralcharacteristics, usually dictated by architectural requirements, are coupled with those owing to
the design according to non-seismic design codes of the 60-70’s in Southern Europe (e.g.,
insufficient detailing of reinforcement, low concrete strength) result in structures with increased
vulnerability to earthquakes. An experimental program of a 0.7-scale model of a real-size frame
structure designed and detailed as old RC buildings in Greece, without engineered earthquake
resistance, was carried out at the Structures Laboratory of the Department of Civil Engineering at
the University of Patras, employing the pseudo-dynamic testing method. To represent the actual
torsional seismic response of such a structure, pseudo-dynamic testing was performed with four
degrees of freedom (DOF): the displacements of the two floors in the direction of the actuators,
plus the two floor rotations with respect to the vertical axis. The structure was excited by a 15sec-long unidirectional input motion that fitted well with the 5%-elastic spectrum of Eurocode 8
modulated after one component of the Herzegnovi record in the 1979 Montenegro earthquake.
Due to their sub-standard design, the columns of the building, both at ground and upper floor,
exhibited low strength and ductility capacity and, thus, damage was expected to develop first attheir ends, due to the insufficient reinforcement overlapping length there. Acoustic emission wasused as an NDT method, to monitor damage development at these vulnerable areas. Due to thecomplex nature of the applied loading and structural response, techniques previously proposed(Matsuyama et al., 1993, Yuyama et al., 1999) for AE evaluation during controlled stimulus, are
not directly applicable. Different source location techniques (zonal, linear and 3-D location) were
J. Acoustic Emission, 25 (2007)33© 2007 Acoustic Emission Groupapplied in order to identify potential high risk areas of increased AE activity during loading. A
summary of the experimental procedure and the derived results are reported and discussed
herein. Comparison of the differences detected in AE behavior among the different columns aswell as between the base and the 1st floor joints is performed and the results are correlated withthe applied load. Further processing of the located AE events, enabled the verification of thedamage areas observed and the assessment of the building structural integrity.
Experimental Set Up
A nearly full-scale model simulating the structural configuration and seismic response ofreal-size reinforced concrete structures was constructed and tested. The structure comprised of
two floors and one bay per direction, while its four columns formed pairs of unequal resistanceand stiffness along the longitudinal (X) direction of the structure. The plan dimensions of thespecimen structure were 3.15 x 3.85 m and the floor height was 2.30 m and 2.0 m at ground and
top floor, respectively as presented in Fig. 1. To create an asymmetric structure that respondswith torsion under translational ground motions, columns of one side were one-half size of thoseof the other side. All columns were 0.175-m wide while their depth varied from 0.175 m for thepair of columns at one side of the structure, to 0.35 m for the opposite pair. Columns were caston spread footings anchored on the strong floor of the laboratory and were longitudinallyreinforced with four 10-mm-diameter smooth (S220) bars for the columns with the smaller
section, and six 10-mm-diameter bars for the larger ones. Transverse reinforcement consisted of
6-mm-diameter smooth (S220) bars placed at 200-mm apart. The beams and the slabs wereheavily reinforced so that inelastic response would appear first on columns. A mean concrete
strength of 22.8 MPa and 18.05 MPa was obtained on the day of testing at the ground and upper
floor, respectively.

X

Z
Y

Fig. 1. Specimen layout, left, and AE sensor positions, right, (AE channels. 1-8 placed oncolumn 1, ch. 9-16 on column 2, ch. 17-24 on column 3 and ch. 25-32 on column 4.).
Prior to AE monitoring, the structure was subjected to the selected earthquake record scaled
at 0.30 g. During this initial testing phase, the structure exhibited strong torsional response,34which - contrary to current beliefs based on simplistic analytical studies - induced larger
displacements on the more flexible side of the building. Columns were subjected to biaxial
flexure and exhibited damage concentrated at the base of smaller ground-floor columns, owing to
the inadequate force transfer along the small bar overlapping length and slippage of vertical bars
within the footing. Beams were over-reinforced – as it was common in this category of structures– and displayed no damage. Some hairline diagonal cracks were observed on the sides of thelarger columns, owing to the development of torsional moments in these members, which closed
after the test. Some cracks formed also at the beam-column interface due to slippage of the(smooth) column reinforcing bars within the joint. To reinstate the torsional balance, smallercolumns (denoted here as columns 1 and 2) were reinforced via a 0.05-m-width concrete jacket
cast from the footing to the top of the structure. The structure was excited again with the same
seismic record at 0.30 g, while being monitored with AE.

Fig. 2. Retrofitted structure: slab displacement (meters) at the ground and the top floor (top); slab
rotation (rads) of the ground and the top floor (bottom).
35

The observation of the structure during testing and the recorded displacement and rotation
data (Fig. 2) reveal that the torsional response almost disappeared and the building displacedalong the axis of excitation. The observed damage consisted of cracking in the larger columns
and spalling of the concrete cover due to and along the lap-spliced bars at the base of thecolumns. Cracking also developed at the beam-column joints at the ground and the top floor.
During the later stages of the excitation though - and due to the damage inflicted on the larger
columns - the torsional response re-appeared, albeit at very low amplitude.
Acoustic emission monitoring was performed using a 40-channel PAC-DiSP (PAC, 2001)
system. The sensors used were standard PAC resonant type R15I, 150-kHz resonance with 40dB integral preamplifier. The position of the AE sensors and the points of applied deformation
are shown in Fig. 1. Sensor position was selected in such a way to permit linear location along
the column height and linear x-y location at the 1st floor joints. Prior to the test the AE sensors
were calibrated and the velocity of the AE signals was calculated in order to achieve the best
possible sensor set-up sufficiently covering the areas with expected high AE activity. In addition
to that the attenuation of the AE signals was measured, Fig. 3.

Fig. 3. Attenuation of the AE signal in column 2 and 3.
Acoustic Emission Results
Prior to the actual test a preliminary, trial loading was performed at low load levels. The
maximum load of the preliminary test was 2% of the load that the building was eventuallyloaded. The AE activity that was recorded during the preliminary test was relatively low withlow energy levels, as presented in Fig. 4. This preliminary test resulted in useful information
concerning the background noise of the system as well as for setting up acquisition parameters
for the AE system. In addition to that, the activity recorded in columns 1 and 2, due to thefriction between the jackets and the concrete frame, was useful during the analysis phase indiscriminating genuine AE signals from friction and defining damage evaluation levels.
The force exerted by each one of the four actuators is presented in Figs. 5 and 6 (fourbackground plots for actuators 1 to 4, respectively) together with AE energy rate from eachcolumn. After jacketing the flexible columns, the building response was mainly uniaxial. The
complex nature of loading (cyclic loading with varying amplitude and phase shift between
actuators) resulted in complicated structural displacements and render infeasible the direct
correlation between the applied loading and the AE activity. The majority of the AE activity,
around 98%, is emitted from columns 1 and 2. As can be seen from Figs. 5 and 6, the AE activity
starts at a deformation level exceeding that which caused the damage during the past loading36

Fig. 4. Applied load (actuator 1) and total energy for the 2% test.

Fig. 5. Applied load at actuators 1 & 2 (background plot) and AE energy from flexible sidecolumn 1 (channels 1-8) and for column 2 (channels 9-16), respectively.
(initial test at 0.30 g). Direct comparison between past and present loading, as a means to
calculate Felicity effect, is not possible due to the different nature of applied loads as well as the
addition of concrete jacket on columns 1 and 2. Moreover, it is worth noting that there is anabsence of AE activity during a displacement hold at relative high force level between 50005800 sec, implying no creep effects during that period.
Finally, it is worth noting that the energy scale for columns 3 and 4 in Fig. 6 is one order ofmagnitude lower than that for columns 1 and 2 in Fig. 5.
37

Fig. 6. Applied load at actuators 3 & 4 (background plot) and AE energy from stiff columns 3(channels 17-24) and 4 (channels 25-32), respectively.
Source Location and Correlation with Visual Inspection
During the entire test high AE data rates were recorded on most channels covering thestructure, especially on the channels attached on flexible columns 1 & 2 after they had beenexternally reinforced by means of concrete jacketing. The use of various AE signal sourcelocation algorithms provided the necessary focus of the analysis to locate the main areas where
damage developed and to find areas with high likelihood of failure. A complex source-location
strategy was employed with each column being treated as a separate entity to avoid signal timeoverlap and event mirroring. The basic source location algorithms used to locate damage was theLinear X-Y and Linear 3D modes in Noesis AE Data Analysis software (Envirocoustics SA,
2005). Typical location results for column 1 are presented in Fig. 7. The height of column 1 isrepresented on the horizontal axis of the plot, starting from sensor #8 (0 m column bottom) up to
sensor #1 (3 m, top area of column) and sensors #3 and #4 projected also on the x-axis.
The linear location strategies showed the areas sustaining damage during the test. Thisdamage is attributed to cracks and hairline cracks or crack face friction in the columns and thereinforcing jacket. The two un-strengthened columns (columns 3 and 4) showed little located AE
activity compared to the other two columns. Any activity was mainly from the joint at the firstfloor and more specifically in the area of beam-column-slab joint at the floor slab. Some activity
was also recorded from the lower section of the columns. Columns 1 and 2 that had sustained
damage during the initial earthquake record of 0.3 g and had been reinforced with concrete
jackets produced significantly more AE activity, which corresponded to more clearly visible
hairline cracking on the jacket itself. This is attributed to the eventual slippage between the initial38

concrete and the jacket. Visual inspection did not yield any information about damage to theinner column. In this case also, the located activity was concentrated to the lower section of thejoint and immediately below the joint of the 1st floor. This is clearly shown in Fig. 8 of Column
1, where the concentration of activity is immediately below the node is the most significant of
any column in this test.

Fig. 7. AE energy for column 1 with respect to sensor location along column height.
This concentration of activity shows the areas that are actually subjected to damage during
the test. It is generally estimated that any failure during the re-positioning of the structure to ahigher deformation state will occur in these areas first. Visual inspection and correlation ofvisible cracks with AE located activity was not always possible during the test, since it was
found by closer inspection later and the present data analysis that cracking, which was invisible
to naked eye produced significant AE. Although no cracks of significant width were observed,
either on the retrofitted or the un-retrofitted columns, the amount of AE recorded distinguishes
these areas as subject to further damage. The success of AE to individualize, at an early stage,
the specific areas, at which medium damage is manifested and most likely enhanced at higher
drifts, was demonstrated in subsequent tests to 0.45 g and 0.55 g. These tests (not reported in thiswork) produced a level of damage sufficient to visually confirm the existence of concrete
cracking and reinforcement slippage. Significant damage at the joint at the top of Column 1
mentioned above was observed and in other areas where AE had given early warning. Morespecifically, increased AE activity recorded from both the node and the base of column 2.
Compared with the activity and events identified on column 1, the activity at the node of column
2 was lower while that at the base (channel 16) was higher.

39

Fig. 8. Event density plot with respect to column height (sensor numbers on the left), x-axis
represents time and density represents events concentration (scale 0 to 13 events).
Concerning the activity from the joints of columns 3 and 4, AE events of similar level,
indicating the onset of emission, were recorded from both columns. For example, the events
located on column 4 versus time are presented in Fig. 8. More specifically the AE activity isdisplayed for the full height of the column (y-axis, with sensor number) versus time (horizontalaxis). For example, at approximately 800 sec, there is some activity located at joint (sensors #27,
#28, #29). On the other hand, the base of column 4 (channels #31 and #32) emitted more signals
resulting in well-defined sources. Figure 8, in conjunction with Fig. 6, provides important
information about the behavior of column 4 and its correspondence to the level of structuraldeformation and time parameters. The successful correlation of located events, both in time andposition, with the hairline cracks observed, is worth noting.
Discussion and Conclusions
A series of full-scale tests on retrofitted or un-reinforced concrete structures were performed,
during which acoustic emission was monitored. The retrofitted columns of the building tested
showed minor cracking on the reinforced concrete jacket with some damage developing at thebase of the ground floor and the joints of the structure. Throughout the test, AE monitoring
identified activity at the bottom of ground floor columns and the joints of the ground-floor beamcolumn intersection, thus, indicating the most damage prone areas. This was evident by theincreased overall AE activity in these areas and significant activity concentration.
Results during subsequent loading tests under 0.40 g and 0.45 g seismic events (not reportedhere) proved that the AE method identified correctly the areas of damage as these were the exact
locations where failure occurred (large cracks, concrete spalling and slippage of reinforcement).

40Based on conventional analysis, AE data signatures from crack and reinforcement slippage
cannot be easily separated. Future work utilizing displacement measurements at the various
positions around the structure for correlation with AE activity versus time and position as well as
unsupervised pattern recognition for the treatment of AE data, can provide further insight to thedamage mechanisms (crack propagation, reinforcement slippage etc) and possibly yield
quantitative damage growth assessment in similar structures and repair techniques.
Acknowledgments
The work reported in this paper was funded by the Greek General Secretariat for Research
and Technology, through the project ARISTION (ARISTION, 2006) within the framework of
the program “Built Environment and Management of Seismic Risk”.
References
Anastasopoulos, A., Bousias, S.N., Tsimogiannis, A., Toutountzakis, T., “Acoustic emission
testing of concrete frame during pseudodynamic loading”, 27th European Conference on
Acoustic Emission Testing, Cardiff, Sept. 2006.
DiSP User’s Manual Rev 1 & AEWin User’s Manual Rev. 2, Physical Acoustics Corp, Sept
2001.
Bousias, S. N, Fardis, M.N., Spathis, L.-A. and Kosmopoulos, A. “Pseudodynamic response of
torsionally unbalanced 2-story test structure”, J. of Earthquake Engng Struct. Dyn. 36:1065–
1087, 2007.
Bousias, S.N., Spathis, A-L, Fardis, M.N., Papanicolaou, C.C., and Triantafillou, T.C.
"Pseudodynamic Tests of Non-Seismically Designed RC Structures Retrofitted with TextileReinforced Mortar", 8th International Symposium on Fiber Reinforced Polymer Reinforcement
for Concrete Structures (FRPRCS-8), Patras, July 2007.
Bousias, S.N., Spathis, A-L, and Fardis, M.N. "Experimental Seismic Response of Eccentric RC
Structures, before or after CFRP-Rehabilitation", 8th International Symposium on Fiber
Reinforced Polymer Reinforcement for Concrete Structures (FRPRCS-8), Patras, July 2007.
Matsuyama K., Fujiyama T., Ishibashi A. and Ohtsu M., (1993), “Field Application of Acoustic
Emission for the Diagnosis of Structural Deterioration of Concrete”, J. of Acoustic Emission,
11(4), 65-73.
NOESIS Ver. 4.1 (2005) - Reference Manual, Envirocoustics S.A.
www.aristion.gr (2006).
Yuyama S., Okamoto T., Shigeishi M., Ohtsu M. and Kishi T., (1999), “A proposed standard forevaluating structural integrity of reinforced concrete beams by acoustic emission”, ASTM STP
1353, pp. 25-40.

41

ACOUSTIC EMISSION LEAK TESTING OF PIPES FOR PRESSURIZEDGAS USING ACTIVE FIBER COMPOSITE ELEMENTS AS SENSORSANDREAS J. BRUNNER and MICHEL BARBEZAT
Empa, Swiss Federal Laboratories for Materials Testing and Research,
Laboratory for Mechanical Systems Engineering,Überlandstrasse 129, CH-8600 Dübendorf, Switzerland

Abstract
In order to explore potential acoustic emission (AE) applications for piezoelectric activefiber-composite (AFC) sensor elements made from piezoelectric fibers, a model experiment forleak testing on pipe segments has been designed. A pipe segment made of aluminum with a diameter of 50 mm has been operated with compressed air (gaseous medium) for a range of operating pressures (between 400 and 800 kPa). Leaks have been simulated by use of screws withholes of various diameters (between 0.1 to 1.2 mm). One AFC sensor has been mounted directlyon the pipe surface, complemented by two conventional AE sensors mounted on waveguides. AE
signal parameters and waveforms were recorded at different pressures with and without simulated leaks. The experiments to date show distinct differences in the power spectra obtained fromfast Fourier transform of the AE waveforms depending on whether a leak is present or not.
Keywords: Leak testing, pressurized pipe, gaseous media, active-fiber-composite sensors, comparative analysis
1. Introduction
Leak testing can be performed with various non-destructive test methods, among them acoustic emission (AE), on both, pressure vessels [1-4] and pipes or pipelines [5]. Leak testing withAE can be performed to indicate the presence of leaks and/or to locate the leak position, as well[6]. The references indicate that AE leak testing can be performed on pressure vessels made frommetal [2, 3] or composites [4], containing either gaseous [2] or liquid media [3].
Based on a model experiment for detecting the presence of a leak in a pipe segment pressurized with fluid (water) and gaseous media (compressed air) using conventional AE sensors [7],the present contribution investigates the feasibility of performing leak testing with piezoelectricactive-fiber-composite (AFC) elements instead of AE sensors. AFC are made with commerciallyavailable piezoelectric fibers that are embedded in an epoxy matrix and sandwiched between interdigitated electrodes. Originally developed as anisotropic piezoelectric actuators [8], AFC elements can also be used as piezoelectric sensors. The tests are monitored with the same AE sensors as in the previous experiment [7] for comparison. AFC sensors are thin, planar elements andoffer an advantage compared with conventional AE sensors since they are conformable to curved
surfaces [9]. Mounting AFC sensors on pipes or pressure vessels does hence not require the useof waveguides, simultaneously reducing time required for mounting and attenuation.

J. Acoustic Emission, 25 (2007)42© 2007 Acoustic Emission Group2. Experimental
The model pipe system for the laboratory tests was similar to that reported in [7], i.e. using
an aluminum pipe segment, but with a length of 60 cm and a diameter of 50 mm, with a wall
thickness of 2 mm (Fig. 1). Leaks were simulated by inserting screws with a hole into a borehole in the wall of the pipe segment, located at about one quarter length (Fig. 2). The diametersof the holes in the screws comprised about 0.1, 0.2, 0.35, 0.50, 0.80 and 1.2 mm (Fig. 3). The
holes were manufactured by conventional drilling, except for diameters of 0.1 and 0.2 mm,
which were made by laser drilling. For the screws with hole diameters below 0.5 mm, the nominal diameter extended about 1 mm into the screw, opening into a hole with a larger diameter (1mm diameter for the 0.1-and 0.2-mm leaks, 2 mm for the other leaks). The pipe segment wasthen equipped with fittings connected to the supply of the medium (pressurized air). Pressure in
the pipe segment was read off a pressure gauge and noted for each test. The flow of the medium
was not recorded and the pressure adjusted by the outlet valve mounted between pressure gauge
and supply pipe. The pipe segment was put on wooden supports for the tests.

Fig. 1: Overview of the laboratory-scale model system for AE leak monitoring with conventional
AE sensors and AFC elements. The inlet valve is on the left, the T-joint on the right connects tothe pressure gauge (not shown), the outlet valve is further to the right (not shown).

Fig. 2: Simulated leak by inserting a screw with a hole of a specified diameter into a bore-hole atone-quarter length of the aluminum pipe segment; screw-insert without hole for reference shown.
AE data have been recorded with three channels using a system with transient recording capability (type AMS-3 from Vallen Systeme GmbH). One channel was used with the AFC sensor,
the others with conventional AE sensors (type SE45-H from Dunegan Engineering Corp).
Thresholds were set at 50 dBAE, preamplifier gain at 34 dB, the rearm time to 3.2 ms, and43

frequency band-pass filtering between 30 and 1,000 kHz was used in the preamplifiers and thedata acquisition channels. Beside the AE signal parameter set, AE waveforms were recorded (5MHz sampling rate) with the same threshold. AE data were analyzed with the equipment specificsoftware (VisualAE and VisualTR from Vallen Systeme GmbH). The AFC sensor was mounted
directly on the surface of the pipe using a commercial, thermosetting two-component epoxybased adhesive, while the AE sensors were mounted on waveguides using a silicone-free vacuum
grease as coupling agent and duct tape for applying the contact pressure. The AFC sensor was
electrically shielded by a self-adhesive aluminum foil, simultaneously connected to its groundwire and the electrical ground of the AE equipment (Fig. 4). The position of the AFC sensor was
about one quarter from the outlet end of the pipe segment on the top position (12 o’clock) that of
the first AE sensor in the bottom position (6 o’clock) shifted towards the leak by about 6 cm
from the center of the AFC. The second AE sensor (9 o’clock) was mounted 180° opposite thebore-hole simulating the leak (3 o’clock) one quarter length from the inlet end of the pipe segment.

Fig. 3: Screws with holes drilled for simulating leaks in the pipe segment model system. Leak
diameters of 0.1 mm and 0.2 mm extend over a length of about 1 mm and then open into a diameter of 1 mm, larger leak diameters extend over a length between 2 and 5 mm.
The conventional AE sensors selected for this study and AFC elements do differ somewhat intheir spectral sensitivity [9]. In addition, AFC elements show a higher sensitivity along the direction of the piezoelectric fibers than normal to it [10]. There are also indications that AFC elements may have a higher sensitivity to in-plane (shear) waves relative to that for out-of-planewaves than the conventional AE sensors. For the experiment, the AFC element was mountedwith the fiber direction parallel to the axis of the pipe segment (compare Fig. 4, center).
Each leak diameter was tested for a range of pressures between about 400 and 800 kPa. The
upper pressure was limited by the supply of compressed air available in the laboratory andshowed some variation (typically about 20 to 30 kPa). Before recording AE signals and waveforms at a selected pressure corresponding to a setting of the outlet valve, the pressure was allowed to equilibrate for about one to two minutes. Nevertheless, for some tests, pressure variations during data acquisition were observed. This variation, however, was less than the pressurechange between different tests. For each setting, data were recorded for about 5 to 10 seconds.
Power spectra of selected AE waveforms within the frequency range between 0 and 400 kHz
were visually compared for the two types of sensor for a given leak and pressure, as well as forincreasing pressure for a given leak diameter and for increasing leak diameter at constant44

pressure, respectively. This is analogous to an investigation reported for leaks with other geometry and size [11].

Fig. 4: AE sensor mounting on the pipe segment for leak testing; (left) conventional AE sensor
on waveguide with duct tape for applying the contact pressure; (center) AFC element adhesively
mounted with a commercial two-component adhesive; (right) AFC element after shielding withan adhesive aluminum foil.
3. Results
Figure 5 shows the power spectra for the pipe without a leak (screw without hole inserted
into the bore-hole of the pipe) indicating frequency contributions between about 30 kHz (limit ofhigh-pass filter) and about 150 kHz for both types of sensor, essentially independent of the pressure of the air (varied between about 400 and 800 kPa). As expected, the power spectra for AE
sensors and AFC elements are somewhat different for comparable pressure values, and thedominant contributions appear at frequencies around 50 kHz. There are no contributions significantly exceeding the noise level at frequencies above about 160 kHz. For a pressure of 800 kPa
both sensors (AE and AFC) show a distinct peak with comparable amplitude just above 150 kHz(note the difference in scale of the graphs for the AE and AFC sensors).
Figure 6 shows power spectra for one leak diameter (0.35 mm) at different pressures between
about 400 and 800 kPa. Compared with the spectra shown in Fig. 5, there are clear contributions
at frequencies above 150 kHz for all pressures that have been tested. These can be observed forboth types of sensor (AE and AFC element). It can be noted that a noticeable contribution to thepower spectra above about 160 kHz appears for the chosen leak diameter of 0.35 mm at about
165 kHz. This contribution can be noted in the power spectra of both types of sensors (AE and
AFC element). A further contribution appears at a frequency around 275 kHz for the AE sensors
and between about 280 and 300 kHz for the AFC element. The latter also shows indications ofanother contribution at about 375 kHz. Indications of that are also observed for the AE sensor,
but are less apparent in the graphs because of the difference in scale. Further, the intensity (amplitude relative to maximum peak in power spectrum) of these contributions is changing with thepressure of the medium (compressed air). Compared with the peak contribution between about
30 and 100 kHz, the peaks at frequencies above 160 kHz are increasing with increasing pressure.
This increase is more pronounced for the AE sensor than for the AFC element.
Figure 7 shows power spectra for selected leak diameters at a constant pressure of about 600
kPa. The graphs in Fig. 7 can be complemented with the center part of Fig. 6 showing the power
spectra for a leak diameter of 0.35 mm at a pressure of 600 kPa. Compared with the spectra
shown in Fig. 5, there are again clear contributions at frequencies above 160 kHz. These can beobserved for both types of sensor (AE and AFC element). Contrary to the case of constant leak45

Fig. 5: Power spectra (0 to 400 kHz) from fast Fourier transform of selected AE waveforms recorded with the AE sensor (left) and with the AFC element (right) for the pipe segment pressurized without simulated leak. The pressure is increasing from about 400 kPa (top) to 600 kPa(middle) and 800 kPa (bottom). Note the difference in amplitude for the different sensors andgraphs.
diameter tested with different pressures, the frequencies are now changing with leak diameter (iftested at constant pressure of about 600 kPa). For a leak diameter of 0.2 mm, a small, but distinct
contribution occurs at about 195 kHz (observed with both sensors), complemented by a weakcontribution around about 330 kHz. For the leaks with diameters of 0.35 and 0.5 mm the frequency contributions shift to about 165 and 275 to 280 kHz, respectively, and an additional contribution around 370 kHz appears. For the leak with 0.8 mm the frequency contributions shift
again to lower frequencies, namely 130 and 265 kHz. The AE sensor indicates additional46

Fig. 6: Power spectra from fast Fourier transform of selected AE waveforms recorded with the
AE sensor (left) and with the AFC element (right) for the pipe segment pressurized with a simulated leak (nominal diameter 0.35 mm). The pressure is increasing from about 400 kPa (top) to
600 kPa (middle) and to 800 kPa (bottom).
contributions centered around 265 and 340 to 350 kHz and the AFC sensor around 230 and 270
kHz, respectively.
4. Discussion
Independent of leak diameter (or more general, leak geometry) and operating pressure of thesimulated pipe, leaks yielded additional contributions in the power spectra of recorded AE signals at frequencies above about 150 to 160 kHz for both types of AE sensor. Of course, this47

Fig. 7: Power spectra from fast Fourier transform of selected AE waveforms recorded with the
AE sensor (left) and with the AFC element (right) for the pipe segment pressurized with a simulated leak at constant pressure of about 600 kPa. The leak diameter is increasing from about 0.2
mm (top) to 0.5 mm (middle) and to 0.8 mm (bottom).
statement holds for the range of leak diameters (0.1 to 1.2 mm) and pressures (about 400 to 800kPa) that have been investigated to date.
A specific leak diameter (geometry) does seem to yield power spectra contributions at thesame frequency, independent of the operating pressure. However, the intensity (amplitude) of thepower spectra contributions indicating the leak is increasing with increasing pressure. At an intermediate, but roughly constant pressure (600 kPa) the power spectra contributions do seem toshift to lower frequencies with increasing leak diameter (geometry). These observations imply
lower bounds for the size of leaks that can be detected within specific operating conditions. For48

small leaks (below 0.1 mm diameter) the indication of the leak may not differ sufficiently fromthe noise in the power spectrum.
Based on the leak testing performed with gaseous and fluid media using conventional AE
sensors on a similar system with the same type of leaks [7], it is expected that AFC elements
would also yield indications of leaks, if the pipe were operated with a fluid medium (water) in asimilar pressure range. Since the pipe used in the previous experiments [7] was of a different diameter (60 mm instead of 50 mm) and shorter, it is expected that changes in the pipe geometry
would not affect the results significantly. Changing to pipes made of a different material has notbeen explored yet. Pipes made of another metal are expected to yield similar results, while forcomposite or polymeric pressure pipes, the pronounced frequency-dependent attenuation in thematerial may affect the sensitivity for leak detection. The dependence of the sensitivity on the
distance between leak and sensor has not been investigated (fixed leak and sensor position in thetests reported here). Depending on the pipe material and the distance of the sensor from the leak,attenuation effects may also reduce the sensitivity. Noise interference from operating conditions
or environment may further affect the sensitivity for leak detection.
As a last remark, it can be noted that the AFC elements permanently mounted on the pipe canalso be used for other types of nondestructive testing or structural health monitoring; for example, electrical impedance measurements [12] by simply connecting the wires to another measurement system.
5. Conclusions
Experiments with a laboratory-scale model system for leak testing of pipes first indicates that
piezoelectric active-fiber-composite (AFC) elements yield acoustic emission signatures (power
spectra) comparable to those recorded with conventional AE sensors. Conformable AFC elements, however, can be mounted directly on pipes without the use of special waveguides. Simulated leaks yield additional frequency contributions mainly above about 160 kHz, while tests
without leaks do not show significant contributions above this limit. Varying leak diameter atconstant test pressure shifts the additional frequency contributions to lower frequencies, whilevarying the test pressure for constant leak diameter changes the intensity of the additional frequency contributions relative to the dominant peaks in the power spectrum at frequencies below
150 kHz. This indicates that there may be a lower limit of the leak diameter (size) that can reliably be detected.
Acknowledgment
The manufacturing of the model pipe system including the simulated leaks, its setup, as wellas technical support during operation by Mr. K. Ruf is gratefully acknowledged.
References[1] M.A. Goodman, R.K. Miller “Acoustic Leak Testing”, in R.K. Miller, E.v.K. Hill, P.O.
Moore (Eds.), Nondestructive Testing Handbook, Vol. 6 Acoustic Emission Testing, 3rd ed.,American Society for Nondestructive Testing, Columbus, 181-226, 2005.

49

[2] ASTM Standard Test Method for Examination of Seamless, Gas-Filled, Pressure Vessels Using Acoustic Emission, E1419-02b, American Society for Testing and Materials International,
Book of Standards, Vol. 03.03 (annual edition).
[3] ASTM Standard Test Method for Examination of Liquid Filled Atmospheric and Low Pressure Metal Storage Tanks Using Acoustic Emission, E1930-02, American Society for Testingand Materials International, Book of Standards, Vol. 03.03 (annual edition).
[4] ASTM Standard Test Method for Acoustic Emission Examination of Pressurized Containers
Made of Fiberglass Reinforced Plastic with Balsa Wood Cores, E1888/E1888M-02, American
Society for Testing and Materials International, Book of Standards, Vol. 03.03 (annual edition).
[5] R.K. Miller, A.A: Pollock, D.J. Watts, J.M. Carlyle, A.N. Tafuri, J.J. Yezzi Jr. “A reference
standard for the development of acoustic emission pipeline leak detection techniques”, NDT&E
International, 32(1), 1-8, 1999.
[6] ASTM Standard Practice for Leak Detection and Location Using Surface-Mounted Acoustic
Emission Sensors, E1211-02, American Society for Testing and Materials, International, Book ofStandards Vol. 03.03 (annual edition).
[7] A.J. Brunner, M. Barbezat “Acoustic Emission Monitoring of Leaks in Pipes for Transport ofLiquid and Gaseous Media: A Model Experiment”, Proceedings 27th European Conference on
Acoustic Emission, Advanced Materials Research, 13-14, 351-356, 2006.
[8] A.A. Bent, N.W. Hagood “Piezoelectric fibre composites with interdigitated electrodes”,
Journal of Intelligent Material Systems and Structures, 8(11), 903-919, 1997.
[9] A.J. Brunner, M. Barbezat, P. Flüeler, Ch. Huber: “Composites from piezoelectric fibers as
sensors and emitters for acoustic applications”, Journal of Acoustic Emission, 22, 127-137, 2004.
[10] M. Barbezat, A.J. Brunner, Ch. Huber, P. Flüeler: “Integrated Active Fiber Composite Elements: Characterisation for acoustic emission and acousto-ultrasonics”, Journal of Intelligent
Material Systems and Structures, 18(5), 515-525, 2007.
[11] K. Yoshida, H. Kawano, Y. Akematsu, H. Nishino “Frequency Characteristic of Acoustic
Emission Waveforms during Gas Leak”, Proceedings 26th European Conference on Acoustic
Emission Testing, German Society for Nondestructive Testing, BB 90-CD, 321-327, 2004.
[12] G. Park, H. Sohn, C.R. Farrar, D.J. Inman “Overview of Piezoelectric Impedance-based
Health Monitoring and Path Forward” The Shock and Vibration Digest, 35(6), 451-463, 2003.

50

ACOUSTIC EMISSION TECHNIQUE APPLIED FOR MONITORING
AND INSPECTION OF CEMENTITIOUS STRUCTURES
ENCAPSULATING ALUMINIUM
L. M. SPASOVA, M. I. OJOVAN and C. R. SCALES*
Immobilisation Science Laboratory, University of Sheffield, Sir Robert Hadfield Building,
Mappin St., Sheffield, S1 3JD, UK; *Nexia Solutions, Sellafield, Cumbria, CA20 1PG, UK

Abstract
The acoustic emission (AE) technique and data analysis have been applied to detect andcharacterise the corrosion of aluminium and its impact on the mechanical stability of encapsulating cementitious systems. To that purpose laboratory scale samples prepared by ordinary Portland cement (OPC) and blast furnace slag (BFS)/OPC to mass ratio 7:3 with encapsulated aluminium rods were monitored for AE. The cumulative number of signals generated by piezoelectric broadband transducers attached to the samples and recorded by a computer-controlled AE
system provided initial indication for the active corrosion process and damage development
within the cementitious structures. Additional analysis based on the time domain parameters of
the recorded AE signals (in relation to the visual and optical microscopy observations) was used
to conclude on the potential AE sources active on micro and macro scale. According to their frequency characteristics the AE signals collected were divided into two main groups. The firstgroup consisted of a large number of signals with two major frequency components distributed
respectively below and above 100 kHz. The second group comprised of fewer resonance-likesignals with a single frequency component at 34 kHz. Wavelet transformation was subsequently
applied for frequency-time analysis. Calculation of the cross-correlation function in time domain
was performed to assess the similarity of the acquired AE signals and therefore to conclude on
the potential sources activity in time and space. The AE technique was shown as a potential tool
for monitoring of the aluminium corrosion development associated with accumulation of structural damage within the encapsulating cementitious structures.
Keywords: Cementation of radioactive wastes, corrosion of aluminium
Introduction
Over a number of years in the UK, cementation of low and intermediate level radioactive
waste (LLW and ILW) has been developed and carried out on an industrial scale resulting in the
manufacturing of tens of thousands of containers with cemented radioactive wastes, e.g., cementitious monoliths termed wasteforms (Main UK Radioactive Waste Inventory Report, 2005).
Composite cement-based formulations with partial replacement of ordinary Portland cement(OPC) by latent hydraulic materials such as blast furnace slag (BFS) or pozzolanic materials
such as pulverised fuel ash (PFA) have been extensively used for encapsulation of liquid andsolid ILW such as sludges, ion exchange resins and metallic debris (Ojovan and Lee, 2005;
Sharp et al., 2003; Streatfield, 2001). The cemented wastes (wasteforms) have been sealed into
500-litre stainless steel containers (Generic Waste Package Specification, 2005; WSP/100, 2005)
and placed in interim storage for a minimum of 50 years before final disposal.
The characteristics of the cementitious wasteforms such as setting time, temperature elevation during early stages of hydration, the presence of surface water as well as the mechanical
J. Acoustic Emission, 25 (2007)51© 2007 Acoustic Emission Groupstability and tensile strength of the final hardened material have been studied for encapsulationof different waste streams (Streatfield, 2001). However, the mechanical behaviour of the cementitious wasteforms in tens and hundreds of years of storage before final disposal can be substantially affected by chemical and electro-chemical interactions between the waste and the immobilising matrix (Streatfield, 2001).
The corrosion of metallic ILW such as aluminium and magnesium-aluminium alloy, termed
Magnox, encapsulated in composite cements is a well-known process that can cause damage of
the wasteforms due to hydrogen gas generation and deposition of an expansive layer of corrosion
products such as aluminium hydroxide and strätlingite or brucite (Setiadi, 2006; Setiadi et al.,2006). The main sources of metallic ILW in the UK are the decanning of the spent nuclear fuelelements and those already accumulated at nuclear sites as legacy radioactive waste aluminium.
According to the latest Radioactive Waste Inventory Report (2005) in the UK there are ~8000
tonnes of Magnox and aluminium classified as ILW, and more than 23000 cementitious wasteforms containing radioactive Magnox produced by the encapsulation plants at Sellafield site.
Given the scale of these figures the safe storage of the wasteforms with encapsulated metals andassessment of their acceptability for transportation and final disposal is an important issue for the
nuclear industry. One of the solutions extensively researched to limit the corrosion of the encapsulated aluminium and Magnox is the application of alternative cement-based formulations suchas calcium sulfoaluminate cements, condensed silica fume and sulphate resisting Portland cements (Streatfield, 2001; Zhou et al., 2006). However, non-intrusive methods such as AE applied
for continuous monitoring and inspection of the cementitious wasteforms would be of significant
benefit.
Advantages of the AE Technique to Monitor Cementitious Structures Immobilising Metallic Radioactive Wastes such as Aluminium
The AE technique has a number of advantages which make it a potentially suitable tool tomonitor and inspect structures immobilising nuclear wastes. These are the high sensitivity of the
AE technique not only to macro-scale damage but also to micromechanical events providing indication for structure degradation in progress (Ono, 2006; Pollock, 1989). AE testing is easy to
implement in-situ and examine the overall volume of the structure with a network of sensors. In
theory the monitoring can be established for an unlimited interval of time without a need for additional forces to be applied to the structures. Nevertheless, when used as an applied tool the AE
technique requires good knowledge of the propagation properties of the medium associated withwave reflection, dispersion or absorption, the type of active sources, e.g., cracks formation andpropagation due to compression or tensile loading, fatigue testing, corrosion development, theoperational frequency range of the sensors used and the quality of their attachment to the examined structure. Moreover, under conditions of high temperature, aggressive solutions or radioactivity, specially designed sensors or waveguides are needed. All of these factors (determined by
the experimental setup) lead to collection of AE signals with diversified parameters and development of relatively complex methods for AE sources location, orientation and differentiationsuch as moment tensor and wavelet (Grosse et al., 2004; Ohtsu, 2000). However, the AE technique has achieved a high level of capability and industrial approval in various applications witha large database of experimental setups on a wide range of materials, components and structuresand procedures for data analysis (Ono, 2006; Pollock, 1989). The major distinct steps in themethodology applied for AE data analysis include (a) variation of the AE signals rate in relation
to the alteration of the experimental conditions, e.g., level of loading, (b) change of the AE signal

52

parameters in time domain and (c) more advanced and computational resources demanding signal processing.
Although used for many years on a large range of materials and structures, the application of
the AE technique in the field of nuclear waste management is not fully explored and very limited. Apart from our previous work (Spasova and Ojovan, 2007; Spasova, Ojovan and Scales,
2006a; Spasova and Ojovan, 2006b) only Belov and Aloy (2004) reported on the potential of the
AE technique to be applied as a tool for quality control during fabrication of glass and ceramic
used for immobilisation of nuclear wastes. Therefore, the AE technique applied for monitoringand inspection of radioactive wasteforms is a new area with a potential for future development
and implementation.
AE Technique Applied for Cementitious Structures
AE testing and monitoring of cement-based structures for civil engineering applications has
been one of the main driving forces for the development and industrial implementation of the AE
technique. For different cement-based materials, the threshold levels used to determine separate
phases in the mechanical performance of the cementitious structures under compression or tensile loading have been defined based on strain-stress, load-displacement or load-time curves
combined with AE detection and analysis and visual or optical microscopy observations. The
physical mechanisms such as microcrack initiation and propagation, formation of a fracture
process zone (FPZ), macrocrack nucleation and subsequent growth in relation to the size andshape of the FPZ have been associated with the parameters of the recorded AE signals (Landis
and Ballon, 2002; Landis, 1999; Yoon et al., 2003).
A significant contribution to understanding the mechanism of failure of concrete due to the
expansive corrosion process of metallic reinforcement has been performed by Uddin at al.
(2006). Based on boundary element method (BEM) and SiGMA code (Ohtsu, 2000) using thearrival time and the amplitude of first motion of the recorded AE signal waveforms, the crack
type, orientation and location in concrete samples were determined and illustrated in 3D space.
According to Uddin at al. (2006) diagonal visible cracks initiated by uniform distributed pressurearound the reinforcement due to the expansive corrosion process are most likely to be formed
within the concrete structures. In terms of AE, the damage initiation and propagation in the examined concrete samples corresponded to an abrupt increase in the number of the detected AE
signals at peak load caused by a number of tensile, shear and mixed-mode cracks (Uddin et al.,2006).
However, when cementitious structures are monitored for AE, it is important to consider thatthe cement clinker composition, water-to-cement mass ratio, aggregate size and geometry of the
prepared samples, the operational range(s) of frequencies of the sensors and their coupling andlocation can substantially affect the time and frequency domain characteristics of the acquired
signals. Moreover, experiments revealed that in the highly damping medium of concrete, mortar
and cement paste the ultrasonic pulse attenuation and velocity are frequency dependent (Philippidis and Aggelis, 2005) and the attenuation increases exponentially with increase of the distancefrom the source (Tarmaratnam and Tan, 1990).
This paper is part of a series of experimental studies (Spasova and Ojovan, 2007; Spasova,
Ojovan and Scales, 2006a; Spasova and Ojovan, 2006b) on cement-based samples with encapsulated aluminium aiming to assess the feasibility of the AE technique to be applied for cementi53

tious structures with encapsulated ILW. In the present paper, the established methodology from
Spasova and Ojovan (2006b) is used to analyse and compare the collected AE data from a
BFS/OPC sample with encapsulated aluminium and those from a reference OPC and to relate theconclusions drawn for the mechanical changes within the structures during the conducted AE
monitoring with the findings of the post-test visual and optical microscopy observations.
Experimental Setup
Preparation of the specimens
The AE monitoring was carried out on cement-based samples with encapsulated high-purity(99.999%) aluminium rods. Two types of specimens were prepared. The first type was a pure
OPC unit with encapsulated aluminium used as a reference. The second was a composite cement
sample with 70 wt% BFS replacement of OPC (BFS/OPC to mass ratio 7:3) used as a lower limit
in the composition envelope for nuclear waste encapsulation applied by Sellafield Ltd. The OPC
and BFS were supplied respectively by Castle Cement and Redcar Steel Works, UK.
For the preparation of the laboratory scale units the cement powder and water to mass ratio
0.33 were mixed and then poured in 340 mL capacity plastic containers. An aluminium rod witha length ~48 mm and 10 mm diameter (Fig. 1a) was centrally placed in the cement grout before
closure of the plastic containers with airtight lids. Cylindrical cementitious samples were formed
as shown in Fig. 1b. The specimens were cured at 20ºC and 95% relative humidity (RH) in anenvironmental chamber for more than 180 days. Before the commencement of the AE monitoring 50 mL of distilled water was poured in the plastic containers. The latter was done in order tocapture the AE, generated from the cement structures in excess of water. This is a major concern
for the safe storage of the cementitious wasteforms in a long period of time (Ojovan and Lee,2005).
a)b)

Fig. 1. Aluminium rod as is (a) and after being encapsulated for 90-days in an OPC matrix (b).
AE monitoring and data collection
A schematic diagram of the AE experimental setup used in this study is shown in Fig. 2. A
piezoelectric transducer (or sensor) with a diameter of 18 mm and a height of 12 mm in stainlesssteel casing was attached to the bottom of the plastic container, containing the cement-based
sample via a thin layer of grease used as acoustic couplant. Wideband transducers (type WD),calibrated and supplied by Physical Acoustics Corporation (PAC), with broad-band response between 100 and 1000 kHz were used to detect AE from both cement-based samples with encapsulated aluminium. The electrical signals from each of the transducers were amplified by a lownoise preamplifier type 2/4/6/ from PAC set at 40 dB and passed through a bandpass filter operating between 20 kHz and 3 MHz. Each of the analog signals (voltage) then was measured and if54

above the threshold level of 40 dB was digitized by an 18-bit analog-to-digital converter (ADC)with 5 MSPS sampling rate.

Fig. 2. AE experimental setup for monitoring of the cementitious wasteform samples with encapsulated aluminium.
A hit-driven data acquisition process with a PCI-2 based AE system, calibrated and supplied
by PAC, was used. When an acoustic wave was detected by the transducer attached to the sample
and its amplitude exceeded the threshold the data streaming was allowed. The data recorded on aconventional PC hard drive in an ASCII file for each detected and measured AE signal (or hit)consisted of a set of parameters such as duration, amplitude, counts, rise time and absolute(ABS) energy. These were used for post-test analysis. The definition of each of those signal parameters in time domain, denoted in Fig. 3, according to ASTM 1316 standard (1989), are given
by PAC as follows:• Duration: the time interval between the first and the last crossing of the threshold level for
the AE signal;• Amplitude: the largest voltage peak in the AE signal waveform given in dB or V. The amplitude for the PCI-2 based AE system covers the range 0-100 dB (0 dB corresponds to 1
μV at the sensor output and 100 dB to 10 V); The conversion of AE signal amplitude frommV to dB is done using the equation:
V (1)
AdB = 20 log10  max   P
 Vmin 
where P is preamplifier gain given in dB, Vmax and Vmin are the amplitude of the AE wave
given as positive voltage (in V) and minimum voltage measured by the AE acquisition system (1 V for the AE-based system used in this study).
• Counts, Ncounts: the number of AE signal excursions over the threshold;• Rise time: the time between the first threshold crossing and the amplitude of the signal
measured in s;• Absolute (ABS) energy, EABS: quantitative measurement of the energy of the AE hit derived as an integral of the squared voltage signal divided by a reference resistance (10 k
for the PCI-2 based AE system) over the duration of the AE waveform packet and measured in atto joules (10-18 J).
Moreover, the waveform of each AE hit (Fig. 3) was described by 3072 discrete points stored
in the PC memory. The pre-trigger time, used to recover the acoustic waveform before the firstthreshold-level crossing was, set up at 100 s. Selective cross-plots such as amplitude versus duration and histograms, plotted by PAC AEWin software package, provided real-time information
for the AE activity from the samples under monitoring.
55

Fig. 3. Schematic of an AE signal and its associated parameters in time domain.
Results and Analysis
Relationship between the AE hit rate and the time domain parameters of the recorded signals
from the cementitious samples and their potential sources due to corrosion of the encapsulated
aluminium
During the conducted AE monitoring of the cementitious samples with encapsulated aluminium the cumulative AE hit number and associated ABS energy provided initial information (inreal time) for micromechanical events within the structures associated with the generation andrelease of acoustic waves. As it can be seen in Fig. 4 there were several stages in the AE historyof the experiments with discriminative rate (high or low) for both the OPC and BFS/OPC samples with encapsulated aluminium, which can be associated with several typical phenomena (Assouli et al., 2005; Landis and Ballon, 2002; Landis, 1999; Yoon et al., 2003; Uddin et al., 2006).
Firstly, the relatively long period of time of 16.5 and 92 hours for the OPC and composite
cement specimens respectively was associated with a very low AE activity. The sources of thisAE can be related to the process of water infiltration through the cement pores (Assouli et al.,2005) and the porous zone in the aluminium corrosion products layer formed at early stages of
cement hydration (Setiadi, 2006; Setiadi et al., 2006). Subsequently as a result of the aluminium
corrosion re-initiation, this AE can be generated and released due to the stable growth of the preexisting microcracks, formed during the setting and curing of the cement samples due to hydrogen gas evolution and accumulation of corrosion products (Setiadi, 2006; Setiadi et al., 2006).
After this first stage, a new period characterised by an abrupt increase in the number of thedetected signals and their ABS energy was observed likely to indicate nucleation and propagation of critical size crack(s) within the structures corresponding to peak loading (Landis and Ballon, 2002; Landis, 1999; Yoon et al., 2003; Uddin et al., 2006). This period with a duration of
only 3.5 hours for the OPC sample but around 50 hours for the composite cement has been followed by a time interval (2b in Fig. 4) where the AE hit rate considerably decreased in comparison with the previous period 2a in Fig. 4. Similar alteration in the AE hit rate were reported tocorrespond to post-peak loading associated with critical size crack growth in cement-based materials (Uddin et al., 2006; Landis and Ballon, 2002), debonding along the steel reinforcement in
concrete (Yoon et al., 2003) or hydrogen gas release as a result of stainless steel corrosion development (Fregonese et al., 2001; Yonezu et al., 2006) associated with the detection of high56

energy, i.e., high amplitude and long duration AE signals ultimately leading to decrease of theoverall stress level within the structures.
a)b)Fig. 4. Cumulative number of AE hits and associated ABS energy during the AE monitoring of(a) the OPC and (b) BFS/OPC samples with encapsulated aluminium.
Then a period of AE “silence” was observed for the OPC specimen followed by a new jumpin the number of the detected hits during stage 4 in Fig. 4a. However, a period of AE “silence”,
denoted in Fig. 4b as stage 3, was also found in the history of the conducted monitoring of the
BFS/OPC sample with encapsulated aluminium.
A visual observation confirmed that visible radial cracks appeared on the surface of the samples, as shown in Figs. 5a and 5c. The latter were found also in the cross-section of the specimens (Figs. 5b and 5d) likely to be formed before the commencement of this AE monitoring for
the OPC sample, but not expected to be formed within the BFS/OPC structure at that age of hydration (Setiadi, 2006). The initiation and/or propagation of these cracks were the very first potential source of AE identified during our test.

57

aFig. 5. Visible cracks formed at (a) and (c) surface and (b) and (d) cross-section of the OPC and
BFS/OPC samples with encapsulated aluminium respectively.
a) Aluminiumb)
Aluminium
Microcrack

Microcrackc)d)Microcrack
Microcrack

Fig. 6. Micrographs of (a) and (b) splitting cracks between the aluminium rod and the corrosion
products layer and (c) and (d) microcracks within the aluminium corrosion products layer
identified within the examined OPC and BFS/OPC samples.

58

An optical microscopy observation of the samples provided additional evidences for the formation of splitting microcracks between the aluminium rod and the corrosion products layer(Figs. 6a and 6b). These can be formed as a result of the increased compressive forces due to thehydrogen gas generation, friction and release. Moreover, the presence of microcracks within the
corrosion products layer, as shown in Figs. 6c and 6d, indicated for another type of active AE
sources within the cementitious structures.
In an attempt to differentiate collected signals from different sources in the cement-based
structures with encapsulated aluminium the analysis was extended from AE hit time distribution,
providing information for some kind of structural changes to the parameters of the detected signals. According to Pollock (1989) some of the most commonly used acoustic parameters forcharacterisation of the AE sources are amplitude, duration, counts and rise time. To the purposeof this study, we applied a selection procedure proposed by Wu et al. (2000) and also adopted
elsewhere (Spasova, Ojovan and Scales, 2006a; Spasova and Ojovan, 2006b) to classify detectedsignals at each of the stages of the conducted monitoring based on their amplitude, duration andcounts and associate their appearance with the mechanisms causing damage within the cementbased structures.
Table 1 summarises the results from the application of the selection procedure from Wu et al.
(2000) on the parameters of the signals recorded for the BFS/OPC sample with encapsulated
aluminium. Similar parameter-based AE study and differentiation of the signals based on theirduration, amplitude, counts and average ABS energy (EABS,av), calculated as a ratio between the
total number of AE hits (Nhits) and total ABS energy (EABS), for the OPC sample with encapsulated aluminium can be found in Spasova and Ojovan (2006b).
The data in Table 1 show that the AE activity during the stage 1 in Fig. 4b consists of a small
number of signals with a relatively short duration (up to 200 μs) and low amplitude (up to 48 dB)
in a good agreement with the data reported for the OPC sample with encapsulated aluminium atstage 1 in Fig. 4a (Spasova, Ojovan and Scales, 2006a; Spasova and Ojovan, 2006b). This typeof AE can be associated with distributed microcracking within the aluminium corrosion products
layer (Fig. 6d) and the cement structure. The signals with a slightly higher amplitude and longer
duration, detected in a most high population at the beginning of the second stage (period 2a in
Figs. 4a and 4b) can be attributed to localised microcracking within the cement structure and theonset and/or activation of damage mechanisms such as debonding along the aluminium rod
caused by the increase of the compressive forces at the aluminium/corrosion products layer andcorrosion products layer/cement interfaces and hydrogen gas evolution. The most energetic signals detected in a high population for the OPC (Spasova and Ojovan, 2006b) and BFS/OPC samples during the second stage can be associated with the extension of the splitting cracks between
the aluminium and the corrosion products layer (Figs. 6a and 6b) and the visible radial crack(s)formation within the cement structures (Figs. 5a and 5c). It is important to note that during eachof those stages of the conducted monitoring there were signals which cannot be classified using
the criteria of the procedure proposed by Wu et al. (2000). These events were less frequent andwere characterised by a higher amplitude, average ABS energy (EABS,av) and a longer duration incomparison with the classified dominant populations of recorded signals (Table 1) and indicate
for the presence of damage (observed at the end of the test) within the cementitious structures.

59

Table 1. Parameters of the signals recorded during the AE monitoring of the BFS/OPC sample
with encapsulated aluminium.
Time
Period0 – 92 h(1)

Hits Duration, [s]Amplitude
Peak,
[dB]

Amplitude
Range,[dB]

0 - 5051 - 200

41
43* Not determined

40 - 42
40 - 48

Max ofCounts/Hits
1
4

42 - 54*10

201 - 1993

Calculated
AverageCounts/Hits

Nhits

Ncounts

% of
Total
Nhits

Total
EABS,
[aJ]

EABS,av ,
[aJ]

1.25
3.92

92
88

115
345

3.95
3.78

4120
11100

35.82
32.17

*19.3

13

251

0.56

68700

5284.6

193

711

8.29

83920

5353.59

1.14
2.07
4.76

740
590
634

841
1222
3018

31.79
25.34
27.23

28200
40900
12400038.1
69.32
195.98*91.55

92

8423

3.95

2240002434.7

2056

88.31

4171002738.1

0.95
1.84

917
24300

41.68
565.11

2.79
0.34
0.25

25217
235
378

606.79
29.37
63

Total
92 – 142h(2a)

0 - 2021 - 80
81 - 1000
1001 10136

41
42
43
*Not determined

40 - 46
40 - 46
40 - 55
40 - 6412
4*Not
determined

0 - 2021 - 601

4143

40 - 45
41 - 53

1
3

1.13
*5.6

22
43

1350
4
25
241

0 - 2021 - 100

40
42

40 - 4242 - 44

1
2

Total
1
1.83

65
8
6

266
8
11

Total
142 – 168
h
(2b)
168 –
183.5 h
(3)

14
19
0.59
613
Total
* The criteria for identification of an internal mechanism initiated AE from Wu et al. (2000) are not satisfied

92.37

The presented results confirm that the parameter-based approach can provide a good AE description and differentiation of the mechanisms causing damage in the cementitious samples dueto the corrosion of encapsulated aluminium. As there is a strong dependence of the measured
acoustic parameters on the experimental setup (quality of the sensor coupling, location and orientation) a signal-based analysis was also applied additional results more independent from the experimental setup. To that purpose conventional fast Fourier transformation (FFT) and wavelet
transformation (WT) were used.
Frequency characteristics of the main populations of AE signals recorded from the cementitious
samples with encapsulated aluminium
The signal-based AE analysis includes advanced mathematical transforms to facilitate theidentification of characteristic features of the generated acoustic signals. Conventional FFT(McGillem and Cooper, 1984) is typically applied to determine the frequency spectra of the detected acoustic waves. The primary frequency, defined as the most energetic frequency component in the acoustic signal, has been used as a “fingerprint” of different AE sources in concrete
structures under loading (Yoon et al., 2003) or corrosion of metals such as stainless steel (Yonezu et al., 2006). However, the FFT is of a fixed resolution at all frequencies and fails to detect
small, high frequency events, which usually last for a very short interval of time (tens of μs).
These events can be characterised by WT analysis. WT is a relatively new signal processing
technique applied for time-frequency analysis of AE signals. Since AE is not a stationary process, i.e., typically more than one wave mode is present within the signal, WT can be helpful tocharacterise the processes, causing AE, in the heterogeneous cement-based structures used in thisstudy.

60

Continuous WT (CWT) is defined as a set of basic functions obtained by compression/dilation or shifting of a “mother wavelet”. CWT of a function f(t) in time t is defined as(Ifeachor and Jervis, 2002):
+

f


W

*
( a,b ) =  f (t ) a,b(t ) dt(2)1
 ( (t – a) / b ) , and  * ( t ) is complex conjugate of the mother functionb (t ). The two terms, a and b, are known respectively as translation and scale parameters. The
translation, a, defines the shift of the time window through the signal. The scale b in WT is a parameter higher for low frequencies, which correspond to global information of the signal andlower for high frequencies that can provide more details for mechanical events, which usually
last very short time. Different mother functions  (t ) have been studied and applied for analysis
of complex signals such as AE transients. In this study Gabor analyzing wavelet was used. Gabormother wavelet  (t ) is given as (Suzuki et al., 1996):

where  a,b ( t ) = (t ) = 

1/ 4

p
  

1/22

t2   p
exp  + i pt 2  


(3)

where  p is the centre frequency and  is a constant calculated as  =  (2 / ln 2 )1/ 2= 5.336 .

The WT of the detected acoustic waves for the cementitious samples was calculated andvisulalised with AGU-Vallen Wavelet freeware (Vallen, 2005) based on Gabor mother wavelet
with key parameters being: maximum frequency 400 kHz, frequency resolution 1 kHz and wavelet size of 600 samples. The results from WT were illustrated in a 3D plot where x-axis corresponds to the time, y-axis to the frequency and z-axis to the calculated coefficients of  (t ) for
given values of a, b and  p . Equation (2) written for a continuous function f(t) has been applied
for the time series describing the acoustic waves via a built-in algorithm to limit the integrationin the interval between 0 and Nt , where N is the number of discrete points (3072 for thisstudy), t is the sampling interval and limited range of values given for the shift a and the scaleb (Hayashi et al., 1999; Suzuki et al., 1996).
Figure 7 illustrates typical acoustic waveforms and their frequency spectrum and 3D plot of
WT from the largest population of signals with a duration up to 80 μs and an amplitude up to 46
dB recorded during stage 2 in Fig. 4 respectively for the OPC (Figs 7a, 7b and 7c) and the
BFS/OPC sample with encapsulated aluminium (Figs 7d, 7e and 7f). The plots in Fig. 7 showthat the signals detected from both OPC and BFS/OPC structures were characterised by frequency spectrum with a peak at 34 kHz but also energy in the frequency band above 100 kHz.
Moreover, the 3D plots of WT (Figs 7c and 7f) confirm the presence of two main frequency
modes, below 40 kHz and above 100 kHz, constituting the first 100 μs of the signals that can bedirectly related to the initial features of the AE sources. As stated by Grosse et al. (2003) after a
few oscillations the signals are dominated by side reflections or geometry, heterogeneity of thematerials under study, the intrinsic properties of the sensors such as resonance frequency or quality of the sensor coupling. For the more energetic signals (with a duration above 81 s and an
amplitude up to 64 dB) also recorded in a high population during stage 2 (Fig. 4) the frequency
spectrum was dominated by the low frequency component at 34 kHz (Figs. 8c and 8f). Similar
resonance-like signals were attributed to hydrogen gas evolution during monitoring of stainlesssteel corrosion (Fregonese et al., 2001; Yonezu et al., 2006).
61

Fig. 7. Typical AE waveforms, their amplitude spectrum by FFT, and 3D plot (inserted) of WT
of a short duration (up to 80 μs) signals detected for the OPC (a, b and c) and BFS/OPC (d, e andf) samples with encapsulated aluminium during stage 2.
Cross-correlation
In order to extend the AE data analysis for characterising and differentiating the potential
sources of AE within the cementitious structures the similarity in the recorded signals was assessed using the maximum value of the calculated cross-correlation function. It is known that theshape of the acquired signals in time domain is influenced by the type of the AE source, thepropagation path and the intrinsic properties of the sensors used (Kurz et al., 2004; Ono, 2006).
Although the frequency response of the AE transducer “masks” the true propagation characteristics of the wave the same type of sensor, applied for monitoring, assures that any changes in the62

Fig. 8. Typical AE waveforms, their amplitude spectrum by FFT, and 3D plot (inserted) of WT
of the more energetic resonance-like signals detected for the OPC (a, b and c) and BFS/OPC (d,e and f) samples with encapsulated aluminium during stage 2.
shape of the recorded signals characterises AE sources with a particular location and activity intime and space. During the conducted AE monitoring of the cementitious samples the sensors
were firmly attached to the plastic containers and therefore the shape of the signals detected was
influenced by the source-time characteristics, the wave propagation path or the distance and orientation of the source to the sensor.
The cross-correlation is a mathematical operation between two series of samples (or two digitized signals) to produce a third signal called the cross-correlation of the two input signals. Each
63

Sample ƒxy[m] of the third signal is calculated from the second input signal y[n+m], shifted by mwith respect to the first one x[n], according to the equation:
fxy [ m ] =N  m 1

 x[n]y[n + m]

(4)

n=0

where m = 1, 2 …2N–1, and N is the number of samples describing the signal in digital form.
The amplitude of each sample in the cross-correlation signal is a measure of how much the second input signal resembles the first one at that location. The value of the cross-correlation is a
maximum when the second signal is “aligned” to the first signal.
In this study the cross-correlation function was calculated between each two subsequent intime signals recorded by the sensors attached to the samples, i.e., between the first and the second, second and third and etc. The plots in Fig. 9 show the maximum value of the calculated
cross correlation functions for the AE activity for the OPC and BFS/OPC samples using a program written in MatLab.

Fig. 9. Maximum value of the cross-correlation function calculated for each two subsequent intime signals recorded by the sensors attached to (a) the OPC and (b) BFS/OPC samples with encapsulated aluminium.
As it can be seen from the plot in Fig. 9a, the signals did not remain identical throughout the
test. It can be suggested that they were generated by sources with a different location (toward thesensor) and time activity. The same implies for the signals detected for the BFS/OPC sample
with encapsulated aluminium, as evidenced from the plot in Fig. 9b. Nevertheless, significantly
larger number of subsequent in time signals recorded for the composite cement specimen wereidentical (maximum cross-correlation function equal to 1) in comparison with those for the OPC
sample. The latter can be used to conclude on more localized (in space and time) AE sources,
detected under the established experimental conditions for the BFS/OPC sample with encapsulated aluminium.
Discussion
A large number of signals for the monitored OPC (5340) and BFS/OPC (2328) samples
were recorded during clearly defined periods with a very low or almost none AE activity

64

followed by abrupt jumps in the detected number of hits. This behaviour of the samples (in termsof AE) resembled the response of cement-based structures under loading associated with micro
and macro-scale damage (Landis and Ballon, 2002; Landis, 1999; Uddin et al., 2006; Yoon et al.,2003). Moreover, the intervals of AE “silence”, recognised in the history of the conducted AE
monitoring, can be related to Kaiser effect (Kaiser, 1953). Thus in the monitored cementitious
specimens the stresses induced by further accumulation of corrosion products and hydrogen gas
generation caused again AE after a period of time, when the initial loading (suggested by the periods with a high AE hit rate) has been exceeded. The applied conventional parameter-based
analysis using the procedure from Wu et al. (2000) for classification of the detected signals according to their duration, amplitude and counts provided the first point to conclude the complex
AE nature and activity of a large number of processes (sources) later confirmed by visual andoptical microscopy observations.
According to their time domain parameters recorded signals can be divided generally in two
main groups. The first group consists of a large number of hits with duration up to 80 μs and an
amplitude up to 46 dB potentially associated with microcracks initiation and propagation. The
second group consists of a lesser number of signals with amplitude above 46 dB and duration
longer than 80 μs that can be associated with hydrogen gas evolution due to the aluminium corrosion and critical size crack(s) formation and propagation within the cementitious structures.
Moreover, this group of hits was distinguished by resonance-like signals with a primary frequency at 34 kHz whereas the first one was characterised by signals with two frequency modes
present: below 40 kHz and above 100 kHz respectively.
It is worth noting that under similar experimental conditions the recorded AE signals for the
BFS/OPC sample were significantly less than those recorded for the OPC sample, being nearly
half of that for the OPC structure with encapsulated aluminium for more than three times longer
period of monitoring. The latter is in a good agreement with the reported by Setiadi (2006)
lower rate of aluminum corrosion in BFS/OPC than in pure OPC environment. Additional evidence for the decreased rate of the aluminium corrosion and therefore less damage developed
within the BFS/OPC sample prior to this test is also the intensity of the AE generation and release calculated for the same stages 2 in Fig. 4 for the OPC and BFS/OPC samples to be respectively of 335 hits/h and only 41.1 hits/h.
However, the basic parameters and frequency characteristics of the detected signals for bothcementitious samples did not reveal any important differences. The maximum of the calculated
cross-correlation function between each two subsequent signals recorded by the sensors showedthat the potential AE sources in both cementitious structures were with different location (toward
the sensor) and time activity throughout the tests.
Conclusions
The AE technique is feasible for monitoring the mechanical performance of cementitious
structures with encapsulated metallic wastes such as aluminium. The methodology for analysis
performed on the AE hit rate, their parameters and frequency characteristics allows conclusions
to be reached on the presence of different AE sources in the cementitious wasteform samples
with encapsulated aluminium using additional visual and optical microscopy observations. An
attempt to differentiate damage mechanisms such as distributed and localised microcracking, hydrogen gas evolution and visible cracks formation and extension within the cementitious wasteforms was made based on signals duration, amplitude and frequency components. Although the65AE hit rate and time distribution for the OPC and BFS/OPC samples with encapsulated aluminium were different under similar experimental conditions the recorded signals have been successfully classified using similar parameters and frequency characteristics.
Acknowledgements
This work was possible with the financial support of EPSRC and Nexia Solution Ltd.
Authors are grateful for advises to M. Hayes and Dr. A. Setiadi from AMEC, H. Godfrey from
Nexia Solutions Ltd, Dr. N. Milestone, Dr. R. Hand and Prof. F.G.F. Gibb from Immobilisation
Science Laboratory, University of Sheffield.
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68

EVALUATION OF REPAIR EFFECT FOR DETERIORATED CONCRETE
PIERS OF INTAKE DAM USING AE ACTIVITYTOMOKI SHIOTANI and DIMITRIOS G. AGGELIS*
Department of Urban Management, Graduate School of Engineering, Kyoto University,
C1-2-236, Kyoto-Daigaku-Katsura, Nishikyo, Kyoto 615-8540, Japan; *Research Institute ofTechnology, Tobishima Corporation, 5472 Kimagase, Noda, Chiba, 270-0222, Japan

Abstract
Deteriorated concrete structures are repaired by means of grouting. In order to evaluate therepair effectiveness, the application of two NDT techniques, namely seismic tomography andacoustic emission is described herein. Using the traveling time of elastic waves, the structural
velocity is estimated. AE activity is monitored along with water pressurization of the permeability test. As a result, contrary to the common expectation that the velocity increases after fillingwith grouting agent, enormous amount of velocity drop was observed after repair, due to the incomplete hydration process of grout material and the resulting property mis-match. On the other
hand, AE activity showed dramatic decrease after repair. Furthermore, the results are correlated
to the quantity of grouting material injected, showing that this was the source of the velocity behavior, while it became clear that damage indices based on AE activity exhibited well the actual
damage of concrete structures.
Keywords: Damage indices, deterioration, concrete structures, tomography, repair effectIntroduction
The present paper reports on the repair evaluation of a water intake diversion facility that wasconstructed 70 years ago in a cold region of Japan. The concrete piers, which support the rollinggate, the most crucial part of the facility, exhibited deterioration. Referring to the repair record of
the facility, both sides of the pier were replaced to the depth of 20 cm with new concrete 20 years
ago, since macroscopic surface cracks developed resulting from freezing and thawing. Recently,
the surface degradation became critical again; therefore damage investigation was performed bymeans of core sampling. Two seriously damaged zones were observed: at the boundary between
the old and the replaced concrete, and in the deep, internal area of the pier owing to a large
amount of voids, pieces of wood and cobblestone. Grouting with injection cement was used for
repair and the present paper describes the applicability of NDT to evaluate the repair effectiveness.
Experimental
Repair Technique
The concrete piers described herein are referenced as Pier 1 and Pier 2. Two directions ofgrouting were used: (i) vertical grouting to fill the internal damaged areas, (ii) horizontal grouting to fill the interface between the old and replaced concrete along both sides. Specifically, avertical borehole with 50-mm diameter was drilled only for Pier 1, whereas many horizontal
boreholes with 20-mm diameter from both bank sides (only to shallow depth ranging from 800 to1000 mm) were made in both piers. The arrangement of the boreholes can be seen in Fig. 1.
J. Acoustic Emission, 25 (2007)69© 2007 Acoustic Emission GroupFig. 1 (a) Grouting maps for Pier 1; (b) Pier 2.
NDT Monitoring
In order to verify the repair effectiveness before and after repair, seismic tomography andacoustic emission (AE) were performed. Through seismic tomography, the velocity structure canbe created from P-wave traveling time. The velocity difference between before and after repair
leads to the characterization of the repair (Shiotani et al., 2005a). Using AE activity, damage canalso be quantified with indices such as RTRI and Calm ratio (Luo et al., 2004), and even in cases
when those are difficult to apply, AE peak amplitude distribution can give reasonable evaluationof the damage extent (Shiotani et al., 2005b).
Seismic Tomography
Seismic tomography needs both excitation and detection of elastic waves. They were performed with a hammer drill edged with different curvatures of 10, 20 and 30 mm in radius andpiezoelectric accelerometers (SAF51, Fuji Ceramics Corp.). The curvature was altered to examine the relation between resulted velocities and the upper range of excited elastic wave frequency(Sansalone & Street, 1997). The present paper only shows the result of 30 mm in radius (others
can be seen in Shiotani and Aggelis, 2006). Figure 2 shows the arrangement of accelerometers inPier 1 used for tomography. 16 channels of accelerometers were used, one of which acted as a
trigger sensor and 15 others as receivers. The receivers are placed in a vertical line, with equal
space of 500 mm on one lateral side, while on the opposite side excitation was performed by ahammer drill, with a near-by trigger accelerometer. The hammering was continuously excited for
10 seconds with 30 Hz repetition, resulting in about 300 waveforms. Using the 300 waveforms,
stacking was conducted to enhance the signal-to-noise ratio and to facilitate the first-arrival
picking. The traveling time was fed to a suitable tomography program (see Kobayashi et al.,2006). Using seismic tomography the velocity structure of the pier was reconstructed in two different cross-sections, namely upper current section and lower current section (see Fig. 2).
70

Fig. 2 Sensor array for tomography in Pier 1.

Fig. 3 AE sensor array in Pier 1.
71Acoustic Emission
To study if the grouting material filled the cracked or void areas, permeability tests were carried out by means of a water pump. AE monitoring was conducted during the application of water pressure. As shown in Fig. 3, 6 sensors on each side and 3 sensors on the back side were set
with 1.5-m spacing, making a total of 15 sensors. The permeability of concrete was monitored inintervals of 1 m in depth, together with AE monitoring. The AE signals detected with AE sensors(R6I, PAC) were amplified 40 dB and the signals over 40 dB were acquired by DISP AE system(PAC) to study their AE parameters as well as waveforms.

Fig. 4 Tomogram of a dam pier section examined (a) before, (b) after repair, (c) after complete
hardening of grout.
Results
Seismic Tomography
In Fig. 4(a) the tomogram of a cross section of Pier 1 before injection of grout is presented.
The general condition can be characterized satisfactory since the propagation velocity is generally higher than 3500 m/s, except in some small areas. However, two weeks after repair, the tomogram showed clearly decreased velocity, even as low as 2500 m/s (see Fig. 4(b)). This behavior was not expected since cementitious material replaced the voids. Even if the defects were
not completely eliminated, the velocity was not supposed to decrease.
Since grouted concrete is a highly heterogeneous medium, it was deemed essential to investigate stress wave scattering, which is known to produce strong dispersive effects (Tsinopoulos etal., 2000). The model studied was that of an elastic concrete matrix initially containing voids(case before repair), subsequently soft elastic scatterers (shortly after repair) and finally hardelastic scatterers (fully hardened grout pockets). The properties of concrete were derived bycores from the dam, while concerning grout, specimens of the same material were created andtested in laboratory at different ages.
With the knowledge of mechanical properties of the constituent materials, the wavenumberof the composite can be calculated using a suitable dispersion relation (Waterman & Truell,1961):
72

2

k
3
9 2
  = 1 + 2 3 f (0)+f 2 (0) f 2 ( )4 6
kc R
4k c R
 kc

[

]

(1)

where R is the scatterer radius (in this case void or grout pocket),  is the particle volume concentration, kc the wavenumber of the matrix and f(0) and f() are the forward and backward
far-field scattering amplitudes, respectively, showing what part of energy is scattered forward
and backward (Ying and Truell, 1956).
The frequency dependent phase velocity C() and attenuation () are calculated from thecomplex wavenumber of the medium:k ( ) =+ i ( )C ( )(2)

where  stands for radial frequency.
The characteristic size of the voids is not readily known. However, due to the low sonic frequencies excited at the test, wavelengths of typically 0.5 m are employed, which are certainly
larger than most cracks present in the material. Therefore, the size of scatterer is not crucial in
our investigation. Assuming for example, void size of 40 mm, and void content of 15%, which istypical in literature (Chaix et al., 2006) and frequency 10 kHz, after solution of Eq. (1), the pulse
velocity for concrete is calculated at 4300 m/s. If instead of voids, 15% of soft grout of 12 GPa isapplied (corresponding to the elasticity of grout at the age of two weeks), the velocity reduces to3850 m/s. Therefore, the scattering theory suggests that a velocity increase should not be expected immediately, but only after sufficient hardening of grout. This could be further delayed ina cold environment. For the final case of hardened grout of 22 GPa, the velocity increases to4400 m/s (Aggelis & Shiotani, 2006a, 2006b). Using Eq. (1), since the mechanical properties ofthe materials have been determined and the velocities measured, the volume fraction of voids is
calculated. This is done for each specific tomography cell, corresponding to an area of 0.5 x 0.5
m, for which velocity values have been assigned by tomography. In order to calculate the final
velocity structure, which should be anticipated after full hydration of grout, the elasticity of 22
GPa is applied to Eq. (1), along with the corresponding void volume of each cell. Therefore, thefinal velocity structure is depicted in Fig. 4(c). The average velocity is increased by approximately 100 m/s, something that was not revealed by the monitoring just after the repair. However,using scattering theory, it was possible to explain the behavior and to evaluate the final conditionof the structure.
According to the above discussion, the amount of grout is responsible for the initially observed velocity drop. This is confirmed by the correlation of the actual quantity of grout injected
at different heights in the structure, with the velocity decrease at the same areas, depicted in Fig.
5. An excellent correlation is observed, since at the positions where a lot of grout was injected(e.g. 120 kg, 9 m from the top), at the same position, a velocity decrease of more than 800 m/s
was measured.
Acoustic Emission
The source location algorithm failed to identify AE sources. Thus, the repair effect is discussed with AE hit activity. For Pier 1, Fig. 6a shows accumulated AE hits for each depth for theleft bank side (right) and rear side (left). Especially for the left bank side, total AE hits of two
allocated sensors at the same height are depicted with horizontal bars. In the chart, the injected
quantity of vertical grout is drawn as well. Before repair, AE hit activity was intensively observed particularly below –9.0 m.
73

Fig. 5 Amount of grout and velocity decrease for different heights of pier 1.

Fig. 6 AE hit activity with crack trace (Right: the left banks side, Left: rear side).
74

At these depths, a complicated and well-evolved surface crack system was observed, as wellas water leakage from some macroscopic cracks among those (see bold lines in the figure),causing a large number of AE hits. A large amount of grout was injected vertically at thosedepths, implying that the macroscopic cracks were filled with the injection cement. AE activity
after repair revealed decrease; i.e., from 5149 to 348 hits at –12 m and from 11903 to 602 hits at–9.0 m. The same trend could be found in the rear view (see the left graph in the figure) as AE
hits decreased before and after repair.
The result for Pier 2 is shown in Fig. 6b. Steep changes of AE activity before and after repair
were only derived around the middle height of monitoring range where 377 hits before repair
decreased to 57 after at -5.75 m and 320 to 75 at -7.25 m (see the right chart in Fig. 6b). No correlation between the AE activity and the quantity of injected horizontal grout was observed.

Fig. 7 AE hit activity with water pressurization process.
Hereafter, two damage indices namely Load ratio and Calm ratio are used to discuss the
structural integrity. These indices are calculated with respect to a mechanical measurement in the
structure. In this case, the water pressure during the permeability test was used. Load ratio was
obtained based on the value of water pressure that was accompanied by the first AE burst, divided by the maximum pressure during the experiment. Calm ratio was calculated from AE hits
during the unloading (reduction of water pressure) divided by the hits during the whole processof pressurization (JSNDI, 2000). Briefly, a healthier state is indicated by large Load ratios andlow values of Calm ratio. Figure 7a shows the cumulative AE hits during the permeability test at-4 to -5 m in depth (cf. at -4.5 m in Fig. 6). For simplicity water pressure is normalized to its
maximum value (0.5 MPa) on the horizontal axis, i.e. the Load ratio can be read from the horizontal value showing the onset of AE activity. In the case of Pier 1 before repair, AE started witha slight pressure of 6.2%, while after repair the onset of AE hits shifted to the larger value of
25.2%. In Pier 2, however, no remarkable change was obtained even after repair i.e., 25.5% before, to 29.5% after repair. These results lead to two important conclusions: (i) P1 was damaged
but sufficiently reinforced. (ii) P2 was in acceptable damage state even before repair and a slightrecovery was obtained afterward.
In Fig. 8a, AE activity of Pier 1 during both processes of water pressurization and its reduction is shown. Since the resulted Calm ratio of 0.11 before, increased to 0.35 after repair, recovery due to repair could not be assumed by the Calm ratio. For Pier 2, improvement after repair
could not be obtained from Calm ratio, either (see Fig. 8b). However, the essential reduction of
AE activity indicated simultaneous reduction of active sources.
75

Fig. 8 AE hits activity during both of water pressurization and its reduction processes.

Fig. 9 Calm ratio and Load ratio based on ringdown count.
All derived ratios from both piers are plotted in Fig. 9. The small dots correspond to verticalzones of 1 m and are distributed over the chart; however, from the average of Load ratio a large
difference appeared in Pier 1 between before and after, while only a slight difference was obtained in Pier 2. The figure also suggests that Calm ratio seems less sensitive to repair effect than
Load ratio.
Discussion
Repair Effect from AE Hit Activity
From the AE activity of Pier 1, a large decrease of AE hits was found in deeper areas, and in
those areas considerable quantity of grout was injected; For Pier 2, although the AE hits tended
to decrease after repair, the decrease of hits was not as large as in Pier 1, and the quantity ofgrout did not correlate to the variation of AE activity, either. Here, it should be reminded thatgrouting in two directions was performed in Pier 1, whereas only in the horizontal direction forPier 2. Furthermore the vertical grouting employed borehole of larger diameter than that of thehorizontal, resulting in increased quantity of injected grout in Pier 1 (see the maximum value in
Fig. 6) in comparison with Pier 2. Those all attributed to the difference in AE activity between
Pier 1 and Pier 2, suggesting greater repair effect in Pier 1 than in Pier 2.
76

The number of AE hits after repair should also be discussed. Specifically in Pier 1, a steep
change was obtained in the number of AE after repair, which stood at 602 and 348 at –9.0 and–12.0 m, respectively, while Pier 2 showed 133 hits at the maximum. This implies that although
recovery rate was estimated to be small for Pier 2, it is healthier than Pier 1. Repair effect/recovery rate, and present health status should thus be considered from different points ofview.
Repair Effect from AE Related Damage Indices
The above assumption can also be followed by AE related damage indices. As shown in Fig.
9, a large amount of increase in Load ratio was obtained in Pier 1 while only a slight increase inPier 2. Thus, the recovery effect due to repair was obviously superior for Pier 1 than for Pier 2.
However, from the point of view of soundness, Pier 2 was significantly healthier even from theoriginal state (see the average before repair in Pier 2) and remained so despite the high recovery
of Pier 1.
Quantification of Recovery Rate
Using AE related damage indices, current damage status of structures can be evaluated as
described above; however, recovery rate, suggesting how much degree the structures were repaired, could not be quantified. Since both Calm ratio and Load ratio are relative values, normalized by an accumulated number of AE activity during the whole loading cycle or by the
maximum value of referred parameter like pressure, respectively, these indices do not take into
account substantial differences in the obtained number of AE hits before and after repair. As canbe found in Fig. 8b and Fig. 9b, the number of AE hits decreased after repair, while the Calmratio remained the same value even after repair as before. Therefore, we propose the recovery
rate considering the numbers of AE hits obtained before and after repair.

Fig. 10 Conceptual illustration of improved Load (i-Load) ratio.
Specifically for the recovery effect, Load and Calm ratios are modified based on the AE activity observed. The idea is demonstrated in Fig. 10 for the modification of Load ratio. Here, twotypical AE hits vs. load curves (before and after repair) are shown. Conventional Load ratios aredefined by dividing Pb, or Pa values (before and after repair) with the previous maximum load,
Pmax. Instead, we introduce the threshold value (in percent ) to determine the onset of cumulative AE curve. Suppose total AE hits before repair, TAEb, is 100 and we set  to be 10%. Thus,
the threshold hit value or TAEt is 10. This defines PAEb for AE hits vs. load curve before repair.
77

After the repair, we use the same threshold value as before, i.e., 10 in this case and we obtain
PAEa. By dividing PAEb and PAEa with the previous maximum load, Pmax, we define improved
Load ratios or “i-Load” ratios. We employ an identical condition for judging the onset of AE
curves for before and after repair.
For modification of Calm ratio, we multiply a conventional Calm ratio with the ratio of cumulative AE activity after repair to that before repair. This allows us to factor in the variation of
AE hits before and after. This new index is named improved Calm ratio or “i-Calm” ratio.
In reference to the AE activity data used in Fig. 9 and setting  as 10%, the i-Load andi-Calm ratios are obtained as shown in Fig. 11. In pier 1, the average coordinate shifted from (0.4,
0.33) before repair to the right bottom on the chart at (0.68, 0.03). This shift clearly indicates ahigh recovery rate after repair. Recovery can be also found in Pier 2 from (0.4, 0.4) to (0.52,
0.28), but not so dramatic as in Pier 1. As shown in Fig. 9, Calm ratio did not reflect the repaired
condition effectively, as almost identical values were obtained before and after. By considering
the number of AE hits, we can account for the recovery of the structure due to repair with the useof i-Calm ratio. It is noted that the use of  = 5% results in the same trend as in 10%, suggesting
the proper range of  in this case being 5 to 10%; however, to determine the appropriate value
for other structures, further studies are necessary.

Fig. 11 Improved Calm (i-Calm) ratio and improved Load (i-Load) ratio to assess the recovery
rate.
Conclusion
In this paper, two NDT techniques, namely seismic tomography and AE technique, are applied in order to evaluate the improvement after repair in severely deteriorated large concrete
piers. Pier 1 was repaired well with a large amount of grout injection in the vertical and lateral
direction, while a less volume of grout was filled in Pier 2. Contrary to the general assumption, asubstantial velocity decrease was found in Pier 1, owing to the resultant low mechanical properties of the freshly injected grout that is assisted by scattering effect. With hardened grout properties, velocity values are further estimated, resulting in velocity increases. Discrepancies of AE
activity before and after repair indicated the repair effectiveness, specifically with employing
78

damage indices of Calm and Load ratios. Additionally in order to quantify the recovery rate improved Calm and improved Load ratios are newly defined, with the consideration of the numberof AE hits, and a lowered AE activity in the structure due to repair.
References
Aggelis, D. G. and Shiotani, T. (2006a), “Wave behavior of concrete structures repaired by cement injection“, submitted.
Aggelis, D. G. and Shiotani, T. (2006b), “Damage Evaluation on Concrete Materials Using Characteristics of Elastic waves”, Proc. of Structural Faults and Repair 2006, Edinburgh, 13-15 June,(in CD).
Chaix, J. F., Garnier, V., and Corneloup, G. (2006), “Ultrasonic wave propagation in heterogeneous solid media: Theoretical analysis and experimental validation”, Ultrasonics, 44, 200-210.
Kobayashi, Y., Shiojiri, H. and Shiotani, T. (2006), “Damage Identification Using Seismic Travel Time Tomography on the Basis of Evolutional Wave Velocity Distribution Model”, Proc. of
Structural Faults and Repair 2006, Edinburgh, 13-15 June, (in CD).
Luo, X., Haya, H., Inaba, T., Shiotani, T., and Nakanishi, Y. (2004), “Damage Evaluation ofRailway Structures by Using Train-Induced AE”, Construction and Building Materials, 18(3),215-223.
M. J. Sansalone, and W. B. Streett (1997), Impact-Echo, Nondestructive Evaluation of Concreteand Masonry, Bullbrier Press, Ithaca, N.Y.
Shiotani, T., Nakanishi, Y., Iwaki, K., Luo, X. and Haya, H. (2005a), “Evaluation of Reinforcement in Damaged Railway Concrete Piers by Means of Acoustic Emission”, Journal of Acoustic
Emission, 23, 260-271.
Shiotani, T., Luo, X., Haya, H. and Ohtsu, M. (2005b), “Damage Quantification for Concrete
Structures by Improved b-Value Analysis of AE”, 11th International Conference on Fracture,
CD-ROM.
Shiotani, T. and Aggelis, D. G. (2006), “Damage Quantification of Aging Concrete Structures by
Means of NDT”, Proc. of Structural Faults and Repair 2006, Edinburgh, 13-15 June, (in CD).
The Japanese Society for Non-Destructive Inspection (2000), “Recommended Practice for In
Situ Monitoring of Concrete Structures by Acoustic Emission”, NDIS2421. JSNDI, Japan.
Tsinopoulos, S. V., Verbis, J. T., Polyzos, D. (2000), “An iterative effective medium approximation for wave dispersion and attenuation predictions in particulate composites”, Adv. Composite
Lett., 9, 193–200.
Waterman, P.C., Truell, R. (1961), “Multiple scattering of waves“, J. Math. Phys., 2, 512–537.
Ying, C.F., Truell, R. (1956), “Scattering of a plane longitudinal wave by a spherical obstacle in
an isotropically elastic solid“, J. Appl. Phys. 27, 1086–1097.

79

ACOUSTIC EMISSION MONITORING OF FLEXURALLY LOADEDARAMID/EPOXY COMPOSITES BY EMBEDDED PVDF SENSORSCLAUDIO CANEVA, IGOR MARIA DE ROSA and FABRIZIO SARASINI
Department of Chemical and Materials Engineering, University of Rome,“La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy

Abstract
Cost-effective and reliable damage detection is crucial for the use of composite materials dueto the relatively large number of failure mechanisms they can experience in service. Of themethods available, acoustic emission (AE) is considered as one of the most effective for on-line
and in situ evaluation of structural health of composites. Thus, AE generated during three-point
bending of aramid-reinforced epoxy composites was monitored by embedded and surfacemounted polyvinylidene-fluoride (PVDF) thin-film sensors instead of traditional piezoceramic
ones. The parametric analysis of the AE signals detected and scanning electron microscopy observations enabled to verify the suitability of these sensors to be embedded as well as to characterize the failure mechanisms of the specimens tested. This work confirms the use of AE as anadvanced, cost-effective, sensitive technique for detecting and locating potential damage mechanisms in composite materials.
Keywords: PVDF sensors, aramid-epoxy composites, three-point bending, structural healthmonitoring.
Introduction
Woven fabric reinforced composites are among the most important and widely used forms oftextile structural composites due to their cost-effectiveness, high processability and enhanced outof plane mechanical properties. The increased use of such composites in the industry, from aerospace to construction, has required suitable and reliable testing procedures. Of primary importance in developing such procedures is the detection of damage initiation, progression and failuremodes during loading of composites in real time. A host of nondestructive test methods havebeen developed to detect defects and damage in composite materials but AE monitoring is theprimary technique used for the identification of different types of failure in composites for inservice monitoring because it is a passive method, which only requires a network of receivers(Hamstad and Sendeckyj, 1993). In this regard, AE monitoring has some unique advantages overother NDT techniques: can detect the dynamic processes associated with the degradation of
structural integrity, it is nondirectional and can be used in situ to monitor a structure while it remains in service. This feature makes AE a suitable sensing technique for structural health monitoring (SHM) (Speckmann and Henrich, 2004). Other unique capabilities include: high sensitivity, location of damage regions and sensitivity to any process that generates stress waves.
The solution analyzed in this work involved the use of polyvinylidene fluoride as sensor’s
material and the AE as sensing technique. In particular, to fulfill the task concerning the in situ
damage detection, the AE sensors have been embedded in the composite laminates during their
manufacturing stage.

J. Acoustic Emission, 25 (2007)80© 2007 Acoustic Emission GroupPVDF is a semicrystalline polymer, which owes its strong piezoelectricity to the strength ofthe electric dipole (between carbon and fluorine atoms) and to the large number of dipoles perunit volume, but most importantly because it crystallizes in a polar crystal phase characterized bya cooperative dipole alignment (Davis, 1987). PVDF differs in many respects from traditionalpiezoceramic materials. Ceramic transducers are in general brittle, stiff, easily damaged frommechanical shock or vibration and are difficult to machine in complex shapes. In contrast, polymeric transducers can be easily tailored to different shapes, are tough, flexible when machined
into thin films thus not affecting the mechanical motion of the structures they are mounted on.
Nevertheless, polymeric transducers can undergo mechanical relaxation phenomena and areprone to thermal depolarization and electromagnetic interference. At present, some of the mostimportant applications of PVDF can be considered to involve pyroelectric, electromechanicaland electroacoustic transduction (Sessler, 1981; Marcus, 1982; Bar-Cohen et al., 1996; Riande
and Diaz-Calleja, 2004; Measurement Specialties, 2006).
Several papers are available in literature dealing with the use of PVDF as AE sensor (Stifflerand Henneke, 1983; Narisawa and Oba, 1984, 1985; Hamstad, 1995; Or et al., 2000; Kim et al.,2005; Park et al., 2005; Bar et al., 2004, 2005) even though as surface mounted AE sensors.
The aim of this work is to verify the suitability of PVDF sensors to be embedded and their influence on the flexural behavior and integrity of the resulting composite laminates. Furthermore,
the AE monitoring of composites during loading and the characterization of AE signals acquired
were addressed to identify the failure mechanisms involved.
Materials
The specimens tested in this work were obtained from square aramid/epoxy panels (250 x
250 mm) made using RA175H4 fabric and a SP106 resin, both from SP Systems. The areal
weight of the fabric, a 4-harness satin weave containing Kevlar49 fibers, was 170 g/m2. The
samples were manufactured by hand lay-up in a closed mould using ten layers of fabric. This
manufacturing technique made the embedment of PVDF sensors easier. In fact, during the layer
stacking process, PVDF sensors were arranged in the middle of the stacking sequence of thelaminate. The thickness of the cured laminates was 3 ± 0.2 mm and the fiber volume fraction was
0.38 ± 0.02 (as calculated from the perform weight per unit area). From the panels, rectangularspecimens having a length of 200 ± 5 mm and a width of 30 ± 0.5 mm were removed and subjected to three-point bending tests. Two different types of specimens were produced: the first
type (labeled as type A) had no embedded sensors (served as reference material) whilst the second one (labeled as type B) had two embedded sensors.
PVDF sensors used in this work were the DT1-052K manufactured by Measurement Specialties Inc. These sensors are rectangular element piezo-film with silver ink screen printed electrodes and are supplied with a thin urethane coating over the active sensor area; the lead attachment legs are free of this coating (Measurement Specialties, 2006). In Table 1 are reported sometypical properties of the piezo-film and the geometrical dimensions of the sensors used in thiswork (Measurement Specialties, 2006). The PVDF sensors for type A specimens were surface
mounted using a double-side tape. A reliable interconnection to piezo-film was made through theuse of a conductive silver epoxy resin manufactured by ITW Chemtronics (CW 2400), which
provided not only excellent electrical conductivity but also high-strength bonding.

81

Table 1. Typical properties of piezo film and dimensions of PVDF sensors used.
Symbol
d31
d33
g31
g33
k31
kt
Y
p

/0
m
T
PVDF Sensor
DT1-052K

Parameter
Piezo Strain ConstantPiezo Strain ConstantPiezo Stress ConstantPiezo Stress ConstantElectromechanical Coupling Factor
Electromechanical Coupling Factor
Young’s Modulus
Pyroelectric coefficient
Permittivity
Relative Permittivity
Mass Density
Temperature Range
Length (mm)
41

PVDF
23
-33
216
-330

Units
-12
10 (m/m)/(V/m)-1210 (m/m)/(V/m)-32
10 (V/m)/(N/m )-32
10 (V/m)/(N/m )

12%
14%
2-4
30
106-11312-13
1.78-40 to 80...100
Width (mm)
16

9

2

3

3

10 N/m-6
2
10 C/m K-12
10 F/m
10 kg/m°C
Thickness (μm)
70

Methods
Before performing flexural tests, the wave velocity of AE signals in the laminates was measured in order to make possible the localization of AE events during the tests. The procedure used
is described elsewhere (Caneva et al., 2007). The value obtained was 4600 m/s, which should beconsidered as an approximate value due to the highly anisotropic nature of composite materials.
Its accuracy was checked by means of calibration tests through the Hsu-Nielsen source method(Caneva et al., 2007) providing satisfactory results.

Fig. 1. SEM micrograph showing the PVDF
sensor embedded in aramid reinforced epoxy.

Fig. 2. SEM micrograph showing details of
the sensor/matrix interface of an aramid reinforced laminate82

After these preliminary tests, the specimens were subjected to three-point bending tests in accordance with ASTM D-790 (three-point loading). These tests were performed in an Instron5584 at a constant crosshead speed of 2.5 mm/min with a span-to-thickness ratio of 20:1. The
strain at the mid-span was determined by means of strain gauges. In order to embed PVDF sensors, width and length ratios were selected to limit edge and large inclusion effects. The PVDF
sensors were located within the span length with a center-to-center distance of about 60 mm. The
position of sensors was selected in order to have them close to the stressed area during bending
with the aim of enabling the detection of AE signals as well as verifying their influence on themechanical response of the composite laminates. Ten specimens were tested for each composite
system (types A and B). The tests were monitored by two PVDF sensors and the signals were acquired by an AMSY-5 AE system manufactured by Vallen Systeme GmbH. The AE acquisition
settings were as follows: threshold = 40 dB (ref. 0 dB = 1 V), rearm time = 0.4 ms, duration
discrimination time = 0.2 ms and total gain = 34 dB. Attenuation of AE waves was measured using the Hsu-Nielsen source procedure. It was small enough to prevent from doing any correctionof the measured amplitudes owing to the small distance between the sensors. In linear location
calculations, the position of the sensor is assumed coincident with the center of the sensor itself.
Only AE signals localized between the sensors have been considered in the off-line parametricanalysis.
The failure modes and microstructures in the composites were investigated with scanningelectron microscopy (SEM).
Results and Discussion
A major concern during the laminate cure cycle was with the health of the embedded sensors.
The maximum temperature of 75°C during fabrication of laminates was very close to the Curie
transition temperature for PVDF sensors, which could result in depoling associated with a sharp
decrease of piezoelectric properties. In addition, residual stresses at the matrix/sensor interface
could affect the ability of the sensor to detect AE events. To investigate these aspects, SEM
analysis was performed on composite laminates. The results of this investigation are summarized
in the following micrographs (Figs. 1-3), which refer to the cross section of the specimens. The
cross sections were graphite coated before SEM observations.
As for embedded PVDF sensors, as it can be seen in Figs. 1-3, the interface between the sensor and the surrounding matrix represents a region of continuity between the different materials
and does not seem to be present any kind of remarkable damage able to affect in a negative way
the piezoelectric behavior of the sensor itself. In fact, especially from Fig. 3, there are no defects
such as microcracks due to different thermal expansion coefficients of the metal and the polymer
at the interface and the metallization adheres well to the matrix, thus enhancing the ability of thematrix to transfer the AE signals to the sensor. These observations confirm the ability and theeasiness of PVDF sensors to be embedded.
Even though the future of smart structure technology seems promising, its application to engineering systems cannot be accomplished until its integrity issues are fully understood. As sensors are integrated into complex structures, questions arise on the mechanical behavior and integrity of the structure. Therefore, the first step in the development of a reliable damage detection
system based on AE technique has to be an investigation of the mechanical properties of the resulting structures.

83

Fig. 3. SEM micrograph showing detailsof the PVDF sensor metallization.

Fig. 4. Typical longitudinal flexural load-displacement curves for specimen types A and B.

A comparison between typical flexural load-displacement curves of A and B specimens isshown in Fig. 4. As it can be noticed, the behavior of specimens with and without embedded
PVDF sensors is almost identical. The failure of aramid-reinforced epoxy composites in threepoint bending can occur by either one of two modes being failure by longitudinal fracture of fibers in the tensile side or by shear delamination. The transition between these failure modes iscontrolled by the fiber volume fraction (Davidovitz et al., 1984). The curve of the fiber fracturemode is characterized by a yielding stage and a longer ultimate deflection whilst the curve fordelamination mode is characterized by a main delamination event followed by a number of secondary delamination. Furthermore, the span length was long enough so that the specimen does
not fail in shear and short enough to neglect the effects of large deformations. The composite
specimens tested exhibited curves characterized by a yielding stage and a long ultimate deflection, during which the composites failed gradually, thus indicating an increased energy absorption and a good damage tolerance. The flexure curve is strongly non-linear and this behavior isconsistent with the non-linear compressive behavior of composites reinforced with aramid fibers(Zweben, 1978; Mittelman and Roman, 1990).
In this case, it can be seen that the failure of both sides, namely tension and compression,
may occur during flexural loading. This is due to the fact that Kevlar fibers are characterized bypoor transverse and shear properties thus providing a composite, which shows poor strength incompression. This aspect can be enhanced by fiber crimps, which tend to lower the composite
compressive strength since they make easier the appearance of kinking. This is not properly thecase because in satin-woven fabrics, the crimp ratio is generally smaller if compared, for instance, with plain-woven fabrics. Nevertheless, some kink bands appeared in the specimens
tested, as it will be explained later but the catastrophic failure was localized in the outer ply on
the tensile side. The interface plays an important role in the behavior of these composites. For
aramid/epoxy composites, the interface is thought to be a particularly weak link (Penn and Liao,
1984).
As regards the effect of PVDF sensors on the mechanical properties of composites, the two
specimen types exhibited a similar three-point bending strength and flexural modulus (within 3% of each other in both cases). Therefore, from the results it can be inferred that the embedment
84

of PVDF sensors does not seem to have any remarkable influence on the damage evolution during three-point bending (Figs. 5-6) in spite of the test conditions’ severity, since the sensor embedded takes up most of the specimen’s cross section.

Fig. 5. Average three-point bending strengthfor specimen types A and B.

Fig. 6. Average three-point bending modulus
for specimen types A and B.

Final failure of composites is considered to be due to the accumulation of different failuremodes such as matrix cracking, debonding and fiber breakage. The peak-amplitude distribution(PAD) of AE signals can be successfully used to characterize the different damage mechanisms
in composite materials since it depends strongly on the material as well as on the deformation
mechanism present thus providing insight into how the structure behaves under a given loading
condition. In this work, AE events generated during three-point bending were recorded until final
fracture occurred. Figure 7 shows typical amplitude distribution plot for specimens A. The same

Fig. 7. Load and amplitude distribution for A specimen.
curve for specimens B is shown in Fig. 8. As it can be noticed, typical load-displacement curves
and AE activity patterns for both types are almost identical. In order to better understand thefailure of these composites and to verify whether or not embedded PVDF sensors are able to follow on line the structural changes occurring, the cumulative event distributions have been85

Fig. 8. Load and amplitude distribution for B specimen.

Fig. 9. Typical load and cumulative AE events versus time for specimen B.
divided in different intervals because, during the flexural tests, the testing machine was periodically stopped and the specimens examined by SEM so as to correlate the different PADs detectedto the actual damage modes and progression (Fig. 9). The PADs corresponding to the different
intervals labelled 1°-5° in Fig. 9 are shown in Fig. 10. During the first part of the loading (intervals 1 and 2), the PAD shows that events are generally situated in the range of 43-50 dB, which
in literature is, in this condition, well documented as related to matrix microcracking and matrixmatrix friction. They also show some events around 55-63 dB characterizing the interface deadhesion. This behavior can be explained referring to the low strength of the aramid/epoxy inter86

Fig. 10. Evolution of a typical PAD during flexural loading for specimen B.

Fig. 12. SEM micrograph from flexural tests
showing longitudinal cross section at the mid
span at the end of interval 2.

Fig. 11. SEM micrograph showing the crosssection of a B specimen in the as-manufactured stage.

face, while fiber failure seems to be negligible in this stage (no significant events characterizedby high amplitude values). These sensors seems to be insensitive to interfacial debonding due tofiber straightening, which is supposed to happen at the onset of loading. In fact, during the very
first part of the loading, few signals or even no signals were detected. This behavior seems tocontradict the experimental evidence that in composite materials AE is noted from the beginningof loading. This implies that a damage-producing mechanism should be already active at low
stress levels. Since both matrix and fibers are not active at such low stress levels, it is reasonable
to assume that some damage develops at the interface between fiber and matrix. Two different
mechanisms can account for the occurrence of this kind of damage. The first is related to a fiber
straightening process, during which the strains produced are higher than the failure strain of the87matrix, thus resulting in interfacial debonding. The second is due to residual stresses at the interface developed during the manufacturing stage of the composites as a result of different coefficients of thermal expansion. The SEM observations agree with what is suggested by the PAD, as
it can be seen in Fig. 12, where de-adhesion in the warp fiber bundle and small matrix cracks canbe seen. In order to make a comparison, Fig. 11 shows a SEM micrograph of a composite in theas-manufactured stage.

Fig. 13. SEM micrograph from flexural tests
showing longitudinal cross section at the mid
span at the end of interval 3.

Fig. 14. SEM micrograph showing cracks inthe weft fiber bundle.

Fig. 16. SEM micrograph from flexural tests
showing longitudinal cross section at the mid
span after final failure occurred.

Fig. 15. SEM micrograph showing some fiber
kinking.

88

Fig. 17. Evolution of a typical duration distribution plot for a B specimen.

Fig. 18. Linear localization plot from flexural testing of B specimen.
The stage that leads to the first drop in the load, is characterized by a large number of events
situated in the amplitude range 40-65 dB, characterizing the growth of transverse cracks in weft
fiber bundle and some delaminations (see Figs. 13 and 14), and events with high amplitudes (8090 dB), which belong to fiber fracture located mainly in the outer ply on the tensile side. Fiber
fracture occurs late during the loading due to the small scatter of aramid fiber strength. This behavior is strongly dependent on the type of the weaving structure used. In this stage, the tensile
failure is the dominant failure mode even though micro-damage and fiber kinking were observedon the compression side of the specimens (Fig. 15).
As loading continues, the corresponding failure is characterized by the growth and accumulation of the micro-damages discussed above, as PAD and SEM observations confirm. Figure 16
shows the longitudinal cross section at the mid-span after final failure occurred. It is evident theaccumulation of the several damage mechanisms discussed so far. These results are also con89

firmed by the duration distribution of the AE events, as can be seen in Fig. 17, which shows theevolution of the duration distribution for a B specimen (the intervals being the same as Fig. 9).
As loading continues, most of the signals are located at 0-200 μs, which are values typical of matrix cracking while longer durations begin to be significant during the third interval and up to final fracture (delamination and debonding), where the occurrence of even longer durations should
be attributed to fiber ruptures.
The embedded PVDF sensors were also able to localize the AE events (see Fig. 18). Due tothe type of loading, most of the signals are located at the center of the specimen even if the AE
signals seem to be more spread around the point of the maximum applied load, probably due tothe enhanced toughness of Kevlar fibers.
Conclusions
Results from this work indicate that the embedment of PVDF sensors in aramid/epoxy laminates
affects negligibly the mechanical response of reference materials. In fact, the two specimen types(A and B) exhibited very similar three point bending strength and flexural modulus (within 3% ofeach other in both cases). The mechanical characterization was preceded by SEM analysis,
which enabled to confirm both the possibility of successfully embedding PVDF sensors in composite laminates and the sensor’s ability to withstand the composite curing temperature without
experiencing significant degradations (especially depoling). The mechanical tests were monitored on-line through the use of AE. A parametric off-line analysis of the signals detected
showed that different failure mechanisms have different characteristics, which can be properly
studied and identified using the AE responses. Through these investigations it can be concluded
that PVDF film sensors can be effectively used as acoustic emission sensors for a SHM system.
However, a more in-depth investigation of AE through the use of techniques such as statistical
pattern recognition is necessary. These results are nonetheless promising in view of the development of a reliable, low cost, effective and in situ damage monitoring system.
References
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H.N. Bar, M.R. Bhat, and C.R.L. Murthy (2005), J. of Nondestructive Evaluation, 24, 121-134.
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M.A. Hamstad, G.P. Sendeckyj (1993), J. of Acoustic Emission, 11, 33-41.
M.A. Hamstad (1995), Proc. of the 5th International Symposium on Acoustic Emission from
Composite Materials, pp. 111-119.

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I-G. Kim, H-Y. Lee and J-W. Kim (2005), J. of Intelligent Material Systems and Structures, 16,
1007-1013.
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I. Narisawa, H. Oba (1984), J. of Materials Science, 19, 1777-1786.
I. Narisawa, H. Oba (1985), J. of Materials Science, 20, 4527-4531.
S.W. Or, H.L.W. Chan, C.L Choy (2000), Sensors and Actuators, 80, 237-241.
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E. Riande, R. Diaz-Calleja (2004), “Electrical properties of polymers,” CRC Press.
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C. Zweben (1978), J. of Composite Materials, 12, 422-430.

91

ACOUSTIC EMISSION SIGNALS GENERATED BY MONOPOLE (PENCIL-LEAD BREAK) VERSUS DIPOLE SOURCES:FINITE ELEMENT MODELING AND EXPERIMENTS
M. A. HAMSTAD
Department of Mechanical and Materials Engineering, University of Denver
Denver, CO 80111, USA

Abstract
Acoustic emission (AE) practitioners routinely use surface pencil-lead breaks (monopoles) toobserve expected AE signal characteristics. In contrast, stress-generated AE sources are almost
universally composed of dipoles. Thus, understanding the primary differences between the signals generated by these two different source classes is of key importance. This research had thegoal of analyzing and contrasting the AE signals generated by monopole and dipole sources. A
finite-element-modeled (FEM) database of AE signals provided an ideal means to study these
two source types. The AE signals represented the top-surface out-of-plane displacement versus
time from point sources inside an aluminum plate of 4.7-mm thickness. In addition, monopole
sources both on the plate top surface and the edge surface were included in the database. The AE
signals were obtained from both in-plane and out-of-plane monopole and dipole sources. Results
were analyzed with both a bandpass filter of 100 to 300 kHz and a 40-kHz high-pass filter. The
wide-plate specimen domain effectively eliminated edge reflections from interfering with thedirect signal arrivals. To supplement and compare with the FEM results, experiments with pencil-lead breaks were carried out on the top surface and the edge of a large aluminum alloy plateof 3.1-mm thickness.
Keywords: Pencil-lead break, AE monopoles, AE dipoles, finite-element modeling, Lambwaves.
Introduction
A standard tool used in experimental acoustic emission (AE) studies is a pencil-lead break(Hsu-Neilsen source). The resulting signals detected at AE sensors have been characterized assimilar to the real AE generated during stressing of the sample. Due to the similarity, the pencillead break is often used for various purposes prior to testing a sample. These purposes include:(a) wave propagation studies, including AE signal attenuation with propagation distance, (b) AE
signal characterization, including the frequencies and the intensity of the different modes in thesignal, (c) selection of sensors and frequency bandpasses, (d) verification of sensor coupling andnormal operation of sensors and system electronics, (e) determination of typical signal propagation velocities, including “demonstration” of source location capabilities, and (f) assessment of
typical signal durations. The purpose of the research presented here was to characterize both thesimilarities and the significant differences between pencil-lead breaks and real AE signals bothby finite element modeling and experimental pencil-lead breaks. In particular, the characterization examined the relative intensity of the Lamb modes excited as well as the frequencies within
the modes that carry significant energy as a function of the source type and the source orientation
relative to a plate.

J. Acoustic Emission, 25 (2007)92© 2007 Acoustic Emission GroupPencil-lead breaks are monopoles and real AE signals are nearly all dipoles. Since the monopole pencil-lead break is normally applied to the outside of a test sample and the dipole AE
sources originate from source points buried inside the sample, it is not clear how a purely experimental study might be carried out on metal samples. For the purposes of this study, the technique of finite-element modeling (FEM) of the operation of AE sources and the subsequent
propagation of the waves is a way to overcome some of the difficulties of experiments alone.
The FEM approach allows placement of monopoles (to simulate pencil-lead breaks) and dipoles(to simulate real AE sources) at exactly the same positions inside a test object, and it also provides a means to place monopoles on the outside surface as well. Thus, all the necessary comparisons can be made. To enhance the comparisons, the results presented here include not only
the out-of-plane displacement signals, but also wavelet transform (WT) results that clearly allow
Lamb modes to be identified. The WT results also show the frequencies that are excited with intensity in the modes. Finally, the study included some experiments with actual pencil-lead breaks
monitored with a wideband sensor. These results were then compared to the associated FEM results.
Finite Element Modeled AE Signal Database
A small part of an existing near-field and far-field AE signal database for a 4.7-mm thickaluminum plate was used in the research reported here. This database has been described in previous publications (Hamstad et al., 2002a; 2002b; Downs et al., 2003; Hamstad et al., 2003). The
validation of this FEM technique is found in a previous publications (Gary and Hamstad, 1994;
Hamstad et al., 1996; Hamstad et al., 1999). The entire FEM signals were numerically processedwith a 40-kHz (four-pole Butterworth) high-pass filter or a similar 100-to-300-kHz filter followed by resampling from the original time step of 44.6 ns per point to 0.1 s per point. These
AE signals were examined out to 150 s after the source initiation time. This procedure avoids
the plate edge reflections, which appear well after the direct signals in the large plate domain.
The sources in the continuous mesh domain were either monopoles (single-cell body forces) or
dipoles (self-equilibrating forces of two oppositely directed monopoles with one cell betweenthem) using the “equivalent body force” concept for displacement discontinuities (Burridge andKnopoff, 1964). The forces were applied with a “cosine bell” temporal time dependence T(t)given by:
0 for t < 0,T(t) = (0.5 – 0.5 cos[  t /  ] ) for 0  t   , and(1)
1 for t >  ,where  = 1.5 s was the source rise time. The three-dimensional cell size was 0.313 mm. The
AE signals from a single node provided the out-of-plane top-surface displacement corresponding
to a perfect point-contact sensor located in the zero-degree propagation direction (in-plane, xaxis direction, aligned with the in-plane source force direction) at a distance of 180 mm from thesource epicenter. To directly compare the results from the two source types, they were locatedinside the plate at either of two different depths (0.783 or 2.037 mm) below the top surface of theplate. Two depths were chosen due to previous results (Hamstad et al., 2002b) demonstratingthat in a thin plate as the source depth of a dipole moves from near the surface to near the midplane, the dominate AE signal mode changes from the fundamental anti-symmetrical mode, A0,
to the fundamental symmetric mode, S0. Since pencil-lead breaks are normally done on an external surface, monopole sources (applied over an exterior cell face) activated on the plate top surface and the plate edge were also used. These results were used to characterize any differences
between AE signals from buried monopoles compared to surface monopoles.

93

Wavelet Transform Information
Wavelet transform (WT) results were used to enhance the identification of the AE signal
Lamb modes and to indicate highly excited frequency regions within the modes. The WT results
were obtained using the AGU-Vallen Wavelet freeware (Vallen, 2005) with the key parametersettings being: maximum frequency = 700 kHz or 1000 kHz (for the experiments to account forthe smaller plate thickness); frequency resolution = 3 kHz and wavelet size = 600 samples. The
Wavelet Time Range Setting for the number of samples (i.e., points) was 1500 to 2048 allowing
the full-direct-arrival signal to be transformed. In the color WT figures, the red color indicatesthe highest intensity region of the WT coefficients. In a black-and-white print out of the color
results, the darkest region inside a lighter region indicates the high intensity region.
Contrasts of a Near-Surface Out-of-Plane Monopole with Dipoles
In order to most directly illustrate the similarities and contrasts between monopoles and dipoles, the point sources were typically centered at the same location in the plate when such a location was relevant. Since pencil-lead breaks normally are applied to the plate surface and/or theplate surface edges, two sections later in this paper demonstrate that the differences between monopoles buried inside the plate and those applied to the surface and/or edge can be ignored forthe purposes of the results presented here. Hence, the terms pencil-lead breaks and monopoles
are used interchangeably in this work. To distinguish the FEM based pencil-lead breaks from theexperimental ones (near the end of this paper), references to the FEM-based pencil-lead breaks
will have quotes (“PLB”) while the experimental pencil-lead breaks will not (PLB).
Figures 1 and 2 show the out-of-plane displacement signals (or AE signals) for (a) an out-ofplane monopole, (b) an in-plane dipole and (c) an out-of-plane dipole, all located 0.783 mm below the plate top surface (Note part (d) will be discussed later). Figure 1 provides the signals for
a 100-to-300-kHz bandpass, and Fig. 2 has the same results for a 40-kHz high-pass filter. In eachof these figures, the first region of the signal is the initial portion of the symmetrical extensionalmode followed by a higher amplitude anti-symmetrical flexural mode. It is clear from all the signals shown that the signal durations do not change significantly with source type. In contrast,
both of these figures demonstrate that the AE signal generated by an out-of-plane monopole isstrongly dominated by the flexural, A0, mode as compared to the symmetric, S0, mode, while, thesignals from dipole sources are not nearly as strongly dominated by the flexural mode. To clarify
these modal comments, Figs. 3 (100 to 300 kHz) and 4 (40-kHz high-pass) show the WT resultsfor each displacement signal with the superimposed “modified” group velocity curves of the two
fundamental modes. The “modification” was the conversion of the group velocities to time byuse of the 180-mm propagation distance. The reader should note in Figs. 3 and 4 that a higher
frequency portion of the S0 mode overlaps in time the A0 mode.
The dominance of the flexural mode in the signals from the monopole source versus thatfrom the dipole sources can be approximately quantified by calculating the ratio formed from the
maximum amplitude of the flexural region of the signal divided by the maximum amplitude of
the part of extensional signal present before the first arrival of the flexural mode. For the 100-to300-kHz signals, the ratios respectively for the out-of-plane monopole, in-plane dipole and outof-plane dipole are about 31, 12 and 10 dB. For the 40-kHz high-pass signals the same ratios areabout 37, 14 and 12 dB. It should be noted for the 40-kHz high-pass dipole signals that the peak
amplitude in the flexural part of the signal is really a superposition of the A0 mode and a smallerhigher frequency part (mostly centered about 522 kHz) of the S0 mode as is clearly shown in94

Fig. 1 Displacement vs. time, sources at 0.783
mm below the top surface and 100 to 300 kHz;(a) out-of-plane monopole, (b) in-plane dipole,(c) out-of-plane dipole and (d) in-plane monopole.

Fig. 2 Displacement vs. time, sources at 0.783
mm below the top surface and 40-kHz highpass; (a) out-of-plane monopole, (b) in-plane
dipole, (c) out-of-plane dipole and (d) in-planemonopole.

parts (b) and particularly (c) of Fig. 2 and in the corresponding WT results shown in Fig. 4. The
ratios show for either frequency range the dominance of the flexural mode in the out-of-planemonopole generated signal is about an order of magnitude stronger than for the dipole-based signals.
Contrasts and similarities between signals from a near-surface (0.783 mm) out-of-plane monopole and those from buried dipoles close to the midplane at a depth of 2.037 mm below theplate top surface are shown respectively in Figs. 5 (100 to 300 kHz) and 6 (40-kHz high-pass) inparts (a), (b) and (c) (Note part (d) will be discussed later). As was done above, the analysis that
follows compares signals from the dipoles with those from an out-of-plane monopole (part (a))
located near the plate surface (referred to as an out-of-plane surface “PLB”, to keep this fact in
the readers mind), since most experimental works apply out-of-plane PLBs on the plate surface.
As before, the signal durations are very similar for the dipoles and the out-of-plane “PLB”. The
100-to-300-kHz bandpass results (Fig. 5 (a) to (c)) show an even larger difference between the“PLB” signal and the dipole signals than was present when the dipoles were near the plate top
surface. With dipoles near the plate midplane, the same peak amplitude ratios for the in-plane
dipole and the out-of-plane dipole are respectively about -3 and -4 dB. We note again that for the
dipoles, the extensional mode contributes to flexural region amplitude, and thus the ratios are nota perfect measure. Never-the-less these ratio values are in sharp contrast to the previously calculated ratio for the near-surface out-of-plane “PLB” source of 31 dB. This extremely strong modal-dominance difference is also seen in the corresponding WT results when part (a) of Fig. 7 iscompared with parts (b) and (c). In these figures, it is clear that both fundamental Lamb modes
are present in the dipole-generated signal results, while only the flexural mode has significant
intensity in the “PLB” case. To summarize, these results show that the 100-to-300-kHz AE signals from the near-midplane dipoles are dominated by the extensional mode, in particular the95

Fig. 3 Wavelet transforms corresponding to
Fig. 1, vertical scale 0 to 0.7 MHz, horizontal
scale 0 to 150 s, sources at 0.783 mm below
the top surface and 100 to 300 kHz; (a) out-ofplane monopole, (b) in-plane dipole, (c) outof-plane dipole and (d) in-plane monopole.

Fig. 4 Wavelet transforms corresponding to
Fig. 2, vertical scale 0 to 0.7 MHz, horizontal
scale 0 to 150 s, sources at 0.783 mm below
the top surface and 40-kHz high-pass; (a) outof-plane monopole, (b) in-plane dipole, (c)out-of-plane dipole and (d) in-plane monopole.

initial arrival of the extensional mode signal. Thus these AE type signals are very different intheir dominant modes (and associated frequencies) and their relative modal intensities from theout-of-plane near top surface “PLB” signal. Even though it is not experimentally possible tocarry out an out-of-plane buried PLB near the midplane of an aluminum plate, a FEM run wasmade for this monopole source at the 2.037 mm depth. Figure 8 shows that the signal from an
out-of-plane “PLB” at a depth of 2.037 mm was nearly identical with the near surface out-ofplane “PLB” at the 0.783 mm depth.
The 40-kHz high-pass AE signals (Fig. 6) from the dipoles located near the midplane havepeak amplitude ratios respectively for the in-plane and out-of-plane cases of about 9 and 13 dB.
96

Fig. 5 Displacement vs. time, sources at 2.037
mm below the top surface and 100 to 300 kHz;(a) out-of-plane monopole (0.783 mm), (b) inplane dipole, (c) out-of-plane dipole and (d)in-plane monopole.

Fig. 6 Displacement vs. time, sources at 2.037
mm below the top surface and 40-kHz highpass; (a) out-of-plane monopole (0.783 mm),(b) in-plane dipole, (c) out-of-plane dipole and(d) in-plane monopole.

Thus it appears that the flexural mode dominates, but the dominance is not as strong as the flexural mode dominance of 37-dB ratio for the monopole source near the plate top surface. Again
even though the AE signal peaks for these two dipoles are in the region often associated with theflexural mode, the peak signal amplitudes are dominated by a slower group velocity and higher
frequency part of the extensional mode. This extensional mode higher frequency dominance,
centered about 522 kHz, is clearly seen in the displacement signals of Fig. 6(b) and (c) and in thecorresponding parts of the WT results in Fig. 9 (in particular, 9(c)). So as was the case before,
the ratios are not a perfect measure. But, in summary, the analysis again shows that only the signal durations are similar. In contrast, the dipoles are very different in both the relative intensitiesof the modes and the relative strength of the dominant frequencies in the signals as compared tothe out-of-plane top surface “PLB” results.
Contrasts of an In-Plane Monopole and Buried Dipoles
Since in-plane PLBs can be made on the edge surface of a plate, this section examines comparisons between signals from in-plane monopoles and dipoles each at the two source depths(Note PLBs can be done at multiple depths below the plate top surface on an edge). In part (d) of
Figs. 1-7 and 9, the AE signals or WTs of the signals from an in-plane monopole were included.
As is clear from the AE signal from the in-plane monopole in Fig. 1(d) (100-to-300-kHz bandpass), its AE signal is much more similar with respect to relative modal intensities to the two dipole-based signals at the same 0.783 mm depth than the out-of-plane monopole generated signal
in part (a) of Fig.1. The ratios of the peak amplitude of the flexural region to that of the extensional region are 6.5, 12 and 10 dB respectively for the in-plane monopole, in-plane dipole andout-of-plane dipole. These dipole ratios are much closer to the in-plane monopole than was thecase for the out-of-plane “PLB”. The WT results (100 to 300 kHz) for these three cases, in Fig. 3
97

Fig. 7 Wavelet transforms corresponding to Fig. 5, vertical scale 0 to 0.7 MHz, horizontal scale 0
to 150 s, sources at 2.037 mm below the top surface and 100 to 300 kHz; (a) out-of-plane monopole (0.783 mm), (b) in-plane dipole, (c) out-of-plane dipole and (d) in-plane monopole.
(b) through (d), also show more similarity in the relative intensity of the two fundamental modes(and frequency regions excited) between the dipole sources and the in-plane monopole sources.
This increased similarity can be seen in that the initial part of the extensional mode is apparent in
the WT (blue or darker region) for all three cases, whereas for the out-of-plane monopole (Fig. 3(a)), the initial part of the S0 mode in the WT is not apparent compared to the highly dominate A0
mode.
For the 40-kHz high-pass signals from the sources at the 0.783 mm depth, analysis of Figs. 2(b) through (d) shows that the signal from the in-plane monopole at this depth does have a more98

Fig. 8 Displacement versus time for 40-kHz high pass signal after propagation of 180 mm, outof-plane monopoles at (a) 0.783 mm and (b) 2.037 mm below the top surface.
favorable ratio between the intensities of the two modal regions than the signal from the out-ofplane monopole (a). For these wideband signals the corresponding peak amplitude ratios are 10,
14 and 12 dB respectively for the in-plane monopole, in-plane dipole and the out-of-plane dipole.
Now the monopole signal modal intensity ratio is very close to the dipole signal ratios. But as isclear from the corresponding WT results in Fig. 4, the in-plane monopole signal (d) does not
fully represent the higher frequency and later arrival parts of the S0 mode of the dipoles (see (b)and (c)). Also, it should be noted in Fig. 2 that the higher frequency and slower part of the extensional mode for the two dipole sources is clearly more intense than the in-plane monopole (seesuperimposed higher frequency near the end of the flexural mode in the dipole AE signals (b)and (c)). This fact is particularly true for the out-of-plane dipole source, where the flexural region signal has significant energy in both the flexural mode and the extensional mode.
When both the in-plane monopole and the dipoles are centered near the plate midplane at
2.037 mm below the plate top surface, Figs. 5 (AE displacement signals) and 7 (corresponding
WTs) show for the 100-to-300-kHz bandpass that the in-plane “PLB” is again considerably moresimilar in relative modal intensity to the dipoles at the same depth than the near-surface out-ofplane “PLB”. For this frequency range, the corresponding peak amplitude ratios are about -8, -3
and -4 dB, respectively, for the in-plane monopole, in-plane dipole and the out-of-plane dipole.
The only missing region in the in-plane monopole signal is that for the high-frequency part of theextensional mode. This later observation can be clearly seen in the WT shown in Fig. 7 part (c)versus part (d) for the out-of-plane dipole and in-plane monopole, respectively. Note that thishigh-frequency region is apparent for the out-of-plane dipole in spite of the filter.
At the 2.037-mm depth, the 40-kHz high-pass results are shown in parts (b) through (d) of
Figs. 6 (AE) and 9 (corresponding WTs). The ratios, which correspond to those pointed out
above for the 100-to-300-kHz bandpass, are -0.5, 9 and 13 dB. Thus, with this frequency range,
the ratio of the two modal amplitudes for the in-plane monopole is not as close to that of the dipoles as it was for the bandpass data, but it is much closer than the ratio for the out-of-plane monopole (near the top surface). It is also again clear (at this source depth) in the flexural region of
the dipole-based signals that the high-frequency part of the extensional mode dominates, while
for the in-plane “PLB” signal this higher-frequency region is present but not dominant. Instead,the initial arrival of the “PLB” S0 mode weakly dominates compared to that portion for the dipoles, and the latter part of the monopole-generated signal is more dominant compared to the dipole-generated signals. In summary, Fig. 9 (d) for the in-plane monopole source shows all the99

Fig. 9 Wavelet transforms corresponding to Fig. 6. Vertical scale 0 to 0.7 MHz, horizontal scale
0 to 150 s, sources at 2.037 mm below the top surface and 40-kHz high-pass. (a) out-of-planemonopole (0.783 mm), (b) in-plane dipole, (c) out-of-plane dipole and (d) in-plane monopole.
modal regions that appear in the dipole source cases. The intensity of the in-plane monopole signal varies in some regions of the modes relative to that of the dipole signals. But, the in-plane“PLB” corresponds much more closely to the dipole generated signals than those from the outof-plane “PLB”.
Differences between Surface and Below-Surface Out-of-Plane Monopoles
To establish the relevance of the previous sections to PLBs, we next show that there areminimal differences between the signals from an out-of-plane monopole source on the plate top
surface and the same source located a small distance below the top surface of the plate. Figure 10
shows the comparison of the out-of-plane displacement signals at a propagation distance of 180
mm for the out-of-plane top surface monopole (a) versus one located at 0.783 mm below the100

Fig. 10 Displacement vs. time. Source out-ofplane monopole, 40-kHz high-pass; (a) source
located on top surface of plate, (b) located at a
depth of 0.783 mm below the top surface.

Fig. 11 Spectral magnitude of corresponding
signals in Fig. 10; (a) source located on top
surface of plate, (b) located at a depth of 0.783
mm below the top surface.

surface (b). These results for 40-kHz high-pass filter data are nearly identical for each source position. The spectra for these signals were also calculated and are nearly identical as illustrated inFig. 11. Thus, the earlier assertion that using the buried out-of-plane monopole at 0.783 mm tobe representative of a PLB on the top surface was valid.
Differences between Edge Surface and Buried In-Plane Monopoles
For the in-plane monopole, Fig. 12 shows that the surface edge source location gives essentially the same modal distribution of signal energy as the same source buried and located transversely near the middle of the large plate. In this comparison, we have chosen to show the similarity by use of the WT of the 40-kHz high-pass displacement signals for the edge surface versus
the buried source. Figure 12 shows the results for a source depth of 0.783 mm below the top surface, and Fig. 13 shows the same type of presentation of the results for the two in-plane monopoles at a depth of 2.037 mm. In these figures the maximum color scale of the WT result was
set at 70 % instead of the WT program default of 100 % to better illustrate the distribution of
signal energy. It is clear in Figs. 12 and 13 that at the 180-mm propagation distance, the same
regions of the same modes are present with slightly different intensity for both source positions.
Thus again it was valid to use buried in-plane monopoles to represent edge surface PLBs. But for
the in-plane monopole case, the reader should note that there are significant changes in the modal distribution of energy as the depth along the edge of the in-plane monopole or PLB changes(compare Fig. 12(a) with Fig. 13(a).

101

Fig. 12 Wavelet transform, source at depth of
0.783 mm below top surface.

Fig. 13 Wavelet transform, source at depth of
2.037 mm below top surface.

For both figures above, source in-plane monopole, 40-kHz high-pass, vertical scale 0 to 0.7 MHz
with horizontal scale 0 to 150 s; (a) source located on edge surface of plate, and (b) located near
center of the plate (buried).
Experiments with Pencil-Lead Breaks
A large aluminum-alloy plate was used for pencil-lead breaks (PLBs) to obtain experimental
results that relate to the preceding FEM results. The dimensions of the plate were 1520 mm by
1220 mm by 3.1 mm. The large transverse plate dimensions precluded reflections from the plateedges arriving at the AE sensor during the duration of the direct-path signal for the 254-mm
propagation distance used in the experiments. The sensor used in these experiments had a conicalsensitive element with a 1.5-mm aperture. This sensor is nearly flat with frequency (sensitive toout-of-plane displacements of the surface it is mounted on) from about 40 kHz to 1 MHz. It wasdeveloped at NIST Boulder, CO, USA (Hamstad and Fortunko, 1995; Hamstad, 1997). It has alow-noise, internal field-effect transistor, and it has a signal-to-noise ratio that is considerably
better than typical commercial wideband sensors calibrated by the NIST Gaithersburg developed
technique (Hamstad, 1997). The sensor was coupled to the top surface of the plate with vacuum
grease, and it was spring loaded with a force of about 5 N against the plate. In these experiments
a 50-kHz high-pass passive filter was used prior to recording the signals with a 12-bit digital
scope at a sampling rate of 0.1 s per point. Pencil-lead breaks were done near the center of theplate’s top surface and midway along one of the long edges of the plate. The in-plane edge PLBs
were done near the plate mid-plane and near the top of the edge. The 2H lead had a diameter of
0.3 mm, and it was broken with a length of about 2 mm.
Figure 14 shows typical results for the signals from the PLBs at the three different positions:(a) top surface out-of-plane, (b) edge surface, in-plane near the top of the edge and (c) edge
102

Fig. 14 Signal amplitude versus time for 0.3-mm pencil-lead breaks with 50-kHz high-pass filter
after 254-mm propagation distance from the source position; (a) out-of-plane PLB, (b) near top
of edge in-plane PLB and (c) edge in-plane near mid-plane PLB.
surface, in-plane near the mid-plane. In the same sequence of PLB locations, Fig. 15 shows WT
results of these signals for the three different lead break positions. Qualitative comparisons of theexperimental Figs. 13(a) and 14(a) with the FEM results (40-kHz high-pass) in Figs. 2(a) and4(a) demonstrate that there is a strong dominance of the flexural mode for both top surface outof-plane PLBs and near top surface out-of-plane monopoles.
When the PLB source was near the top surface on the plate edge, a qualitative comparison of
the PLB results in Figs. 14(b) and 15(b) with the FEM (40-kHz high-pass) in-plane monopole(near the top surface) results in Figs. 2(d) and 4(d) again show that the edge in-plane PLB and
the in-plane monopole signals are similar. Namely, with this position and orientation of the monopole sources, the amplitude of the extensional mode is now considerably closer to the amplitude of the flexural mode than was the case with the out-of-plane sources.
When the PLB source was near the mid-plane on the plate edge, a qualitative comparison of
the PLB results in Figs. 14(c) and 15(c) with the FEM (40-kHz high-pass) in-plane monopole
results in Figs. 6(d) and 9(d) again show for the two different sources similar signals result.
Namely, with this position and orientation of the monopole sources, the amplitude of the extensional mode is now even closer to the amplitude of the flexural mode than was the case with theout-of-plane monopole sources and the in-plane monopole sources near the top edge. Thus, inboth edge source positions (in-plane orientations), even with the differences in the plate thicknesses of the FEM data and the PLB data, the modal emphasis of the signals with an in-plane
PLB source or monopole are qualitatively much more similar to dipole-type AE sources. Hence,with in-plane PLB or monopole sources, there are not the extreme differences in the relative amplitudes of the two fundamental modes as are present in the out-of-plane PLBs and out-of-planemonopoles.
103

Fig. 15 Wavelet transforms with superimposed group velocities corresponding to Fig. 14; (a)out-of-plane PLB, (b) near top of edge in-plane PLB and (c) edge in-plane near mid-plane PLB.
To make the comparisons of the FEM (monopole and dipole) and PLB results more quantitative but not perfect due to changes in plate thickness, overlapping of modes (as pointed out earlier), the experimental sensor not being perfect, propagation distance differences and a small difference in the high-pass frequency of the experimental work, Table 1 was created using the 40kHz high-pass data (modeled data) and 50-kHz high-pass (experimental data). This table shows
the ratios (as was done earlier in the paper) of the flexural mode region peak amplitude divided
by the peak amplitude of the extensional mode region. Clearly, in this more quantitative comparison, the trend is the same as was described above in the qualitative comparison.

104

To better relate these PLB and FEM monopole results to real dipole AE sources additionalrows (shown in bold type) in Table 1 show the same ratios for FEM dipoles (40-kHz high-passdata) at the same positions as the monopoles. These results clearly show that the distribution ofpeak amplitudes of the two fundamental modes for in-plane and out-of-plane dipoles is muchbetter simulated by in-plane edge PLBs at the two depths.
Table 1 Ratios of peak amplitudes of flexural mode region to extensional mode region.
Monopole source typeOut-of-plane top surface PLB
Out-of-plane near top surface monopole (FEM)
Out-of-plane dipole near top surface (FEM)
In-plane edge PLB near top of edge
In-plane monopole near top surface (FEM)
In-plane dipole near top surface (FEM)
In-plane edge PLB near mid-plane of edge
In-plane monopole near mid-plane (FEM)
In-plane dipole near mid-plane (FEM)

Ratio of flexural to extensional mode
amplitude, dB
28
37
12
13
10
14
1.6
-0.5
9

Implications on the Use of Experimental PLBs to Simulate Real AE Sources
Based on the study done in this paper (AE signals, WTs and ratios of modal regions), the useof in-plane edge PLBs seems to be a much better choice to simulate the modal intensities of real
AE signals that might be expected in thin plate or shell-type structures. This choice of PLB locations and orientations results in the simulated AE exciting both modes (and the associated frequencies) rather than just the flexural mode. The PLB testing need not be done on the real structure. Instead, it could be done on a plate or shell of the same thickness and material as the real
structure. Also, the edge PLBs should include breaks with the lead position both nearer the top or
bottom plate surface and near the mid-plane of the plate. From practical experience, this author
has found that some visual magnification of the pencil-lead contact region helps to locate thelead at the desired depth.
Conclusions••••

Surface “PLBs” sources on either the top surface or edge surface of a 4.7-mm thick plate
are respectively well represented by interior buried out-of-plane monopole or in-plane monopole sources.
In-plane PLB sources on a plate edge provide displacement signals with relative modal (A0and S0) intensity distributions much closer to those from buried dipoles at the same depths
than do surface out-of-plane PLBs.
In-plane edge PLBs should be done both near the top or bottom edge surface and near the
mid-plane to excite all the modal intensities (with most of their associated frequencies) ofreal dipole sources over this range of depths.
Out-of-plane PLBs on a plate surface and modeled out-of-plane monopoles do not provide
displacement signals with relative modal (A0 and S0) intensity distributions at all similar to
those from buried dipoles.

105

Acknowledgements
The development of the finite element code by Dr. John Gary (NIST retired) and the finite
element computer runs by Ms. Abbie O’Gallagher, NIST Boulder, are appreciated.
References
Burridge, R. and Knopoff, L. (1964) “Body force equivalents for seismic dislocations,” Bulletin
Seismic Society of America, 54, 1875-1914.
Downs, K. S. Hamstad, M. A. and O’Gallagher, A. (2003) “Wavelet transform signal processing
to distinguish different acoustic emission sources,” J. of Acoustic Emission, 21, 52-69.
Gary, John and Hamstad, Marvin (1994) “On the far-field structure of waves generated by a pencil-break on a thin plate,” J. of Acoustic Emission, 12, (3-4), 157-170.
Hamstad, M. A., Gary, J. and O’Gallagher, A. (1996) “Far-field acoustic emission wave by
three-dimensional finite element modeling of pencil-lead breaks on a thick plate,” J. of Acoustic
Emission, 14, (2) 103-114.
Hamstad, M. A. and Fortunko, C.M. (1995) "Development of practical wideband high fidelity
acoustic emission sensors," Nondestructive Evaluation of Aging Bridges and Highways, Steve
Chase, Editor, Proc. SPIE 2456, pp. 281-288.
Hamstad, M. A. (1997) "Improved signal-to-noise wideband acoustic/ultrasonic contact displacement sensors for wood and polymers," Wood and Fiber Science, 29. (3), 239-248.
Hamstad, M. A., O'Gallagher, A. and Gary, J. (1999) "Modeling of buried acoustic emission
monopole and dipole sources with a finite element technique," J. of Acoustic Emission, 17, (3-4),
97-110.
Hamstad, M. A., O’Gallagher, A. and Gary, J. (2002a) “Examination of the application of awavelet transform to acoustic emission signals: part 1 source identification,” J. of Acoustic
Emission, 20, 39-61.
Hamstad, M. A., O’Gallagher, A. and Gary, J. (2002b) “Examination of the application of awavelet transform to acoustic emission signals: part 2 source location,” J. of Acoustic Emission,
20, 62-81.
Hamstad, M. A., Downs, K. S. and O’Gallagher, A. (2003) “Practical aspects of acoustic emission source location by a wavelet transform,” J. of Acoustic Emission, 21, 70-94 and A1-A7.
Vallen, J. (2005) “AGU-Vallen Wavelet transform software version R2005.1121,” VallenSysteme GmbH, Münich, Germany. Available at http://www.vallen.de/wavelet/index.html.

106

HIGH-TEMPERATURE ACOUSTIC EMISSION SENSING
USING ALUMINUM NITRIDE SENSORHIROAKI NOMA, TATSUO TABARU, MORITO AKIYAMA, NORIKO MIYOSHI,TOMOHARU HAYANO* and HIDEO CHO*
Measurement Solution Research Center, National Institute of Advanced Industrial Science andTechnology (AIST), 807-1 Shuku, Tosu, Saga 841-0052, Japan;* Faculty of Science and Engineering, Aoyama Gakuin University,
5-10-1, Fuchinobe, Sagamihara, Kanagawa, 229-8558, Japan

Abstract
We developed a heat-resistant acoustic emission (AE) sensor using AlN thin films and detected AE signals from molten-salt attack of Type-304 stainless steel pipe. Aluminum nitride(AlN) is a promising AE sensor element for high-temperature environment, such as gas turbinesand other plants because AlN maintains its piezoelectricity up to 1200°C. Highly c-axis-oriented AlN thin-film sensor elements were prepared on silicon single crystals by rf magnetron
sputtering. The AE sensor sensitivity increased with d33 and thickness of AlN elements and theheat-resistant AE sensors were developed using AlN elements with high d33 values and thicknessof 9 μm. The sensor showed a constant sensitivity across a frequency range of 100 kHz to 1 MHz
at ambient temperature. The sensor characteristics were evaluated at elevated temperatures up to
700°C. It was confirmed that the AE sensor works well up to 700°C and does not deteriorate.
The AE sensor detected AE signals from frequent cracking of oxide scales produced bymolten-salt attack (85% V2O5 + Na2SO4) at 700°C during cooling of the pipe.
Keywords: AE sensor, high temperature, aluminum nitride, molten-salt attack
Introduction
Plant monitoring systems are on high demand due to a series of recent accidents of power
stations and chemical plants in Japan. Thus, there is the need to monitor high-temperature members in large structures. The AE method offers advantages such as observing the progress ofplastic deformation and microscopic fracture in real time, locating a flaw using several AE sensors, and diagnosis capability without shutdowns. The AE method is used in a wide range offields, including the inspection of manufactured products, monitoring the safety of structures anddetecting natural disasters such as landslides [1]. The most common detection material for application in AE sensors is lead-zirconate titanate (PZT). Its Curie point, the temperature above
which a piezoelectric material loses its piezoelectricity, is approximately 300˚C. When an AE
sensor with a PZT element is applied in high-temperature environment, a waveguide and/or a
cooling device are essential and so it is difficult to measure AE correctly. Therefore, the development of a heat-resistant AE sensor that can be used above 300˚C is strongly desired.
Aluminum nitride (AlN) is a piezoelectric material, which has a piezoelectric strain constantd33 of 5.6 pm/V [2] and an ability to maintains piezoelectricity up to 1200˚C [3]. AlN is a promising material as the active element of a heat-resistant AE sensor without a cooling device.
However, the polarization of AlN cannot be controlled after preparation and sintered AlN polyJ. Acoustic Emission, 25 (2007)107© 2007 Acoustic Emission Groupcrystals exhibit no piezoelectricity. This had been a major hindrance to the commercializationof AlN piezoelectric devices. After trying several methods, we finally succeeded in making
highly oriented AlN thin films by rf magnetron sputtering [4].
In our previous studies [5-7], we developed both ordinary AE sensors and heat-resistant AE
sensors using the AlN elements. The sensor characteristics were evaluated at ambient temperature and at elevated temperatures. To evaluate of d33 and thickness of AlN elements on
sensor sensitivity, AlN elements with d33 from 2 to 7 pm/V and thickness from 3 to 9 m wereprepared. It is confirmed that the AE sensor sensitivity increased with d33 and thickness of AlN
elements [7]. The sensitivity of the heat-resistant AE sensor was also improved by a design ofthe sensor structure. The sensor characteristics were evaluated at elevated temperatures from 200
to 600˚C. It was confirmed that the AE sensor works well at 600˚C and does not deteriorate.
In this study, we developed a heat-resistant AlN AE sensor using AlN element of 9-m
thickness and Inconel-600 housing. The sensor characteristics were evaluated at elevated temperatures up to 700°C. Finally, using this AlN AE sensor, we succeeded in detecting AE signals from frequent cracking of the oxide scale produced by the molten salt attack (85% V2O5 +Na2SO4) at 700°C during cooling of the pipe.
Experimental Procedures and Results
Preparation of AlN Thin Films
AlN thin-film sensor elements were prepared on silicon single-crystal substrates (thickness:
0.625 mm) by rf magnetron sputtering. The sputtering conditions for the AlN thin film are reported in a previous study [4]. The thickness of AlN thin film was controlled by the sputtering
time. The full width at half-maximum (FWHM) of the X-ray rocking curves of the film deposited under the optimized sputtering conditions was 2˚ and the film was highly oriented along thec-axis. A platinum (Pt) thin film used as the upper electrode was also prepared by rf magnetron
sputtering. The crystal structure and orientation of the film were investigated by X-ray diffraction (XRD) analysis.

Fig. 1 Diagram and photo of a heat-resistant AlN AE sensor.
Development of Heat-resistant AlN AE Sensors
Disk-like AlN elements with 6-mm diameter were cut out from Si wafer for AE sensors.
The d33 value of each AlN element was measured using d33 PiezoMeter System (Piezotest). The
heat-resistant AlN AE sensors were developed using AlN element with high d33 values and108thickness of 9 μm. The AlN elements were selected and directly fixed to the wear plate (alumina)
by Ag paste just like an ordinary AE sensor. The diagram and the photo of the heat-resistant AlN
AE sensor are shown in Fig. 1. The metal housing was made of Inconel 600, which has highthermal and corrosion resistances. A heat-resistant coaxial cable was developed for connecting
the sensor to a preamplifier.
Evaluation of Characteristics of AlN AE Sensor
To measure the frequency responses of the AlN AE sensor, a commercial PZT AE sensor,
AE-900S-WB (NF Corporation), was used to generate quasi-AE waves. This AE sensor is a
wide-band model and possesses a constant sensitivity in the range of 100 kHz to 1 MHz (± 10dB). A preamplifier (9913, NF Corporation) with a 40-dB gain in the frequency range of 100 Hz
to 20 MHz was used for the AlN AE sensor. The AlN AE sensor was fixed to the PZT sensor
using an acoustic coupler, Gelsonic (Nihon Kohden Corporation). Single-cycle sine wave
(Vp-p: 20 V) was applied to the PZT sensor and the output from the AlN AE sensor was measured and expressed in dB (the ratio of the output to the input 20 V). The frequency of the sine
wave was varied from 100 kHz to 1 MHz by 10 kHz intervals.
The frequency responses are shown in Fig. 2. It showed a constant sensitivity across therange of 100 kHz to 1 MHz (± 5 dB) like the PZT commercial AE sensor. While the PZT commercial AE sensor has a damper for constant wideband sensitivity, the AlN AE sensor shows aconstant sensitivity without such a damper. The thickness of AlN element is 9 m so that theresonance frequency must be much higher than 1 MHz. That is the reason why the AlN AE
sensor shows a constant sensitivity, which is an advantageous feature of this heat-resistant AlN
AE sensor.

Fig. 2 Frequency responses of AlN AE sensor.
Evaluation of AlN AE Sensor at High-temperatures
Figure 3 shows a diagram of the experimental setup for AE sensor responses at elevated temperatures. A stainless steel rod (diameter: 20 mm, length 400 mm) was used as an AE waveguide,
which transmitted elastic waves from the PZT sensor outside an electric furnace. The waveguide
was cooled by water outside the furnace and the temperature of PZT sensor was maintained
under 60˚C. The responses of the AlN AE sensor were measured at ambient temperature, 200,
300, 400, 500, 600 and 700˚C after keeping the AlN AE sensor at each temperature for more109

Fig. 3 Experimental setup for AE sensor responses at elevated temperatures.
than 30 min. A tone-burst of 10 sine waves was generated and the output of AlN AE sensor wasobserved. A preamplifier was used for the AlN AE sensor.
The AlN AE sensor was coupled using an anti-seize paste (PBC® aka polybutylcuprysil,
http://www.kspaul.de/TDB-engl/MP-PBC-engl.pdf). This is semi-synthetic grease with metallic
powders and inorganic thickener. After high temperature exposure, it became powdery, but still
functioned as a couplant during cooling. The sensor was mechanically held with a ceramic
spring.
Figure 4 shows a generated envelope of a tone-burst wave with 10-cycle sine waves of 200
kHz, and the response signals of the AlN AE sensor at 300, 500 and 700ºC. It is confirmed thatthe sensitivity of the AlN AE sensor is maintained from ambient temperature up to 700ºC.

Fig. 4 Generated tone-burst wave (a) with 10 sine waves of 200 kHz and the response signals of
AlN AE sensor at (b) 300º, (c) 500º and (d) 700ºC.
110

Fig. 5 Experimental setup for AE monitoring of molten salt attack.
AE Monitoring of Molten Salt Attack by AlN AE Sensor
Accelerated oxidation of heat-resistant alloys by molten salt is well known as the most dangerous damage, and often called as catastrophic oxidation. We measured AE signals by molten-salt attack (85 mol% V2O5 + Na2SO4) by using the AlN AE sensor. We used a similar experimental setup reported previously [8]. Figure 5 shows an experimental setup for molten-salt
attack and AE monitoring method of a hot stainless steel pipe (type-304) of 34-mm diameter,
3.0-mm thickness and 1200-mm length. The PZT sensor (AE-900S-WB) was also mounted as areference on the pipe end, where the temperature was lower than 40°C. Preamplifiers (2/4/6,
PAC) with 40-dB gain were used for the AlN AE and PZT sensor. We used an AE monitoring
system (PAC, DSP PCI-2). We placed 0.1 g of a mixed salt (85 mol% V2O5+Na2SO4) on theupper portion of the pipe at the center of the furnace and then heated the pipe.
Figure 6 shows cumulative AE hits of the AlN sensor and temperature of the pipe in the furnace. The pipe was heated to 700ºC and kept for 3 hours, then the power of the furnace wasturned off and the pipe was allowed to cool. The threshold for AE hits of the AlN AE sensor
was set at 10 mV. We detected few AE hits during heating and holding, but monitored frequent
AE hits during cooling. The similar AE during cooling was observed in a previous study using
optical fiber sensors [8]. Therefore, the AE hits must be caused by frequent cracking of the oxideFig. 6 Cumulative AE counts detected by the AlN AE sensor with temperature after molten-salt
attack.
111

Fig. 7 Waveform of AE detected by AlN AE sensor.

Fig. 8 FFT of waveform in Fig. 7.

Fig. 9 Same signal as Fig. 7 by PZT sensor.
112

scales (and possibly salt cracks at lower temperatures) produced by the molten-salt attack duringcooling of the pipe. A typical AE waveform is shown in Fig. 7. The power spectra of the AE(Fig. 8) showed many peaks below 100 kHz and a few peaks around 110, 240 and 360 kHz. The
latter two appear to be due to F(1,2) and F(2,2) cylindrical waves from the cracking sources
propagating on the pipe [9]. When the same signal (Fig. 7) was detected by the PZT sensor outside the furnace, the dispersion of cylindrical waves stretched the signal by several times, as
shown in Fig. 9.
We have not tested for the durability on this AlN AE sensor, but it worked at 700ºC for atleast several hours for these experiments. The high-temperature couplant remained effective. It is
confirmed that the AlN AE sensor detected AE signals from the fracture of the oxide scales produced by the molten-salt attack at 700°C during cooling of the pipe.
Summary
We developed a heat-resistant AE sensor using AlN thin films and detected AE signals frommolten-salt attack of Type 304 stainless steel pipe. Aluminum nitride (AlN) is a promising
acoustic emission (AE) sensor element for high-temperature environments such as gas turbinesand other plants because AlN maintains its piezoelectricity up to 1200°C. Highly c-axis-oriented
AlN thin-film sensor elements were prepared on silicon single crystals by rf magnetron sputtering. The heat-resistant AE sensors were developed using AlN elements with high d33 values andthickness of 9 μm. The sensor showed a constant sensitivity across a wide band of frequencies in
the range of 100 kHz to 1 MHz at an ambient temperature. The sensor characteristics were
evaluated at elevated temperatures up to 700°C. It was confirmed that the AE sensor works wellup to 700°C and does not deteriorate for at least several hours. The AE sensor detected AE signals from frequent fractures of the oxide scales produced by the molten salt attack (85% V2O5 +Na2SO4) at 700°C during the cooling of the pipe.
Acknowledgment
A part of this study was supported by Industrial Technology Research Grant Program in 2004
from New Energy and Industrial Technology Development Organization (NEDO) of Japan.
References
1. A. Chichibu, K. Jo, M. Nakamura, T. Goto and M. Kamata, Proc. 9th International Acoustic
Emission Symposium, Kobe, Japan, 1988, pp. 92-97.
2. I.L. Guy, S. Muensit and E.M. Goldys, Applied Physics Letters, 75, 1999, 4133-4135.
3. R.C. Turner, P.A. Fuierer, R.E. Newnham and T.R. Shrout, Applied Acoustics, 1994, 41,
299-324.
4. M. Akiyama, C. N. Xu, K. Nonaka and T. Watanabe, Thin Solid Films, 315, 1998, 62-65.
5. H. Noma, K. Kishi, N. Ueno, M. Akiyama and S. Takahashi, The Society of Instrument andControl Engineers (SICE) 21st Sensing Forum, 2004, pp. 51-54

113

6. E. Ushijima, H. Noma, K. Kishi, N. Ueno and M. Akiyama, The 3rd U.S-Japan Symposium on
Advancing Applications and Capabilities in NDE, Hawaii, 2005, pp. 139-142.
7. H. Noma, E. Ushijima, Y. Ooishi, M. Akiyama, N. Miyoshi, K. Kishi, T. Tabaru, I. Ohshima,
A. Kakami and T. Kamohara, Proc. 27th European Conference on Acoustic Emission Testing(EWGAE 2006), Cardiff, 2006, pp. 111-116.
8. T. Hayano, T. Matsuo, H. Cho and M. Takemoto, Progress in Acoustic Emission XIII, 2006,
JSNDI, pp. 205-211.
9. H. Nishino, K. Yoshida, H. Cho and M. Takemoto, J. Acoust. Soc. Jpn., 63, 2007, 13-20.

114

DAMPING, NOISE, AND IN-PLANE RESPONSE OF MEMS ACOUSTICEMISSION SENSORSAMELIA P. WRIGHT, WEI WU*, IRVING J. OPPENHEIM and DAVID W. GREVE*
Dept. of Civil & Environmental Engineering, *Dept. of Electrical and Computer Engineering,
Carnegie Mellon University, Pittsburgh, PA 15213

Abstract
Resonant sensors for acoustic emission detection have been designed and fabricated as
MEMS capacitive transducers with resonant frequencies between 100 and 500 kHz. We reportfour recent advances in our understanding of their mechanics and in the implications of thoseadvances for improved sensitivity. One advance involves a successful laboratory method to seal
and evacuate the MEMS device within its ceramic package, thereby operating in a coarse vacuum and reducing or eliminating squeeze film and radiation damping effects; we present characterization measurements showing an approximate fourfold increase in quality factor Q. A second
advance is a summary of our theoretical analysis of noise sources for a resonant, capacitive
MEMS transducer; we report that Brownian noise associated with the impact of air molecules is
the major source. A third advance is the use of a grillage of beams, rather than a perforated
plate, as the moving plate in the spring-mass system; we present characterization measurements
showing a significant reduction in damping and therefore a higher Q. The fourth advance is afinger-type mechanism to sense in-plane motion; we show characterization measurements confirming the resonant behavior of that device and showing that the in-plane device has a muchhigher Q than comparable out-of-plane devices.
Keywords: Damping; in-plane motion; MEMS; sensor innovation.
Introduction
Our research group has developed a series of MEMS devices to function as resonant transducers sensitive to out-of-plane motion. Their mechanics and their use as acoustic emission sensors are most completely described in a paper by Ozevin et al. [1]. The transducers are fabricated in the PolyMUMPS surface micromachining process as spring-mass systems to form capacitors in which the moving plate is an elastic structure in polysilicon with a thickness of 2 μm.
We typically place on each chip a suite of transducers at different frequencies in the range up to
500 kHz, placing four transducers on a 5  5 mm chip or a larger number of transducers on a 10
 10 mm chip.
Figure 1a shows a completed four-channel AE sensor system [2, 3]. It consists of a MEMS
chip, nominally 5  5 mm, containing four independent transducers with resonant frequencies in
the range between 126 and 500 kHz. The chip is mounted in a Spectrum Semiconductor Materials CPG06856 pin-grid array ceramic package, 26  26 mm, chosen because it provides a smoothbottom surface for coupling to structural plates. The ceramic package engages a bottom PC
board and Sullins 2.0 mm connectors engage a bottom PC board containing four amplifier circuits, as shown in Fig. 1a, each with a nominal gain of 100 V/V (40 dB). The whole system(apart from the cable connector) is contained in a volume of 35  35  30 mm.

J. Acoustic Emission, 25 (2007)115© 2007 Acoustic Emission GroupVacuum Sealing of a Perforated Plate Transducer
Among various mechanisms limiting the sensitivity of such transducers, the damping effectof air is very significant. Damping occurs both from acoustic radiation into the air and fromsqueeze-film damping as air is forced through the gap between the moving plate and stationary
plate. The effectiveness of a resonant transducer is related to its sharpness of resonance, or(equivalently) to its dynamic magnification, and is commonly calculated as the quality factor Q.
Operation in coarse vacuum would reduce the damping effects and increase Q, and we report apractical laboratory method for sealing and evacuating the chip in its ceramic package.

125 um

125 umFig. 1a. Four-channel AE sensor system in 35  35  30 mm volume.
1b. CAD layout and section of one unit.
1c. Admittance at resonance, f = 443 kHz, Q = 5.5 (atmospheric), Q = 19.7 (evacuated).
Figure 1b is the CAD layout and section of a single unit, showing a perforated square plate(125 m on a side, with 3.5-m square etch holes on a triangular grid with 8 m between holes)
suspended by four flexural springs, with a gap of 1.25 m between the plate and the underlying
stationary electrode. FEM simulation was used in selecting the flexural spring length to achievetarget design frequencies between 126 and 500 kHz. Each transducer consists of 144 units (a 12 12 array) to obtain a target design capacitance near 13 pF. Figure 1c shows the admittance plot
for the transducer with nominal design frequency of 500 kHz, and resonance is observed near
443 kHz. The admittance plot shows the sharpness of resonance before and after the process ofsealing and evacuation. The broader resonance in Figure 1c is the admittance plot at atmosphericpressure, corresponding to a Q of 5.5.
For the different transducers (different resonant frequencies) the admittance measurements at
atmospheric pressure showed Q factors ranging from 1.4 to 5.5, and the measured Q generally
increased with transducer frequency, as predicted [1]. In an attempt to make the transducers
more sensitive, we next developed a laboratory method to seal and evacuate the device within its
package. The CPG06856 ceramic package product has a matching lid, plated with nickel andgold. In our method, a hole is drilled in the solder seal lid, the perimeter of the lid is soldered to
the CPG06856 package, and a small amount of solder is dropped around the hole. The package
is then moved to a vacuum chamber, within which a soldering iron can translate on a verticalaxis. After the chamber is evacuated, the soldering iron is energized and the solder around thehole is melted, sealing the hole in the lid. After the solder cools, the package is removed fromthe vacuum chamber. This method has proven reliable and the resulting seal appears to be durable.
116

The sharper resonance in Fig. 1c is an admittance plot repeated 28 days after sealing, showing no loss of vacuum, and corresponding to a Q of 19.7. The increase observed in Q, approximately fourfold, is helpful in increasing the expected sensitivity of the transducer. However, it
immediately raises the question, what factor is limiting the Q? The observed Q is substantiallylower than that typically observed in polysilicon resonators; for example, other researchers havebuilt resonators in the PolyMUMPS process with a measured Q value between 300 and 550 [4].
It is likely that the observed Q is limited by imperfect coherence in the response of the 144 units
that comprise the transducer. The 12  12 array occupies an area less than 2-mm square, whichis small compared to the ultrasonic wavelength in steel at the frequencies (below 500 kHz) of ourtransducers, and therefore it is reasonable to expect that an arriving mechanical excitation will
uniformly drive the units in the array. It is more likely that the 144 units do not have identicalresonant frequencies. For example, the stiffness of the anchors, assumed to be rigid, will differ
from the perimeter of the array to the interior, creating one source for slight deviations in theresonant frequencies. Another likely source of variation in resonant frequencies is variation in
the spring constant due to the manufacturing variation in the polysilicon beam width. The effectof imperfect coherence would be a “spreading” of the aggregated peak, which appears as abroader peak in admittance measurements.

Fig. 2. Pencil lead break response, comparing three MEMS sensors to PAC R15 sensor.
We applied pencil lead breaks on a large steel plate, 9.5 mm thick, with the MEMS system(Figure 1a) and a PAC R15 sensor mounted symmetrically 20 mm from that source. Figure 2
shows the response of three sensor channels on the MEMS device, after onboard amplification,and the raw response of the PAC R15 sensor; for each MEMS channel we indicate in Figure 2 its
design frequency and its bias voltage. Signal strength from the sensor channels on the MEMS
device is comparable to that from the PAC R15 sensor, although the SNR is not as favorable.
Noise Analysis
We recently developed a theoretical analysis of noise sources in the electromechanical behavior of a resonant, capacitive-type transducer. We examine both Johnson noise from thermal
agitation of electrons and Brownian noise from impact excitation of the moving plate by air
117

molecules. The RMS noise current of Johnson noise is independent of frequency (white noise)
and is given by [5]iRMS = G 4k BTf / R ,where G is the amplifier gain, kB is the Boltzmann constant, T is the absolute temperature, R is
the resistance at the amplifier input, and f is the bandwidth. We determine that Brownian noise,for example as calculated by Gabrielson [6], is the other mechanism of interest. The spectral
density of the squared magnitude of the force [N2/Hz] caused by air molecules striking a suspended plate with resonant frequency 0 is independent of frequency and is given by [6]FB ( )

2

= 4k BTm0 / Q ,where m the mass of the diaphragm and 0/Q is the mechanical damping coefficient. The equation of motion for the displacement x(t) of the diaphragm is

x +

0
F (t)
x +  02 x = B ,mQ

where FB(t) is the Brownian force. Taking the Fourier transform and solving for the velocity
spectral density, V()=jX(), gives

V ( ) =FB ( ) / m
 0  02
+
j +
Q
j

from which we find for the ensemble average

V ( )

2=

FB ( ) / m

222       0  +  0 
  Q

2.

The current in the external circuit is related to velocity spectral density throughd (CVDC )dC VDC C0
= VDC
=
v(t )
dt
dt
g

i (t ) =where VDC is the applied DC bias, g the gap between plates, A the plate area, and C0=0A/g is thecapacitance between the plates. Consequently, in the frequency domain
I ( )

2= (VDC C0 / g ) 2 V ( )2and integrating over the amplifier bandwidth we have for the RMS noise current
2

iRMS

2

1  VDC C0  H 4k BT0 /(mQ)=0   2 2   2 d .
2  g   0 +  0QThe argument of the integral captures the dynamic response of the resonator to air molecule
impact, and therefore shows a dependence upon the quality factor Q, together with the influenceof amplifier bandwidth. However, if the amplifier bandwidth H encloses the resonant peak,during contour integration (not shown) the upper limit of the integral can be extended to infinity.
In this case, the result,
118

2

i RMS

2

V C  k T=  DC 0  B , g  mis almost independent of Q. Physically, increasing the resonator Q increases the velocity resulting from a particular collision. However the width of the resonance decreases with Q, resulting
in an RMS noise level that is independent of Q. The noise thus consists of a frequencyindependent component due to Johnson noise and a peaked component due to Brownian noise.
Table 1 shows the predicted noise voltage at the output of the amplifier for an amplifier gainof 100 V/V and in input resistor of 160 k. The predicted RMS noise voltage is independent of
Q. (Our devices have the same area and gap, and the only other parameter leading to a different
noise prediction would be the DC bias voltage, which was 9 V for all transducers). Also shownis the measured RMS noise voltage for three transducers with an amplifier of bandwidth 800
kHz. The measured and predicted noise voltages confirm the deduction from the noise analysis,that the RMS noise voltage is independent of Q. This suggests that the performance of the transducer is limited by the combination of Brownian and Johnson noise, rather than amplifier noise
and interference.
Table 1. Predicted and measured RMS noise at output of amplifier.
Frequency
Measured
[kHz]
126
351
500

Q
Measured
1.4
4.8
5.5

Brownian Noise
Predicted
[mV]
12
12
12

Johnson Noise
Predicted
[mV]
5
5
5

Noise
Measured
[mV]
14
22
19

Grillage Transducer for Out-of-Plane Motions
We report on a new transducer mechanism, fabricated in 2007, an out-of-plane sensor with adesign frequency of 250 kHz. The design features a moving plate constructed as a grillage rather
than as a perforated plate with periodically spaced etch holes. We show characterization measurements suggesting two advantages to the grillage geometry. Capacitance measurements andFEM simulations show the grillage to approximate a whole plate in its electrical behavior, andadmittance measurements show the grillage to have higher Q (lower damping) than a comparable
perforated plate.

Fig. 3a. CAD layout of grillage unit for out-of-plane sensing.
3b. C-V plot, C0 = 10.9 pF.
3c. Admittance at resonance, f = 160 kHz, Q = 6.7.
119Figure 3a is the CAD layout of a single unit, showing a grillage (outside dimensions 138 
140 m) supported by four flexural springs; the beams forming the grillage are 3 m in width,with a clear spacing of 3 m between beams, and a gap of 1.25 m between the grillage and theunderlying stationary electrode. FEM simulation was used in selecting the flexural spring length
to achieve the target design frequency of 250 kHz. The transducer consists of 72 units (a 9  8array) to obtain a target design capacitance, C0, of 5.8 pF, calculated based upon the net area of
the grillage. Figure 3b shows the C-V plot, referring to the capacitance as a function of applied
DC voltage. A capacitive spring-mass system should show an increase in capacitance with applied DC voltage, because the electrostatic attraction force will deflect the system, reduce thegap, and thereby increase the capacitance. The C-V plot confirms the expected behavior of thetransducer, but it indicates a C0 near 10.9 pF. Subsequent FEM simulations show that the capacitance closely approximates that of the gross area of a whole plate rather than the net area of agrillage; in other words, the “cutouts” between grillage beams do not diminish the capacitance.
Figure 3c shows the admittance plot in the vicinity of resonance, which is observed to occur near
160 kHz. (The difference between predicted and observed frequency is attributed to support
flexibility and to fabrication deviations in the spring width.) Figure 3c depicts the sharpness ofresonance, from which a Q near 6.7 is extracted. A comparable perforated plate transducer (asdepicted in Fig. 1b) with a resonant frequency of 182 kHz displayed a Q near 2.0, so the grillage
geometry represents substantial improvement. (Considering squeeze-film and radiation damping
in air, by theory, Q will increase with frequency, and comparisons must be taken between transducers at comparable resonant frequencies.) Compared to a perforated plate, we predicted thatthe grillage geometry would decrease damping by permitting freer venting of the air beneath thegrillage, reducing squeeze- film damping; we interpret these results as evidence qualitatively
confirming that prediction.

Fig. 4: Pencil lead break response of out-of-plane sensor.
Figure 4 shows the characteristic response of the out-of-plane sensor to a pencil-lead breakapplied directly to the ceramic package, which is consistent in appearance with the higher Q
demonstrated by the device.
Finger Transducer for In-Plane Motions
Finally, we report on another new 2007 transducer, designed to sense in-plane motion. It is afinger-type (comb-type) capacitive transducer with a design frequency of 250 kHz. The predicted Q is much higher (predicted damping is much lower) than for out-of-plane sensors,120because in-plane motion mostly produces a direct shearing of air in the gap, rather than asqueeze-film or radiation actuation of the air. At the same time, squeeze-film damping in theout-of-plane direction is used beneficially to isolate the desired in-plane mechanical response
from the unwanted out-of-plane response.

Fig. 5a. CAD layout of finger-type transducer to sense in-plane motion in the y-direction.
5b. C-V plot, C0 = 2.95 pF. 5c. Admittance at resonance, f = 176 kHz, Q = 197.
Figure 5a is the CAD layout of a single unit (128  132 m) showing a stationary central
spine with projecting fingers, which form capacitors in relation to fingers that project from aframe supported by four flexural springs and oriented to sense motion in the y-direction; thepitch between fingers is unsymmetrical in order to effect a change in capacitance during motion.
Again, FEM simulation was used in selecting the flexural spring length to achieve the target design frequency of 250 kHz, and to calculate the predicted capacitance C0, which is significantly
and beneficially influenced by the effects of fringe capacitance. The transducer consists of 532
units (a 19  28 array) to obtain a target design capacitance, C0, of 3.1 pF. Figure 5b shows the
C-V plot, confirming the expected behavior of the transducer with a measured C0 near 2.95 pF.
Figure 5c shows the admittance plot in the vicinity of resonance, which is observed to occur near
176 kHz, and the sharp resonance corresponds to a Q near 197. The characterization measurements confirm the design characteristics outlined above, and suggest that the transducer may
provide a practical approach to sensing in-plane particle displacements. In principle, the transducer can be fabricated on one MEMS chip together with a similar transducer orthogonal to it,
along with a third transducer sensitive to out-of-plane motion, creating a sensor system responding to the three-dimensional components of particle motion.

Fig. 6: Pencil-lead break response of in-plane sensor.
121

Figure 6 shows the response of the in-plane sensor to a pencil-lead break applied directly tothe ceramic package. The beat frequency, near 8 kHz, is consistent with a slight difference infrequency between interior and exterior units in the 19  28 sensor array; that frequency difference can be explained by a small difference in their spring lengths, possibly influenced by difference in anchor rigidity. The beat effect is visible only because the in-plane device is a high-Q
resonator.
Summary
We have described four recent advances in our understanding of the mechanics of capacitive
MEMS transducers resonant in the range between 100 and 500 kHz. We seek to improve thesensitivity of these transducers as acoustic emission sensors, and the advances provide insights
for those improvements. Among other factors, damping and noise limit transducer sensitivity,
and all four advances guide us to better sensitivity. We have shown an effective laboratory approach for sealing and evacuating a device, thereby reducing the squeeze film and radiation
damping effects of air. Characterization experiments show an approximate fourfold increase inquality factor Q, and response of the sensor in pencil lead break testing, after onboard amplification, is compared to the response of a commercial transducer. We have also summarized our recent theoretical analysis of noise, identifying the effective floor to result from Brownian motion
and the impact of air molecules with the resonator, confirmed by our measurements. We havereported a significant reduction in squeeze-film damping, in air, when using a grillage of beams
as the moving plate rather than a perforated plate with periodically spaced etch holes. Finally,
we have reported our first characterization measurements of a new transducer designed to sense
in-plane motion. Those results confirm its design characteristics, and show it to be minimallydamped (in air) because the in-plane motion mostly produces a shearing of an air volume rather
than a squeeze film or radiation excitation of the air volume.
Acknowledgements
This work is supported by the National Science Foundation, Grant CMS-0329880 entitled
SENSORS: Collaborative Research: MEMS for Multi-Mode Civil Infrastructure Sensing, and by
the Pennsylvania Infrastructure Technology Alliance. We gratefully acknowledge contributionsof our research collaborators at Lehigh University, Professor S. P. Pessiki and Dr. D. Ozevin,who is now a Research Scientist with Physical Acoustics Corporation. The opinions, findings,
conclusions, and recommendations expressed in this material are those of the current authors anddo not necessarily reflect the views of the sponsors or of our collaborators.
References
1. D. Ozevin, D. W. Greve, I. J. Oppenheim, and S. P. Pessiki, Resonant capacitive MEMS
acoustic emission transducer, Smart Mater. Struct., 15, 2006, 1863-1871.
2. D. W. Greve, I. J. Oppenheim, and W. Wu, Modes and Damping in Cmut Transducers for
Acoustic Emission, IEEE Ultrasonics Conf., 2006.
3. D. W. Greve, I. J. Oppenheim, W. Wu, and A. P. Wright, Development of a MEMS Acoustic
Emission Sensor System, SPIE Smart Structures/NDE Joint Conf., 2007.

122

4. B. Bahreyni, C. Shafai, Fabrication of Piezoresistive Sensors in Standard MEMS Foundry
Processes, IEEE Sensors Conf., 2005.
5. S. D. Senturia, Microsystem Design, Springer, 2001, 438.
6. T. B. Gabrielson, Mechanical-thermal noise in micromachined acoustic and vibration sensors,
IEEE Trans. Electron. Devices, 40, 903–909.

123

IMMERSION-TYPE QUADRIDIRECTIONAL OPTICAL FIBERAE SENSOR FOR LIQUID-BORNE AE†
TAKUMA MATSUO†, HIDEO CHO, TAKESHI OGAWA
and MIKIO TAKEMOTO
Department of Mechanical Engineering, Aoyama Gakuin University,
5-10-1, Fuchinobe, Sagamihara, Kanagawa 229-8558, Japan

Abstract
Immersion-type AE sensor is needed for corrosion monitoring of underground fuel tanks. We
have developed a multi-directional optical fiber AE sensor to monitor the longitudinal AE signal
through liquid (liquid-borne AE). The developed AE system is an optical-fiber-based MachZender-type laser interferometer using a weak laser and is intrinsically safe. Multi-sensing function was facilitated by winding an optical fiber on four small-size short hollow pipes with different diameters, which were installed on the inside walls of a box of 22.5-mm square and 230-mm
height. Each sensor element was screwed on a different sidewall. The four fiber sensors werecascaded in series on a single fiber and monitored the liquid-borne AE signals from quadridirection simultaneously via frequency discrimination. As the each fiber-wound sensor element monitors at the resonant frequency of the pipe, we can estimate the direction of AE signals using theirfrequency spectra and wavelet transform. The functionality of the sensor was demonstrated by
50-day monitoring of liquid-borne AE signals from rust fractures on four corroded carbon-steel
sidewalls of a water tank. The immersion-type quadridirectional sensor successfully monitoredliquid-borne AE signals from the four corroded plates.
Keywords: Optical fiber AE sensor, intrinsically safe sensor, immersion-type sensor, quadridirectional sensor, rust fracture, parabolic law
Introduction
Integrity loss of underground fuel tanks by localized soil corrosion is becoming serious problems in Japan. Fire and Disaster Management Agency (FDMA) reports leak accidents of 344
tanks in 2001. Localized corrosion was the cause of leaks in 30% of the tanks [1].
AE technique is expected to be a useful tool for monitoring corrosion damages of the tank,however we cannot use the conventional AE system using the piezo-sensors, since the system is
not intrinsically safe.
We are developing an optical fiber laser interferometer to monitor AE signals [2]. Thissystem is composed a homodyne Mach-Zender-type laser interferometer with a phasecompensation feedback circuit. The sensor of this system uses a telecommunication singlemode optical fiber. This system is intrinsically safe, immune from corrosion and electromagnetic
noise. It was previously used to monitor liquid borne P-waves by immersing the sensing fiber
into the liquid [3]. We have improved this sensor so it can monitor the corrosion zone or the
direction of the P-waves over a long time. Here, a single sensing fiber was spirally wound on
four pipes, and detected AE signals quadridirectionally.
† A Student Award winner at the 50th Acoustic Emission Working Group Meeting, November 2007.
J. Acoustic Emission, 25 (2007)124© 2007 Acoustic Emission GroupWe first introduce our new quadridirectional AE sensor and next AE monitoring from four
plates with rust (corroded steel plate samples) for 50 days. We discuss relationship between the
AE activity and corrosion of the steel with rust.
Immersion-Type Optical Fiber Sensor
A sensing box developed is shown in Fig. 1. The box was designed for underground gasoline
tanks and contains four AE sensors. Box size is 22.5-mm square and 230-mm height. The top
and bottom plug-plates were thick enough so that four aluminum sidewalls were screwed on withspecial polymer sheet as gasket. This structure can prevent the vibration interference of neighboring walls and gasoline leakage into the box. A hollow long pipe of 16-mm diameter wasscrewed into the top plug-plate. This pipe is used to insert the sensing box in the tank through a
32-mm diameter nozzle, and to connect the optical fiber to the monitoring system.
Sensing fiber was wound on four cap-shaped aluminum cylinders of 20-mm height (called
sensor elements) each with a different diameter (8, 10, 15 or 19 mm). These elements werescrewed on the sidewalls with a polymer sheet. The sensing fiber was wound on the holders multiple times. This structure improves the sensor sensitivity significantly. Four sensors were connected by a single fiber in series and connected to the monitoring system via fiber connectors.
The four sensors detect the wall vibration excited by liquid-borne P-waves. Here the different
resonance frequencies of four sensors are utilized to identify the direction of the P-waves. We
call the sensor with 15-mm diameter element as “D15 sensor” and the entire system as ITQS
(Immersion-Type Quadridirectional System), hereafter.

Fig. 1 Immersion-type quadridirectional AE sensor using optical fiber.
Directionality of ITQS was studied using a rectanglar water container of 1220 x 700 x 250
mm. Water-borne P-wave was produced using a PZT transmitter (DE, S140B) immersed in water at 1000 mm from the ITQS. We fixed the transmitter and rotated the position of the ITQS
counter-clock-wise. Here  = 0º designates that the normal of the sidewall with the D19 sensor
orients to the transmitter; D10 faces the transmitter at 90º, D15 at 180º and D8 at 270º.
125Figure 2 show examples of the waveforms and power spectra at  = 0º, 90º, 180º and 270º.
These are waveforms detected by the sensor, whose sidewall faces the transmitter. Resonancefrequencies of the sensors were separated with frequency separation of more than 15 kHz.
We next studied relationship between angle  and wave amplitude. Figure 3 shows examples
for the P-waves from 30º, 45º and 60º. These angles are between the D19 sensor and D10 sensor.
We observed two or three wave packets. Power spectra show two higher peaks at around 42 and95 kHz. Using the normalized amplitude profiles of two peaks of Fig. 4, we can estimate thedirectivity of the P-wave sensing as ±30º.

Fig. 2 Waveforms and frequency spectra of AE signals at  = 0º, 90º, 180º and 270º.

Fig. 3 Waveforms and frequency spectra of AE signals detected by ITQS at  = 30º, 45º and 60º.

126

Fig. 4 Amplitude profile as a function of rotating angle of ITQS.
Monitoring of Water Borne P-wave From Rust Fracture
Experimental Setup
We monitored AE signals from rust fracture using the ITQS for 50 days. Experimental setup
is shown in Fig. 5. We inserted four steel plates in the four side walls of a rectangular watercontainer, 630 x 450 x 440 mm. Four steel plates with natural rust produced by atmosphericcorrosion were taken from a tank yard in a sea coast area. As these samples have rusts on one
side, the samples were placed on the sidewalls using silicone rubber sealant such that the rusted
surfaces face outward. Sample size, area of rust, rust thickness and corrosion method are shownin Table 1. Rust area is different among the samples, but we used them in as-received condition,
since the rust tended to separate during cutting.
These samples were further corroded in a room without air conditioning using different
methods in Table 1. Sample A with the thickest rust was exposed to both thermal cycles (heating
to 23oC and natural cooling to room temperature) and mist spraying of 5% NaCl solution to therust. By coating silicone grease, other parts were not corroded. Sample B was mist sprayed with
5% NaCl solution without thermal cycles. Sample C was exposed to the thermal cycles. Thermal
cycling and mist spray were not given to sample D. Here, the thermal cycles were given byturning on-off of an infrared lamp at 6-hours intervals. The mist was sprayed for 60 minutes(total spray amount 66 ml for sample A, 24 ml for sample B) at 8-hour intervals. Sample A was
exposed to the severest corrosion environment and sample D to the least. These combinations
were selected to simulate the corrosion condition, which each sample was supposed to be
exposured.
Table 1 Specimen size, area of rust and corrosion test method.

127

Fig. 5 Experimental setup for monitoring AE from rust fracture of corroded four plates.
The container was filled with water. A trace of corrosion inhibitor was added to prevent wet
corrosion of the inner surfaces of the samples. The boxed AE sensor was immersed with its
bottom at 200-mm height. Experimental setup is shown in Fig. 5. We also mounted PZT-type AE
sensors (PAC, Type PICO) on sanded parts of the samples to examine the timing and amplitudeof water-borne P-waves and Lamb waves. Outputs of the PZT sensors were amplified 40 dB,
while those of the ITQS were not amplified but used a 20-150 kHz bandpass-filter. AE
monitoring was started on February 27, 2007 and continued to April 19 for 51 days. We
simultaneously measured room temperature and temperatures of samples A and B.
AE activities
The left of Fig. 6 shows change of room temperature and cumulative AE events counts of
the ITQS. Here one cycle of the temperature corresponds to one day, and the cumulative event
counts mean all events from four samples, since the four sensors were connected on a single
fiber in series. We had two cold days on March 25 and April 9. We observed four fairly large
step-wise increases, as marked by four ellipses. Step-wise increases of number 2 and 3 coincidewith large temperature changes, but two others (1 and 4) were at higher temperatures. It isnoteworthy that less AE signals were emitted for about ten days from 38 to 47th days.
We believe that the rust fracture will be accelerated by temperature changes of the member.
Thus, we studied relations between the temperature change of the samples A and B and event
counts, since AE signals were supposed to be mostly generated by these samples. As shown in
the right of Fig. 6, we observed a fairly good relation between the plate temperature and event
counts. Stepwise increases of AE events tend to occur when the plate temperature is at high or
low. Stepwise increases of No. 1, 3 and 4 occurred at higher temperatures and that of No. 2 at a
low temperature. However, very few AE signals were emitted at 3.7 Ms (42nd day) in spite of
low temperature. This appears to be due to capricious nature of living rust, which often makes
corrosion monitoring of tanks difficult. It is also noted that the corrosion rate cannot be estimatedfrom AE data monitored for one or two hours.
128

Fig. 6 Change of room temperature (left) and plate A, B (right) and cumulative AE counts by
optical fiber AE system.
Direction of the P-wave
We next estimated the direction of the P-wave. Figure 7 shows typical examples of AE signals. Event I was classified as the AE from the sample A due to its strong frequency component
at 45 kHz (D19 sensor). It counted 936 events. In the same manner, event II was from the sample
B (by D10 sensor). We observe two or three wave packets in events III (D15 sensor) and IV (D8sensor). Two peaks at 40 kHz and 60 kHz of event III suggest simultaneous emission from thesample C and A, respectively. Two packets of the event IV suggest two AE signals from thesample D due to frequency component at around 130 kHz.
The left of Fig. 8 shows changes of classified events with time. Sample A with large thickrust produced many AE signals when exposed to severe corrosion, but sample D, exposed tomild environment, less AE signals. These are expected result, and nothing new. However, it isnoted that the event count of sample C, exposed to the thermal cycles, is large.

Fig. 7 Waveforms and their frequency spectra detected by four optical fiber sensors.
We next compared the event counts per unit rust area. The right of Fig. 8 shows change ofclassified event counts per 100 cm2 from the samples A, B, C and D. There are some interestingfindings. These are:129

Fig. 8 Cumulative AE counts (top) and changes of classified event counts per 100 cm2 from four
samples (bottom).
1) Cumulative event counts, N, increase with time, t, according to the parabolic law, i.e.,
N=No(t-to)n, where n<1. This law is common to high temperature oxidation of metals
covered by protective oxide films. Small No and n indicate a protective film. Steps in aparabolic oxidation curve indicate the rapid oxidation due to partial breakdown of the film.
These correspond to the stepwise increase of AE signals due to fractures of the rust.
2) Incubation time, to , becomes longer for the samples exposed to mild corrosion environment.

1303) There are slight time lags in the timing of steps for samples A, B and C, suggesting that the
AE activity changes depending on the rust history and corrosion environment. Step height ofsamples A and B with thicker rust are larger than that of sample C.
4) In spite of non-corrosive environment, sample C shows higher emission rate.
Next we determined two parameters: No and n by curve fitting of the time-shifted data. Herethe parameters were decided so that the parabolic curves pass through the bottom of the steps, as
shown in the right of Fig. 8. Due to the limited data for only 50 days, correct determination was
impossible, but tentative values were estimated. The parameter No increases in the order of samples DCBA, indicating corrosiveness and rust nature. The parameter n presents the role ofrust on the corrosion rate. Here, it is noted that the rust plays two roles. One is protection of thebase metal as a diffusion barrier of water and oxygen. Another is acceleration effect of corrosion
by lowering the acidity of impregnated water. The latter is enhanced by chloride ions. It is noted
the sample A, with thicker rust and exposed to severe corrosion environment, shows n= 0.68, andalmost the same values for the samples B (0.685) and C (0.7). This suggests the rust on sample
A is playing a protection role.
Conclusion
We developed a quadridirectional AE system and monitored liquid-borne AE signals fromfour corroded plates which were exposed to different corrosion environment.
1) Immersion-type quadridirectional sensor was developed. Direction of AE can be identified
by the frequency characteristics of four sensor elements with the directivity of ±30º.
2) The sensor monitored the water-borne P-wave from rust fracture.
3) We observed good relations between AE activity and rusted plate temperature. Stepwise
increases of AE events tend to occur when the plate temperature is at higher or lower .
4) AE events increased with time according to a parabolic law: N=No(t-to)n, suggesting
protective nature of the rust.
References
1. Corrosion Center News, 29, Japan Society of Corrosion Engineering, 2004.
2. H. Cho, R. Arai and M. Takemoto, J. Acoustic Emission, 23, 2005, 72-80.
3. T. Matsuo, H. Cho and M. Takemoto, Progress in Acoustic Emission XIII, IAES-18, 2006,
311-318.

131

A SIMPLE METHOD TO COMPARE THE SENSITIVITY OF
DIFFERENT AE SENSORS FOR TANK FLOOR TESTING
HARTMUT VALLEN, JOCHEN VALLEN and JENS FORKER
Vallen-Systeme GmbH, 82057 Icking, Germany

Abstract
AE testing of atmospheric storage tanks filled with liquid becomes more and more accepted.
Sensors are attached to the tank wall and shall differentiate smallest amplitudes in the 25-45 kHz
frequency range from the background noise. Different sensor models are offered for thisapplication. Are these comparable regarding sensitivity and signal-to-noise ratio? This paper
describes a reproducible method to compare such sensors. For continuous as well as for pulse
excitation, a suitable setup and obtained results are described. Measuring the noise spectra helps
to explain the influence of the frequency filtering on the signal-to-noise ratio.
Introduction
This paper addresses the needs of AE testers of atmospheric storage tanks of various sizes, whowant to compare the sensitivity of different sensor models or sensors of same model using asimple method. The most important standards for AE sensor calibration are ISO 12713 (primarycalibration) [1] and ISO 12714 (secondary calibration) [2]. Both standards are designed forcalibration laboratories. They require a large and heavy calibration block. AE testers usually do
not have access to such a calibration block. Additionally, ISO 12714 is recommended for thefrequency range 100 kHz – 1 MHz, whereas tank testing is usually done in the frequency range
25-45 kHz. Hence, these standards are of no help for the AE tester. Other standards like EN
13477 [3] and ASTM E976 [4] are general and do not consider the fact that plate waves
propagate in different modes.
To detect smallest AE signals, the sensor should have the largest possible signal-to-noise
ratio (SNR). This means that for a certain excitation the sensor shall provide the largest possible
signal voltage superposed with the smallest possible inherent noise.
For a sensitivity comparison one has to proceed as follows:
1. Excite both sensors in identical manner (frequency sweep) and strongly enough so that thenoise can be neglected against the signal from the excitation. The output signal shall be
UA(f).
2. Measure the inherent noise of the sensors (no excitation!). This is the output voltage UR.
3. The SNR at given excitation is then: SNR(f) = UA(f)/UR. It might be of interest to calculate a
SNR for peak values and one for RMS values.
4. The frequency response as well as the noise measurement shall be made with the same
frequency filters as used for the tank test.
5. The sensor with the higher SNR can distinguish smaller excitations from the backgroundnoise. Hence, this is the more sensitive sensor.
Setup
For the comparison the following setup was used:
J. Acoustic Emission, 25 (2007)132© 2007 Acoustic Emission GroupFunction Generator (FG)The function generator creates a sine wave voltage with adjustable frequency and amplitude or apulse with selectable duration and amplitude, respectively. In our test we used model 33220A(Agilent).
Sensor Excitation
For a comparison, the sensors under test (SUT) need to be excited acoustically in exactly thesame way. As emitter an ultrasonic transducer model V101 (Panametrics) was coupled face toface to one SUT using light machine oil as coupling agent. The emitter was driven by afrequency-swept sine wave and the AE signal amplitude from SUT was measured.
This method provides well reproducible results and is well suited for routine sensor
verification. But the following objections could be raised:a) AE tank-floor testing analyzes burst AE and not continuous (sine wave) AE. A comparison
should also consider burst excitation.
b) Different SUT models could have different feedback on the V101 emitter and thereby tamper
the comparison result.
Considering these objections, a second comparison was made using a pulse excitation via an
aluminum rod of 610-mm length and 19-mm diameter with polished ends. Both excitation
methods led to almost the same results for the frequency range 25-45 kHz.
Compared Sensor Models
We compared a Vallen VS30-SIC-46dB sensor (S/N 120) with another sensor, hereafter
called XXX. Both sensors have an integral preamplifier requiring 28V DC supply voltage on thesignal wire and 20.6 mm diameter. The following lists the differences.
Model
LengthFace
Connector
VS30-SIC-46dB 52.8mm isolated ceramic plate
BNC at case
XXX
38.8mm non-isolated metal plate BNC with 1 m cable
Measurement Chain with Various Filters
For measurements we used Vallen AMSY-5 AE system with dual-channel AE processor
ASIP-2, a 25-45 kHz band-pass for the first test, and a 25-100 kHz band-pass for a second test.
These band-pass filters consist of digital high- and low-pass filters each with 48 dB/octave
steepness. Figure 1 shows the response curves of the band-pass at 1 VPP continuous sine wave at
ASIP-2 input. Frequency sweep and RMS measurement were controlled by Vallen Sensor Tester
software.
Sensor Frequency Response
To obtain Figs. 2 and 3, the FG output (50 mVPP, terminated externally with 50) wasconnected to the V101, face-to-face with SUT. The red curves were taken with 25-45 kHz filter
and the blue curves with 25-100 kHz filter in ASIP-2. For the determination of the inherent noiseof the SUT, we removed the acoustic excitation by disconnecting the V101 from the FG. We
amplified the sensor output with an auxiliary amplifier by 40 dB, which allowed one to ignore
the noise added by subsequent measurement stages. In this way the horizontal lines in orange(25-45 kHz) and green (25-100 kHz) were recorded.

133

Fig. 1 Frequency response of the used filters.
During this noise measurement, we ensured that no acoustic noise sources like fans, humanvoices, or others could cause a distortion within the frequency range under evaluation. Both SUT
were treated in exactly the same way.

Fig. 2 Frequency response and noise of VS30-SIC-46 dB.
25-45 kHz: red and orange lines; 25-100 kHz: blue and green lines.

Fig. 3 Frequency response and noise of XXX. Line colors same as in Fig. 2.
The results are summarized in Table 1. They were deduced from the 4 lines of Figs. 2 and 3,
whereby the noise has been corrected by the 40 dB post-amplification. As can be seen from noiseand maximum amplitude, the gain of the integral preamplifier of XXX is lower than that of theVS30-SIC-46dB, but the deciding factor is the SNR as this is independent of the gain. Results forcontinuous excitation and 25-45 kHz filter: VS30-SIC-46dB provides 17 dB more signal
amplitude and 9.5 dB better SNR.
134

Table 1 Results for continuous sensor excitation.
Sensor:Filter [kHz]:
Maximum amplitude (RMS):
line color:
Frequency at max. ampl.:
RMS noise:line color:
Signal-to-noise ratio SNR:
Difference wrt. XXX at 25 - 45 kHz:

VS30-SIC-46dB
25-45
25-10087 dB90.5 dB
redblue
35 kHz53 kHz
10 dB
20 dB
orangegreen
77 dB
70.5 dB
9.5 dB
3 dB

XXX
25-45
70 dB
red
35 kHz2.5 dBorange67.5 dB
0 dB

25-10072 dB
blue
78 kHz
5 dB
green
67 dB
-0.5 dB

Using Burst Excitation
For XXX the exciting pulse amplitude was 5 VP. For VS30-SIC-46dB the amplitude was
reduced to 1 VP because 5 VP led to saturation due to the larger gain. Figure 4 shows the
response of the VS30-SIC-46dB with 1-VP excitation amplitude at V101, and Fig. 5 shows the
response of XXX with 5 VP at V101, both with 25-45 kHz filter. For the FFT, a 190-s long
Hamming window was used. Table 2 lists the maximum amplitude in the time domain (line 2),
converted to dB (line 3), the maximum amplitude in the frequency domain (line 4). To
compensate for the 5-VP excitation of XXX, its line-2 value is divided by 5 (600/5 = 120 mV)
and line 4 is reduced by 14 dB (97–14 = 83 dB). Table 2 also lists the noise in mVP (line 5) andconverted to dB (line 6). The noise values were recorded separately, in reference to the SUT
output (before 40-dB amplification) and are maximum values (peaks), which occurred in afrequency of 1/s or less. The resulting signal-noise-ratios are listed in line 7 (time domain) andline 8 (frequency domain). Scaling in Figs. 4-7 refers to the input voltage at the AE signal
processor (ASIP-2).

Fig. 4 Pulse response VS30-SIC-46dB, filter: 25-45 kHz, excitation 1 VP x 1 s.
max. 940 mV in time domain or 100 dB in FFT. Noise: 0.58 mVP.
Figures 6 and 7 were taken with band-pass of 25-300 kHz. Figure 6 shows at approximately
30 s the arrival of the s1 mode, which travels with ~4000 m/s at 250 kHz according to Fig. 8.
Considerable differences between the two sensor models are seen: VS30-SIC-46dB exhibits a135

resonance at ~60 kHz, where XXX exhibits resonances at 40 and 80 kHz and an anti-resonanceat 60 kHz. Due to the obscure influence of the s1 mode, a direct comparison of Figs. 6 and 7 is
not recommended.

Fig. 5 Pulse response XXX, filter: 25-45 kHz, excitation 5 VP x 1 s.
max. 600 mV in time domain, 97 dB in FFT. Noise: 0.20 mVP.

Fig. 6 Pulse response VS30-SIC-46dB, filter: 25-300 kHz, excitation 1 VP x 1 s.
max. 1900 mV/103 dB at 60 kHz. Noise: 2.18 mVP. Arrival of s1 mode at t = 30 s.
Result for burst excitation with 25-45 kHz filter: Table 2, line 3 indicates that VS30 delivers
17.8 dB more amplitude and line 8 shows 8.6 dB more SNR in time domain and 7.8 dB more in
frequency domain. This result is very similar to continuous excitation.

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Fig. 7 pulse response XXX, filter : 25-300 kHz, excitation 5 VP x 1 s.
Max. 1750 mV/98 dB at 80 kHz. Noise: 0.33 mVP.

Fig. 8 Dispersion curves for 19 mm aluminum rod, according to [5].
Table 2 Burst excitation results with 25-45 kHz filter.

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Noise Spectra, Impedance and “Natural Frequency”
The frequency, at which a sensor shows a sudden jump in its impedance, is called natural
frequency [6]. For obtaining an impedance curve (Figs. 9 and 10), a passive sensor must be used.
A sine wave of 100 mVPP from a function generator in series with 10 pF was fed in parallel to asensor VS30-V (same piezo-element as VS30-SIC-46dB) connected to a preamplifier AEP4 (40dB). Figure 9 shows the lowest impedance at 51 kHz, and the highest at 58 kHz. Peculiar withthis frequency is, that the amplitude measured with sensor connected (Fig. 9: 94 dB) is higher
than measured with the sensor disconnected (89 dB)! The impedance combination of both,
sensor and preamplifier, generate a sharp resonance peak.

Fig. 9 Impedance jumps of VS30-V (no integrated preamplifier).

Fig. 10 Zoom of Fig. 9 around the natural frequency (58 kHz).
This peak can also be seen in the noise spectrum (Fig. 11) of a sensor with integrated
preamplifier. This dominating peak in the noise spectrum is the reason for the increase ofinherent noise when using the 25-100 kHz band-pass filter instead of 25-45 kHz. In both Figs. 9
and 11, further peaks at 112 kHz, 175 kHz and 270 kHz can be identified. XXX has its
dominating peak in the noise spectrum below 25 kHz (Fig. 12). This explains why the noise of
the XXX does not substantially increase with a 25-100 kHz band-pass filter.
The determination of a reproducible noise spectrum requires averaging the FFT over manymeasurements as the individual spectra of noise records scatter considerably. For Figs. 11 and
12, we averaged 1000 noise records using the Vallen FFT-Averager. The absolute scaling ofFigs. 11 and 12 must not be compared. These figures shall just illustrate the different ‘natural
frequencies’ and the effect of filter bandwidth on the noise of the filtered signal.
138

Fig. 11: Noise spectrum VS30-SIC-46dB(average of 1000 FFTs).

Fig. 12: Noise spectrum XXX(average of 1000 FFTs).

Conclusion
This report describes two setups to compare the sensitivity of different sensor models. One iswith face-to-face coupling and continuous excitation, and the other with burst excitation via an
aluminum rod. In both cases, the excitation is perpendicular to the sensitive area. When looking
at the SNR (signal-to-noise ratio), the inherent noise and the noise spectra have to be considered.
Two sensor models have been compared. The result depends strongly on the used frequency
range. For 25-45 kHz and identical excitation, one sensor model provides 17 dB more signal andabout 9 dB better SNR than the other. For tank floor testing, the frequency range of 25-45 kHz issuited best. The more sensitive sensor model shows a natural frequency of 60 kHz. This causes apeak in the noise spectrum, which is excluded effectively by the 25-45 kHz band-pass filter.
References[1] Non-destructive testing – Acoustic emission inspection – Primary calibration of transducers,
ISO 12713, ISO/TC135, 1998.
[2] Non-destructive testing – Acoustic emission inspection – Secondary calibration of acoustic
emission sensors, ISO 12714, ISO/TC135, 1999.
[3] Non-destructive testing – Acoustic emission – Equipment characterisation- Part 2:
Verification of operating characteristics, EN13477-2, CEN/TC138, 2001.
[4] Standard Guide for Determining the Reproducibility of Acoustic Emission Sensor Response,
ASTM E976 ASTM/E07.04, 2001.
[5] J.L. Rose, Ultrasonic Waves in Solid Media, Cambridge University Press, ISBN 0 521 54889
6, 2004, p. 149+.
[6] G. Gautschi, Piezoelectric Sensorics, Springer Verlag, ISBN 3-540-42259-5, 2002, p. 195+.

139

DAMAGE IN CARBON FIBRE COMPOSITES: THE DISCRIMINATIONOF ACOUSTIC EMISSION SIGNALS USING FREQUENCYMARK EATON, KAREN HOLFORD, CAROL FEATHERSTON and RHYS PULLIN
Cardiff School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK
Abstract
This work considers the use of frequency content as a discriminating factor for acoustic
emission (AE) signals from damage mechanisms in carbon fibre composite materials. Using abroadband conical transducer as an artificial source, investigations were made into the effects ofsource frequency (relaxation time), specimen geometry and sensor response on the frequency
content of the recorded signals. It was shown that source frequency had an effect on the frequency content of the recorded signals, however, the specimen geometry and sensor response
were shown to have a more significant effect. Additionally, AE signals were recorded from realdamage mechanisms in tensile and beam buckling coupon specimens. The peak frequency content was used to examine signals resulting from the different damage modes identified. It was
shown that some level of discrimination could be achieved and observations were in generalagreement with previous research studies. However it was shown that great care is required whenusing peak frequency content as a discriminating factor because geometry and sensor response
can have a distorting effect on the results.
Keywords: AE, Composites, Signal Discrimination, Frequency.
Introduction
Due to the widespread use of composite materials in the automotive and aerospace industries,
in particular their use in safety critical structures, there is an increasing need to ensure theircontinued safe operation throughout long service lives. Acoustic emission (AE) can be used forin-service structural health monitoring of composite structures and offers great potential for thelocation and characterisation of damage. Many different damage mechanisms, such as delamination and matrix cracking, can occur in composite materials which may not be detected visually
but can dramatically reduce the ultimate failure load of a component. The prediction of damage
type and its onset is difficult due to the possible variations in component lay-ups and manufacturing processes. As such it is very desirable to be able to detect and discriminate between AE
signals from different damage mechanisms in composite materials.
A variation of source relaxation time has been shown for different damage mechanisms inglass fibre / epoxy [1] and it is commonly believed that this will result in AE signals with different frequency content. A number of researchers have attempted to utilise frequency content as adiscriminating factor for AE signals [2-4]. The results are in good agreement, with all researchers
finding matrix failures such as matrix cracking and delamination producing signals with low frequency content (~<150kHz) and fibre failures producing signals with high frequency content(~>300kHz).
Due to their laminate nature, composite material components are commonly manufactured asthin walled structures and as such are approximately plate like. To perform meaningful analysis
it is necessary to consider the effects of propagation in a plate on recorded AE signals. When AE
J. Acoustic Emission, 25 (2007)140© 2007 Acoustic Emission Groupwaves propagate in a plate like structure they couple at the surfaces to produce two basic platewave modes (also known as Lamb waves). These are the ‘symmetric mode’ (s0) in which theprincipal displacement is in the plane of the plate, parallel to propagation direction and the‘asymmetric mode’ (a0), in which the principal displacement is perpendicular to the plane of theplate and the direction of propagation [5].
Experimental Procedure
An investigation into the effects of specimen geometry and sensor response on frequency
content was conducted on a 1500 mm x 500 mm x 2.16 mm carbon fibre/epoxy plate manufactured from Advanced Composites Group (ACG) MTM28-1/HS uni-directional (UD) pre-pregwith a (0,90)4S lay-up. An artificial source of AE was used to generate test signals. This consistedof an in-house manufactured broadband conical transducer provided by the National PhysicalLaboratory, UK. Two Physical Acoustics Corp. (PAC) S9208 broadband sensors were used forthe detection of all signals. The detection sensors were arranged along the 0o material direction at
the centre of the plate such that one sensor was adjacent to the conical transducer and the otherwas at a distance of 200 mm. The conical transducer was coupled once only at the start of the test
to avoid any repeatability of coupling issues. The detection sensors were coupled with ultrasoundgel and mounted with magnetic clamps. A sensitivity test was conducted using a Hsu-Nielsen
source each time a sensor was mounted, to ensure adequate coupling was achieved. A PAC
Wavegen-1410 board provided a 160-V single-cycle square-wave with frequency varying from
100-900 kHz at 100 kHz intervals, to drive the conical source transducer. The test was repeatedwith the sensors in opposite positions. Both tests were repeated using two PAC WD wideband
sensors for detection. Signals were recorded throughout all tests using a PAC PCI-2 system at 10
MSPS.
An investigation into the frequency content of signals from various failure modes was conducted using six tensile specimens measuring 240 mm x 30 mm x 1.08 mm and manufactured
from ACG’s HTM45/HS UD pre-preg with (0)8 and (0,90)2S lay-ups. To suppress grip noise andminimise slippage, aluminium end tabs were bonded to the specimens reducing the unsupported
length to 150 mm. The specimens were instrumented with a single PAC WDi sensor mounted at
the centre using electrical tape with brown grease as a couplant and loaded to failure under displacement control at a rate of 0.125 mm.min-1.
An investigation into failure modes during buckling was conducted using six beam buckling
specimens manufactured from HTM45/HS UD pre-preg with measurements of 200 mm x 30 mm
x 2.16 mm and lay-ups of (±45)4S and (0,90)4S. The specimens were subject to an in-plane compressive load along their length. This is facilitated by a loading cage that provides built-in supports at each end, reducing the unsupported length to 170 mm, and ensuring the supports remainaligned throughout the test. The specimens were loaded to failure at 0.5 mm.min-1 and 1
mm.min-1 for the (0,90)4S and (±45)4S lay-ups, respectively. Two PAC WD sensors were
mounted using electric tape with brown grease as a couplant at  and  of the unsupported
length to correspond with the two points of inflection in the deformed mode shape.
Results and Discussion(i) Effect of specimen geometry and sensor response
The waveforms and FFTs of the signals recorded by two S9208s adjacent to the conicalsource transducer are shown in Fig. 1a and b for driving pulses of 100 kHz, 500 kHz and 900
141

0.9

40

100kHz Driving pulse

100kHz Driving
pulse

0

0

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pulse

0

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4

900kHz Driving pulse

900kHz Driving
pulse

0
-0.24
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0
50

150

250

350

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450

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400

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a) S9208-1
0.9

40100kHz Driving pulse

100kHz Driving
pulse

0

0

-0.9
0.48

10

500kHz Driving pulse

500kHz Driving
pulse

0

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-0.48
0.24

4

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900kHz Driving
pulse

0
-0.24
-50

0
50

150

250

350

0

450

200

400

600

800

1000

b) S9208-2

Fig. 1 Waveforms and their FFTs recorded by S9208s adjacent to the source, presented in V
versus μs and mV versus kHz respectively
kHz. The response of both sensors to a 100-kHz driving pulse shows that most of the energy inthe signal is contained below 100 kHz. As the frequency of the driving pulse increases, so thefrequency content of the recorded signal expands to higher frequencies. The response to a 500kHz artificial source shows that the frequency content of the signals has increased, with most of
the energy contained below 400 kHz and very little energy seen above 600 kHz. However, as thefrequency of the driving pulse is increased above 500 kHz, there is little increase in the frequency content of the signal, which can be seen in the response to a 900 kHz source where,
again, most of the energy is contained below 400 kHz and very little is seen above 600 kHz.
When the sensors are moved away from the conical source transducer, the response becomesvery different. Figure 2a and b show the recorded signals and their FFTs for both the S9208 sensors mounted at a distance of 200 mm from the conical transducer, with driving pulse frequencies of 100 kHz, 500 kHz and 900 kHz. Both the s0 and a0 plate-wave modes are observed in all
the waveforms recorded at 200 mm. The response of the S9208 sensors to a 100-kHz driving
pulse show an a0 mode that is much larger than the s0 mode. This is reflected in the FFTs bymost of the energy being contained below 100 kHz, due to the lower frequency of the a0 mode.
The half-cycle times for the peak of the a0 modes equate to frequencies of approximately 52 kHz
and 55 kHz for S9208-1 and 2, respectively. The 500-kHz and 900-kHz driving pulses generate a
response with s0 and a0 modes of similar amplitudes, and this is reflected by the observation ofhigher frequency peaks in their FFTs between 200 kHz and 500 kHz. The two regions of frequency content can be attributed to the fundamental plate-wave modes a0 and s0. This is ratified
by the half-cycle times of the peak cycles of the s0 and a0 modes from S9208-1 for a 500-kHz
142

10

s0

0.15

100kHz Driving
pulse

100kHz Driving pulse

0

a0

-0.15
0.033

0
2

500kHz Driving pulse

500kHz Driving
pulse

0

0

-0.0330.015

1900kHz Driving pulse

900kHz Driving
pulse

0-0.015

0
0

100

200

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400

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200

400

600

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1000

a) S9208-1
0.15

10

100kHz Driving pulse

100kHz Driving
pulse

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2

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500kHz Driving
pulse

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pulse

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b) S9208-2

Fig. 2 Waveforms and their FFTs recorded by S9208s 200 mm from the source, presented in
V versus μs and mV versus kHz, respectively.
source, which have corresponding frequencies of 374 kHz and 39 kHz, respectively, suggestingthat geometry has a considerable effect on the frequency content of a signal.
The response of two WD sensors mounted adjacent to the conical source transducer is presented in Fig. 3a and b for driving pulse frequencies of 100 kHz, 500 kHz and 900 kHz, respectively. The response of the two WD sensors to a 100-kHz driving pulse contain most of the energy below 150 kHz, which is similar to the response of the S9208 sensors to the same source.
As observed in the response of the S9208, the frequency content of the recorded signal expands
to higher frequencies as the driving pulse frequency is increased. For a 500-kHz driving pulse,
the majority of the signal energy is contained below 600 kHz. For a driving pulse of 900 kHz,
there is still very little energy contained above 600 kHz. The increase in higher frequency content observed is thought to be an attribute of the sensor because it is not observed for thebroadband S9208 sensors and indeed the WD is known to have a peak in sensitivity at approximately 525 kHz. The observation of most interest from this test is the difference in response between the two WD sensors to the same source. It can be seen in Fig. 3 that the two WD sensors
have a considerably different response to a 500-kHz driving pulse. The signal recorded by WD-1
has more low frequency content than that of WD-2 and its peak is approximately 100 kHz,
whereas WD-2 has more higher frequency content with significant content observed at approximately 475 kHz and 550 kHz. This demonstrates how the response of a sensor can affect the frequency content of a recorded signal and how the response can vary, even within sensors of thesame model.

143

6

300

100kHz Driving pulse

100kHz Driving
pulse

0

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40

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pulse

0

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900kHz Driving
pulse

0
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a) WD-1
6300

100kHz Driving pulse

100kHz Driving
pulse

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40

500kHz Driving pulse

500kHz Driving
pulse

0

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30

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900kHz Driving
pulse

0
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0
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150

250

350

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b) WD-2

Fig. 3 Waveforms and their FFTs recorded by WDs adjacent to the source, presented in V
versus μs and mV versus kHz, respectively.
The waveforms and their FFTs, recorded by the two WD sensors mounted at a distance of
200 mm from the conical source transducer are shown in Fig. 4a and b for source driving pulsesof 100 kHz, 500 kHz and 900 kHz. It can be seen that the level of attenuation observed with the
WD sensors is considerably less than that for the S9208 sensors, because the broadband frequency response results in reduced sensitivity. Both the fundamental plate-wave modes are observed in a signal recorded from a 100-kHz driving pulse but as the frequency of the driving
pulse increases the a0 mode rapidly diminishes and for a 500-kHz driving pulse is no longer observed. The FFTs of these waveforms have three distinctive peaks occurring at approximately
100 kHz, 275 kHz and 550 kHz. The amplitudes of these peaks are seen to vary with driving
pulse frequency. As expected, more energy is contained at lower frequencies for a low frequency
driving pulse and more energy is contained at higher frequencies for a higher frequency driving
pulse. The lower and middle frequency peaks are again attributed to the a0 and s0 modes in accordance with the S9208 results. It can also be seen that as the a0 mode loses amplitude with increasing driving-pulse frequency, a corresponding reduction in the amplitude of the low frequency peak in the FFTs is observed. The higher frequency peak centred about 550 kHz is again
considered to be an artefact of the sensor response. Additionally, it appears that the frequency
content at this level is very different for the two WD sensors, suggesting that the frequency response of the two sensors is different. A difference in sensor response such as this could lead toconfusion when considering frequency as a discriminating factor. For example the two FFTs
seen in Fig. 4a and b for a 500-kHz driving pulse both have a peak frequency of approximately
275 kHz. WD-1 has a very dominant peak at 275 kHz, whereas WD-2 has a peak at 275 kHz,
whose amplitude is only slightly larger than that of the peak at 550 kHz. It is clear that a small
variation in the source could quite easily lead to a dramatically different result from
144

0.45

30

100kHz Driving pulse

100kHz Driving
pulse

0

0

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0.6

15

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500kHz Driving
pulse

0

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10

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900kHz Driving
pulse

0
-0.36
0

0
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200

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400

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200

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1000

a) WD-10.45

30

100kHz Driving pulse

100kHz Driving
pulse

0

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15

500kHz Driving pulse

500kHz Driving
pulse

0

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900kHz Driving
pulse

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b) WD-2

Fig. 4 Waveforms and their FFTs recorded by WDs 200 mm from the source, presented in V
versus μs and mV versus kHz, respectively.
sensor WD-2 to a similar source frequency. It is interesting to note that other researchers [2-4] allobserved frequencies in low, medium and high frequency bands using PAC WD sensors that correspond to the three frequency peaks of 100 kHz, 275 kHz and 550 kHz observed here.
(ii) Effect of failure mode typeFigure 5a and b present the failures observed in two tensile specimens having lay-ups of(0,90)2S and (0)8, respectively. In Fig. 5a, ((0,90)2S lay-up), large amounts of matrix damage wereobserved in the form of matrix cracking and fibre-matrix debonding. In Fig. 5b, ((0)8 lay-up), thepredominant failure mechanism observed was fibre failure, and it should be noted that the longitudinal splitting observed occurred at the point of final failure and was not observed beforehand.
Figure 5c and d present the frequency analysis of the AE signals recorded during the tensile testing of the two specimens shown in Fig. 5a and b. The hits for each test are separated using theirpeak frequency, recorded by AEWin, into three frequency bands of 0-150 kHz, 150-400 kHz and>400 kHz. The hits within each band are then plotted cumulatively against time of test. The low
frequency band for the (0,90)2S specimen is clearly dominant and this corresponds to the large
amounts of matrix damage observed in Fig. 5a. The (0)8 specimen was observed to have muchless matrix damage and more fibre failure. Consequently, the frequency analysis shows thehigher frequency band to be dominant for this type of specimen. Previous research in this area
[2-4] has also related matrix damage to signals with low frequency content and fibre failure tosignals with high frequency content. This demonstrates that some level of discrimination can beachieved between signals resulting from different types of failure mechanism. As yet the causeof activity in the 150-400-kHz band has not been addressed. The previous work presented byothers [2-4] has suggested that frequencies in this range may be attributed to fibre pull-out. The
145

physical identification of fibre pull-out is very difficult, so it is not possible to confirm or refute
this. It is worth noting, however, that the results presented in the first part of this paper showedthat the s0 mode generated a peak within this range. Therefore, it is not unreasonable to consider
that activity in this band maybe a result of the specimen geometry and/or the sensor response.
Additionally, the frequency content of a signal may be further complicated by different source
mechanisms occurring simultaneously; for example, a fibre break is likely to be accompanied bysome fibre pull-out or movement.

a) (0,90)2S lay-upb) (0)8 lay-up

1000

160

750

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500

80

250

40

0

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1000

2000

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0-150kHz 150-400kHz >400kHz
150-400kHz >400kHzc) (0,90)2S lay-upd) (0)8 lay-up
Fig. 5 Frequency analysis of tensile specimens, presented in cumulative hits versus timeof test (s).

0-150kHz(iii) Effect of buckling failure
The frequency analysis for two representative beam-buckling specimens is presented in Fig.
6a and b, and the analysis is conducted individually per channel. The damage observed in both
specimens was predominantly matrix failure in the form of surface-ply delamination and matrix
cracking, which is represented by the presence of a large number of low frequency hits on bothchannels for both specimens. However, the activity in the middle and high frequency bands wascompletely different on channels 1 and 2 for both specimens. For channel 1 of specimen 1 (Fig.
6a) the most activity is seen in the middle band and the least activity is seen in the higher band,
whereas for channel 2 of specimen 1 (Fig. 6a) the most activity is seen in the high frequency
band and the least activity is seen in the middle frequency band. Indeed, the rate of hits recordedon channel 1 between 150-400 kHz is very similar to that of hits recorded above 400 kHz on
channel 2 and vice versa. The same effect is observed for specimen 2 (Fig. 6b), where the levelsof activity in the middle and high frequency bands are opposite for each channel. The two WD
sensors used for these tests are the same two sensors used in the first part of this work to assess146the sensor response to different frequency sources. The variation in results observed between different sensors for the same test is a result of the increased sensitivity of WD-2 in the higher frequency region. Figure 4 shows how the different sensors can produce a different response to thesame source. In particular, the 500-kHz source shows that for the same source the peak frequency might lie in either the 150-400-kHz band or the >400-kHz band depending on the sensor.
This highlights the effect that sensor response can have on the frequency content of a signal andthe potential confusion it can cause.
2400

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b)Fig. 6 Frequency analysis of beam buckling specimens, presented in cumulative hits versus
time of test (s) per channel.
Conclusions
Differing source mechanisms or more specifically the differing relaxation time of source
mechanisms does have an effect on the frequency content of the resulting AE signals. It has beenshown that peak-frequency content of an AE signal can be used to provide some level of discrimination between signals resulting from different source mechanisms. However, it has also
been demonstrated how the overriding effects of specimen geometry and sensor response canproduce misleading results. As such, this technique should be approached with great care and athorough understanding of the wave propagation and sensor response for a specific case is essential to achieving meaningful results.

147

Acknowledgements
The authors would like to thank Dr Pete Theobald from the National Physical Laboratory(NPL) for the loan of the conical transducer.
References
1. H. Suzuki, M. Takemoto, K. Ono, J. Acoustic Emission, 14, 1996, pp. 35-50.
2. J. Bohse, Composites Science and Technology, 60, 2000, pp. 1213-1226.
3. C. R. Ramirez-Jimenez, N. Papadakis, N. Reynolds, T. H. Gan, P. Purnell, M. Pharaoh, Composites Science and Technology, 64, 2004, pp. 1819-1827.
4. P. J. de Groot, P. A. M. Wijnen, R. B. F. Janssen, Composites Science and Technology, 55,
1995, pp. 405-412.
5. H. J. Rindorf, Bruel and Kjaer Technical Review, 2, 1981, pp. 3-44

148

CHARACTERISTICS OFACOUSTIC EMISSIONS FROM DEHYDRATING WOOD RELATED TO SHRINKAGE PROCESSESSABINE ROSNER
Institute of Botany, Department of Integrative Biology; University of Natural Resources andApplied Life Sciences, Vienna (BOKU); Gregor Mendel Str. 33, 1180 Vienna, Austria
Abstract
Cavitation events in water-conducting elements of wood and dehydration cracks caused bywood-shrinkage processes induce acoustic emissions (AEs) in the high-frequency range. The aimof this study was to relate spectral features of AEs from dehydrating Norway spruce (Picea abies(L.) Karst.) trunkwood to shrinkage processes. Wood shrinkage in the radial direction was assessed by load cells. Resonant 150 kHz, 60 kHz and broadband transducers (operating range 50200 kHz, 35-100 kHz and 100-1000 kHz, respectively) were used to detect AEs at the same timeon standard size samples (5 or 10 cm (length) x 6 mm (radial) x 6 mm (tangential)). Two different shrinkage processes were detected, where the first shrinkage period was termed “tension
shrinkage”. Shrinkage caused by high tensions is a phenomenon observed also in field measurements and is practically used for drought stress measurements of living trees. The final shrinkage
process started at relative water contents around the “fiber saturation point”, where most of thecell lumina contain no water, but walls are fully saturated with liquid. The onset of the rapid risein tension shrinkage coincided with the decrease of the first rapid rise of the total AE rate (r =0.99). The valley between the last two AE peaks was located at the onset of the final shrinkage
process. Characteristics for AEs during the tension shrinkage period were higher proportions of
AEs with very high amplitudes (> 60 dB) and lower frequency components. Therefore, the highest relative AE energies (pVs) were measured during this stage. AEs during the final shrinkage
period showed lower amplitudes, lower energy values and higher frequency components.
Keywords: Lumber drying, wood shrinkage, peak amplitude, frequency, AE transducers
Introduction
Norway spruce wood consists of 90% of small water conducting elements with a diameter ofabout 10-40 m and a length of 1-4 mm (tracheids). Water in tracheids exists in three states;
first, as “free” liquid water, second, as water vapor and within the tracheids, and third, as chemically “bound” water in the cell wall matrix [1]. „Free” water in the tracheids of the sapwood istransported under tension, requiring high mechanical strength of the cell walls in order to avoid
implosion, and hydraulic safety against breakage of the water column (cavitation). During periods of drought stress decreases in the stem diameter of living trees can be observed [2-3]. Theseshrinkage processes are supposed to induce internal checking in living trees [4]. Stem diameterchanges are caused by hydrostatic tension forces resulting in a decrease in diameter of the conducting elements, which still contain “free” water. The breakage of the water column is induced
when the tension stress increases above a certain threshold. Once cavitated, the conduit does notconduct water any longer and the hydraulic conductivity of the plant is reduced, leading to impairment of water supply of the crown [5]. Cavitations induce AEs with the highest amplitudes in
the range of 100-300 kHz, which are used to detect periods of drought stress non-destructively[2].
J. Acoustic Emission, 25 (2007)149© 2007 Acoustic Emission GroupAE testing is also an established method for optimizing lumber drying conditions, where theanalysis of the amplitude or energy distribution of AE signals has been successfully used to pinpoint wood checking [6-7]. Most of these tests are not performed on fully saturated wood, wherestill all conducting elements contain “free” water, but on “green” wood at varying moisture contents. When cell lumina contain no water, but cell walls are fully saturated with liquid (“fiber
saturation point”), further dehydration leads to a shrinkage process, which can induce drying
checks. Drying checks develop because the fiber saturation point is reached far earlier in theshell than in the core of a specimen and because wood is an anisotropic material concerning
shrinkage (radial = in direction of the annual rings: tangential = 1 : 1.5) [7-8]. Nevertheless,combined shrinkage and AE tests are rare in AE literature on wood [9] and plant physiologists
stop observations of stem diameter changes and AE rates long before most of the tracheids reachthe fiber saturation point [2]. The aim of this study was to relate AEs of dehydrating Norway
spruce (Picea abies (L.) Karst.) sapwood to radial wood shrinkage processes from the totally
saturated “green” state till the cessation of all shrinkage processes.
AE Testing Procedure and Data Analysis
AEs were monitored with the DiSP Digital AE system from Physical Acoustics Corporation (PAC, Princeton Jct, NJ, USA). Preamplifiers (40 dB) were used in connection with 150kHz R15 resonant transducers, 60-kHz R6 and WD broadband transducers (operating frequency
range 50-200 kHz, 35-100 kHz and 100-1000 kHz, respectively). AEs were recorded with a detection threshold of 35 dB (0 dB = 1 V input). The detection threshold was chosen as the peak
amplitude of signals produced by waving the AE transducer in air, plus 15 dB. Extraction of features such as the number of counts, the peak amplitude (dB), the AE duration (s), the relative
AE energy (pVs) and the average frequency (kHz) of each AE signal was carried out with AE
Win software (PAC). AE energy (also referred to as “PAC-Energy”) is defined as the area ofthe rectified voltage signal over the duration of the AE signal.
AE transducers were positioned on the tangential face of fully saturated Norway spruce standard beams (cambial age = 20 - 25) using an acrylic resin clamp [9]. Preparation of fully saturated sapwood beams (0.6 cm tangential, 0.6 cm radial, and 5.0 cm or 10.0 cm longitudinal) is
described in [10]. Silicone paste (Wacker, Burghausen, Germany) served as a coupling agent.
The samples were positioned on an acrylic resin sample holder fixed upon a compression spring.
Coupling pressure during the dehydration process was recorded with a DMS load cell (Type8416-5500, range 0 - 500 N; amplification with an inline amplifier for DMS, Type 9235; Burster,Gernsbach, Germany) between the AE transducer and the screw of the acrylic resin clamp. The
coupling pressure was set to 30 N. The acrylic resin clamp was then kept so deep in water thatthe wood sample was totally covered till the applied pressure reached a constant value. After
quickly removing superficial water from the clamp and the wood, recording of AEs and couplingpressure was started. AE testing was done at ambient temperatures (25°C, 30% r.h.) until coupling pressure reached a constant value, which was the case after about 20 h. The coupling pressure decrease during dehydration induced by shrinkage (about 3 N in total) was related to thetotal radial shrinkage (digital gauge, accuracy 1 m). AE data filtering and presentation was
done with Vallen VisualAE™ software (Vallen Systeme GMbH, Munich, Germany).
Values are given as mean values ± standard error. Differences between mean values were
tested for significance with One-Way ANOVA and subsequent Scheffe-test or by t-test. Associations between two variables were examined using linear regression analysis.
150

Results and Discussion
Shrinkage processes and total AE-rate
Two different radial shrinkage processes were observed (Figs. 1-3). First, a shrinkage period
at moderate water losses, which was termed “tension shrinkage” (TS), and second, the final
shrinkage (FS) period, leading to 3.86 ± 0.11% (n = 71) shrinkage. The onset of the rapid rise intension shrinkage coincided with the decrease of the rapid rise of the total AE rate (r = 0.99, n =22). Tension shrinkage is caused by a decrease in diameter of tracheids, which still contain“free” water. Hydrostatic tension forces acting perpendicular to the cell walls try to draw thewalls inwards. When the stress becomes too high cavitation occurs [8]. Accordingly, tension
shrinkage was to some extent a reversible process, because the tension was released after cavitation. After this relaxation period, the final shrinkage process started when most of the cell lumina
contained no free water, but cell walls were still fully saturated with liquid. The beginning of the
final shrinkage process coincided with a rapid rise in the AE rate. Towards the end of AE detection, two peaks in the AE rate could be found in most of the samples. The first peak occurred
after 72.76 ± 0.72% (n = 42), the second after 85.38 ± 0.45% (n = 42) of the AE detection time,
which was defined as the time between the start of AE testing and when the last AEs were detected. No AEs were detected after no more than 25 % of the total radial shrinkage had takenplace (Figs. 1-3). Whereas WD transducers detected a quite similar total AE number in 5 and 10
cm samples, R6 and R15 transducers detected significant higher numbers of AEs in 10 cm samples (Table 1).

Fig. 1 AE activity (rate/min) and radial wood shrinkage during dehydration detected by WD
transducer and load cell, respectively. AE rates were clustered in peak amplitude steps (left side)
and average frequency steps (right side). Mean AE energy (1 eu = 1 pVs) was calculated for 1
sec steps (below, red line, right y-axis).
151

Fig. 2 AE activity (rate/min) and radial wood shrinkage during dehydration detected by R6
transducer and load cell, respectively. AE-rates were clustered in peak amplitude steps (left side)
and average frequency steps (right side). Mean AE energy (1 eu = 1 pVs) was calculated for 1
sec steps (below, red line, right y-axis).

Fig. 3 AE activity (rate/min) and radial wood shrinkage during dehydration detected by R15
transducer and load cell, respectively. AE-rates were clustered in peak amplitude steps (left side).
Mean AE energy (1 eu = 1 pVs) was calculated for 1 sec steps (right side, red line, right y-axis).
AE characteristics during shrinkage processes
Highest mean peak amplitudes and energies were measured during the tension shrinkage period at moderate water losses (Figs. 1-3). The relative time occurrence of the maximum mean
peak amplitudes/min showed no significant differences between the transducer types (WD, R6,
R15). Highest mean amplitude values were reached after 27.77 ± 0.90% of the AE detection time(n = 42). The cumulated AE energy rate can be used to characterize the hydraulic vulnerability of152sapwood. Norway spruce wood consists of 90% of tracheids, which vary in diameter. It is supposed that mean high energy AEs represent cavitations of more vulnerable, larger tracheids, because more elastic energy is stored in larger conduits under tension [10]. Tension shrinkage
processes can be observed in living trees and are used to pinpoint periods of drought stress in thefield [2, 3]. In accordance, the maximum AE rate of signals >60 dB and the maximum AE energy rate/min were detected in the middle of the tension shrinkage period (R6, R15, r = 0.88, n =14). At the time of maximum AE rates of signals >60 dB, lower AE rates/min were measured by
WD transducers than by R6 or R15 transducers (P < 0.05, Fig. 4). The lower number of AEs
recorded by WD transducers might be explained both by the lower sensitivity of WD than R6
and R15 transducers and the frequency operating range (100-1000 kHz). In highly attenuating
materials, such as wood, the usable upper frequency level is supposed to be 100-200 kHz [6].
Table 1 Frequency characteristics of AEs measured by transducer types WD, R6 and R15 at different shrinkage stages of wood samples varying in length (L). Frequency factors were calculated
from AE rates/min clustered by average frequency steps. Stage TS gives results for the middle ofthe tension shrinkage period, when the maximum AE energy/min was detected, FS1 for the firstAE peak at the onset of the final shrinkage period, and FS2 for the second AE peak during thefinal shrinkage period. Different letters indicate significant differences (P < 0.001) in the Frequency Factors between 5-cm and 10-cm samples.
L(cm)

AEs total

Frequency FactornStage TS
Mean (SE)Stage FS1
Mean (SE)Stage FS2
Mean (SE)

WD
WD

5
10

350909.5 (23420.8)a < 175 kHz / > 175 kHz 7 1.73 (0.21)a 0.95 (0.08)a 0.20 (0.02)a
410353.5 (37057.6)a < 175 kHz / > 175 kHz 7 1.44 (0.06)a 0.97 (0.08)a 0.17 (0.02)a

R6
R6

5
10

393935.3 (29590.1)a620891.8 (26459.2)b

R15
R15

5
10

370243.3 (18305.9)a < 155 kHz / > 155 kHz 7 2.83 (0.19)a 2.92 (0.41)a 0.99 (0.15)a
575638.7 (28961.2)b < 155 kHz / > 155 kHz 7 2.51 (0.12)a 3.89 (0.35)a 1.07 (0.11)a< 50 kHz / > 50 kHz< 50 kHz / > 50 kHz

7 1.43 (0.16)a 0.31 (0.06)a 0.05 (0.01)a7 0.77 (0.05)b 0.47 (0.04)a 0.04 (0.02)aAnalysis of average frequencies showed that the number of signals <175 kHz was 1.4 - 1.7
times higher than those of signals >175 kHz when the mean AE energy rate/min reached maximum values (Table 1). At the same time the R6 transducer recorded 1.4 times the number of AEs<50 kHz than of AEs >50 kHz in 5-cm samples, but only 0.8 times the number in 10-cm samples(R6, Table 1). The rate/min of signals <50 kHz, related to the corresponding total AE rate/min,reached maximum values during the tension shrinkage period (R6, 58.57 ± 2.61% (5 cm), 43.36± 1.52% (10 cm), P < 0.001). Sample length-dependent differences were also present in the
maximum mean energy/min values. In 10-cm samples, much lower maximum energies/min weredetected than in 5-cm samples. Maximum mean energies/min during the tension shrinkage period were positively related to the relative amount of signals <50 KHz (related to the total AE
rate at the same time, Fig. 5).
AEs within the frequency range from 30 to 50 kHz reached highest absolute rates shortly before the final shrinkage process started (Fig. 2). The relative rates (related to the corresponding
total AE rate) of AEs <50 kHz were, however, lower than during the tension shrinkage period,
when maximum mean energies/min were measured (R6, 22.56 ± 3.52% (5 cm), 31.82 ± 1.84%(10 cm), P < 0.05). The number of signals with average frequencies <50 kHz was less than half153the number of signals >50 kHz at the next to the last AE rate/min peak (R6, Table 1). Shortly
before the final shrinkage took place, WD transducers detected a quite similar number of signals<175 kHz and >175 kHz (WD, Table 1). The AE rate/min measured by WD transducers was
significantly lower than measured by R15 or by R6 transducers (P < 0.001, Fig. 4).

Fig. 4 Box- and whiskers plots of the AE rate/min at different shrinkage stages detected by different transducer types (WD, R6, R15) for 5-cm sample length (left side) and 10-cm sample
length (right side). Stage TS gives results for the middle of the tension shrinkage period, whenthe maximum AE energy/min was detected, FS1 for the first AE peak, located around the fiber
saturation point, at the onset of the final shrinkage period, and FS2 for the second AE peak during the final shrinkage period.
The final shrinkage process was characterized by a very high proportion of signals with highaverage frequencies (>250 kHz (WD), >100 kHz (R6)), low amplitudes and energies (Figs. 1-3).
These signals may come from cavitation events in tracheids, which contribute less to axial watertransport than to other wood functions such as radial water transport, mechanical support andstorage of water, or from cavitations in ray tracheids [10]. At the last AE peak, the number ofsignals <175 kHz was less than 25 % the number of AEs >175 kHz (WD, Table 1). R6 transducers measured a much more lower number of AEs with average frequencies <50 kHz in relation tosignals >50 kHz (Table 1). The relative amount of signals <50 kHz made up only 5.05 ± 1.00%(5 cm) and 4.25 ± 0.80% (10 cm) of the total AE rate. R15 transducer detected a lower relative
amount of signals <155 kHz than during the tension shrinkage period or at the onset of the final
shrinkage period (R15, Table 1). The AE rate/min measured by WD, R6 and R15 transducers
showed no significant differences (Fig. 4).
The bulk of AEs during lumber drying resembles cavitation events of water conducting elements [7]. Extremely high energy AEs, which were detected during the tension shrinkage period
as well as at the onset of the final shrinkage process may have been induced by mechanical failure [11]. References [12, 13] could distinguish two phases during Norway spruce lumber drying
by analyzing AE burst rates. The first peak in the AE rate was interpreted as checking caused bysurface tension stress, and the second peak (at high water loss) as checking caused by tensilestresses inside the samples. Reference [8] supposed that sapwood within-ring internal checking is
caused by water tension and not by differential shrinkage, because checking occurred long before154most of the cells reached the fiber saturation point. Internal checks caused by high tensions may
also occur in low density earlywood of living trees during periods of summer drought, and are asevere problem of short rotation forests [4].
The method presented might help to understand the mechanisms that cause internal checking
in living trees. Knowledge about the relationship between shrinkage processes, AE rates and AE
features, such as the average frequency, the peak amplitude and energy (especially single highenergy events), could be also helpful for analyzing checking processes during lumber drying
stages. It is recommended to use standardized sample sizes, because sample length had a significant impact on frequency and energy composition. Both WD and R6 transducers were suitable tocharacterize the different stages of shrinkage by analyzing the average frequency composition of
the AEs. R6 transducers reacted, however, very sensitively to background noise.

Fig. 5 Relationship between the relative amount of signals <50 kHz related to the total AE rate
and the maximum mean AE Energy/min during the tension shrinkage period detected at the sametime by R6 transducers.
Acknowledgements
This study was financed by the Austrian Science Fund FWF (Project T304-B16). Raphael
Klumpp and Peter Kritsch are thanked for providing Norway spruce sample material.
References
1. C. Skaar, Wood-Water Relations, Springer-Verlag, 1988, 283 p.
2. T. Hölttä, T. Vesala, E. Nikinmaa, M. Perämäki, E. Siivola, M. Mencuccini, Tree Physiology
25, 2005, 237-243.
3. I. Offenthaler, P. Hietz, H. Richter, Trees, 15, 2001, 215-221.
4. M. Grabner, P. Cherubini, P. Rozenberg, B. Hannrup, Scandinavian Journal of Forest Research, 21, 2006, 151-157.
155

5. M.T. Tyree, M.H. Zimmermann, Xylem structure and the ascent of sap, Springer, 2002, 283 p.
6. F.C. Beall, Wood Science and Technology 36, 2002, 197-212.
7. S. Kawamoto, R.S. Williams, Acoustic emission and acousto-ultrasonic techniques for woodand wood-based composites A Review, U.S. Department of Agriculture, Forest Service, Forest
Products Laboratory, Gen. Techn. Rep. FPL-GTR-134, 2002, 16 p.
8. R.E. Booker, Proceedings of the 4th IUFRO Wood Drying Conference: Improving Wood Drying Technology, 1994, pp. 133-140.
9. S. Rosner and R. Wimmer. 14th International Symposium on Nondestructive Testing of Wood,
Shaker Verlag, 2005, pp. 123-134.
10. S. Rosner, A. Klein, R. Wimmer, B. Karlsson, New Phytologist 171, 2006, 105-116.
11. S.L. Quarles, Wood and Fiber Science 24, 1992, 2-12.
12. P. Niemz, R. Emmler, E. Pridöhl, J. Fröhlich, A. Lühmann, Holz als Roh- und Werkstoff, 52,
1994, 162-168.
13. I. Cunderlik, W. Molinski, J. Raczkowski, Holzforschung 50, 1996, 258-262.

156

CHARACTERIZATION OF TITANIUM HYDRIDES USING A HYBRIDTECHNIQUE OF AE AND FEM DURING INDENTATION TESTYOSHIHIRO TANIYAMA, HIDEO CHO, MIKIO TAKEMOTO
and GEN NAKAYAMA*
Faculty of Science and Engineering, Aoyama Gakuin University, Fuchinobe, Sagamihara, Kanagawa 229-8558, Japan; *Research Laboratory, Ishikwajima Harima Heavy Industry,Shin-nakahara, Isogo, Yokohama, Kanagwa, 235-8501, Japan

Abstract
Pure titanium (Gr.1) and titanium-0.6 mass% palladium (Gr.17) are prone to produce brittle
hydrides when they absorb hydrogen. This research studied structures and mechanical characteristics of two hydrides in Gr.1 and Gr.17. Fracture strength of thin hydrides was measured by ahybrid method of AE and FEM during micro-indentation. We determined the threshold indentation load to cause the Mode-I fracture during Vickers indentation by AE monitoring and thencomputed the critical strain to cause the Mode-I fracture by the FEM. Utilizing the stress andstrain curves of base metals and hydrides obtained by a dual indentation method, the fracturestrain of the Gr.17 hydride is calculated as half that of the Gr.1 hydride. X-ray diffraction revealed that the Gr.1 and Gr.17 hydrdies are TiH1.971 and TiH2, repectively. Palladium in Gr.17
contributes to produce hard and brittle hydride with higher hydrogen content.
Keywords: Titanium hydride, mechanical property, fracture strain and strength, indentation,
critical indentation force, FEM, Mode-I crack
Introduction
Study is underway to assess the endurance capability of titanium-clad overpack of high-level
radioactive waste in deep underground water. The overpack will be exposed to oxygen-free
3.5% NaCl solution. Thus, pure titanium (Gr.1) and titanium-palladium (Gr.17) are likely to produce brittle hydrides by the cathodic current density (proton reduction) corresponding to the passivation holding current density. We previously reported both the fracture behavior and fracture
dynamics of Gr.1 hydride [1]. Measurement of mechanical properties of hydrides is, however,difficult since the hydrides are thinner than 100 m. We used a hybrid technique of AE and FEM
during Vickers indentation on the thin hydrides.
This study reports AE results on the progression of micro-cracks in growing hydrides in asimulated underground environment. Next we report the mechanical properties of Gr.1 andGr.17 hydrides using an indentation technique. AE was successfully utilized to determine thecritical indentation load to cause the Mode-I fracture in the hydrides. Mechanical properties ofGr.1 and Gr.17 hydrides were found to be much different, due to chemical compositions of thehydrides.
AE from Micro-fractures in Growing Titanium Hydrides
Experimental method
The amount of hydrogen charge to the overpack in underground environment is calculated tobe 10 MC/m2 for 60,000 years storage [2]. We supplied the specimens with hydrogen at cathodeJ. Acoustic Emission, 25 (2007)157© 2007 Acoustic Emission GroupFig. 1 Experimental setup for hydrogen charging and AE monitoring for Ti.

Fig. 2 Changes of the Gr.17 hydrides with hydrogen charge.
current density of 10 A/m2 in a 0.6 mol/l NaCl solution purged by nitrogen gas at 70ºC. The
amount of hydrogen charging was 1, 3, 5, 6.5 and 10 MC/m2.
Figure 1 shows the hydrogen charging and AE monitoring method for plate-shaped Ti specimens of 11-mm thickness, 50-mm width and 145-mm length. The specimen was subjected to 3point bending with surface tensile stress of 200 MPa. AE was monitored by four resonant-type
small sensors (PAC: Type-PICO) mounted on the side surfaces of the specimen. Sensor outputs
were amplified 40 dB by pre-amplifiers and fed to a personal computer.
Chemical composition of Gr.1 titanium is 0.01 mass%C-0.0007H-0.031O-0.0005N-0.032Fe
and <0.02Pd. That of the Gr.17 is 0.014 mass%C-0.0007H-0.09O-0.003N-0.035Fe and 0.062Pd.
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Growth behavior of hydrides
We studied growth behavior of Gr.17 hydride as seen in Fig. 2. Below 0.5 MC/cm2, we observed needle-shaped hydrides as shown in photo (a). Above 3 MC/m2, the needle shaped hydrides coalesces and forms colonies of dense layer of 10-m thickness. Characteristic features of
Gr.17 hydride are that these are non-uniform and irregular in shape. Above 4 MC/m2, plateshaped hydride layer is produced. At 5 MC/m2, the hydride layer grows to thick plates of 20 m
as shown in (d). Thickness of Gr.17 hydride does not exceed 150 m at 10 MC/m2, but the hydrides tend to crack on their own.
Figure 3 shows changes of cumulative AE counts with hydrogen charge for Gr.1 and Gr.17
Ti. In Gr.1 Ti, AE counts increased gradually till the charge amount of 6 MC/m2 and rapidly
increased above 6 MC/m2, while it increased rapidly above 4.5 MC/m2 for Gr.17 Ti. Sources of
AE were located using both the arrival-time difference of the So-wave and sheet velocity of 5990
m/s. All 33 events with strong So-wave were located in the corrosion cell. Figure 4 compares
the transverse structure of two hydrides produced in Gr.1 and Gr.17 Ti at 10 MC/m2. Thicknessof Gr.17 hydride layers reached approximately 120 m, while that of Gr.1 hydride is 10 m. For
Gr.17 hydride, we observed dish-shaped shallow exfoliation. It is noted that crack morphology
changes depending on the geometry and mechanical properties of the hydrides. We sometimes
observed vertical and/or slant cracks.

Fig. 3 Changes of cumulative AE counts with H charging to Gr.1 Ti (a) and Gr.17 Ti (b).

Fig. 4 Transverse SEM of Gr.1 (left) and Gr.17 hydrides after charging 10 MC/m2.

159

Fig. 5 Indentation force F vs. penetration depth h curves of Gr.1 and Gr.17 substrates by the dual
indentation method.
Mechanical properties of Ti and hydrides
We first studied whether the Gr.17 hydride suffers median crack by micro-indentation. We
observed no median crack, but slight extrusion around the periphery of the Vickers indentation,
indicating that the hydride is not so brittle like ceramics, but possesses some extent of ductility.
Next, we measured the stress-strain curves of the substrates and hydrides using a dual indentation method. Indentation load: F vs. penetration depth: h curves were first obtained using a
dynamic micro-indentation machine (Shimadzu, DUH-W201). Figure 5 compares the F vs. hcurves of Gr.1 and Gr. 17 Ti, using two indenters with different tip-angle. Details of the dual
indentation method can be found elsewhere [3, 4]. It estimates the Young’s modulus: E, yieldstrength: y, strength coefficient: R and work hardening coefficient: n of the stress-strain curves
with  =Rn in the plastic region.

Fig. 6  - curves of substrates and hydrides by the dual indentation method.
Figure 6 compares the  - curves of Gr.1 and Gr.17 Ti. Three parameters: E, R and n are
shown in the figure. The fracture strains (38% for Gr. 1 and 54% for Gr.17) of the base metals
160

were determined by the tensile tests. Experimental - curves agreed quiet well with those predicted by the dual indentation method.
Fracture strains of the hydrides
We determined the fracture strains by inducing a fine Mode-I crack using a new indentation
machine. We studied whether the Mode-I crack can be induced by Vickers indentation, using acommercial indentation machine (HVM-2000). Figure 7 shows examples of cracks induced into
thin hydride layer of Gr.1 Ti. The left figure shows the crack induced by loading up to 15 N, andthe right figure by loading to 20 N. The vertical crack is produced by tensile stress induced by
the indentation, and is the Mode-I crack. Large loading of 20 N produced both the partial exfoliation and subsequent Mode-II cracks as well the Mode-I crack. These data indicates that we candetermine the critical strain to induce the Mode-I crack by FEM method, if we can correctly determine the critical load or the critical penetration depth.

Fig. 7 Cracks induced in the hydride by Vickers indentation at 15 N (left) and 20 N (right).
The commercial indentation machine, however, does not measure the F vs. h curve. Thus, wedeveloped a new indentation machine, which can measure the F vs. h curve and used AE system
to monitor the critical load to cause the first Mode-I crack. The new indentation machine wasconstructed by modifying the micro-servo testing machine (Shimadzu, Microservo MNT1000NB-10). We controlled the load using a feedback program developed in laboratory andmonitored the penetration depth by an eddy-current displacement meter to the accuracy of 0.1
m. Construction of sliding device of the sample from a microscope to the indentation machine
was difficult. Positioning accuracy of this device is not so good as hoped for.
The critical load to cause the first Mode-I cracks was monitored by AE. We mounted two
small sensors (PAC, Type-PICO) on the two counter surfaces. Output of the sensors were amplified by 60 dB and fed to a personal computer. AE from friction between the indenter and hydridewere separated from AE by the Mode-I crack, using the polarization distribution of the first arriving So-mode waves [4]. The Lamb waves with the same So polarity was diagnosed as the AEfrom the Mode-I crack. Amplification of 60 dB is needed since the Mode-I crack is generally
less than 5 m and emitted weak Lamb waves.
Figure 8 compares the F vs. h curves for Gr.1 and Gr.17 hydrides. Vertical triangles near the
curves indicate the first AE timing from the Mode-I cracks. The critical load to cause the firstMode-I crack is determined as 19.6 N for Gr.1 hydride and 7.4 N for Gr.17 hydride. We previously reported the critical load of 16 N for Gr.1 hydride [1]. Larger critical load and penetration
depth for Gr.1 hydride than those for Gr.17 hydride indicates that Gr.1 hydride is less brittle.
161

Fig. 8 F vs. h curves for Gr.1 and Gr.17 hydrides with timing of AE from the Mode-I crack.

Fig. 9 SEM photos with the Mode-I cracks, indentations and region for FEM analysis.

Fig. 10 Elements of quater model for three dimentional FEM for strain computation during
indentation.
162

Figure 9 shows the Mode-I cracks in two hydrides. Due to poor positioning capability of thesliding device developed, indentation location could not be accurately determined. Short crack of
8.6-m length was induced in Gr.1 hydride when the indenter reached the hydride. FEM analysis
was performed in the region surrounded by dashed line. Here, the triangle by solid line designates the ridge of the indenter. Center of the indentation was originally indented in the basemetal. Figure 10 shows elements of a quarter model for 3-dimensional FEM. Lower left cornerof the triangle corresponds the center of the tip of the indenter. We computed the strain 33 in the
direction of X3 using the software package of MARC and MENTAT.

Fig. 11 Strain distribution around the Vickers indentation computed by 3-dimensional FEM.
The strains 33 were computed using the F vs. h curves of both substrates and hydrides bychanging the penetration depth (displacement controlling condition). Figure 11 shows the results.
Detail strain distribution in the X2 directin is shown in Fig. 12. The strain 33 is lower at interfaceof the base metal/hydride, but inreases toward the free surface. The critical strain is dertermined
as 0.081 and 0.043 for Gr.1 hydride and Gr.17 hydride, respectively.
Deviation of the critical load to cause the Mode-I crack in Gr.1 hydride was studied by two
graduate students (A and B) for three years. The student A reported the critical load of 16 N and
20. Student B reported, using another sample prepared by the same hydrogen charging condition,
19.6 N (this study). Average critical load for Gr.1 hydride is 18.5 N. Thus, the critical strain of
0.081 for Gr.1 hydride, obtained using 19.6 N, is supposed to be close to the critical strain
computed using the average critical load of 18.5 N. Student B attmepted to obtain more data for
Gr.17 hydride, but could not, since the location accuracy of the sliding device was poor. He
could not induce the Mode-I crack in the hydride. Thus we cannot discuss the correct criticalload of Gr.17 hydride. We examined propriety of the FEM method. Figure 13 compares theexperimetal F vs. h curve with that by the FEM. Though the curve by the FEM is slightly lower
than the experimental data, there observed a fairly good agreement, indicating a reliable
simulation of experiment by FEM.
163

Fig. 12 Distribution of 33 in the direction of X2.

Fig. 13 Comparison of F vs.h curves measured and computed by FEM for the Gr.17 hydride.
The stress-strain curves in the final form were already presented in Fig. 7. Both the fracture
strength and strain of Gr.17 hydride are smaller than those of Gr.1 hydride. This difference wasthought to be due to chemical compostion of the hydrides. We studied chemical composition of
the hydrides by X-ray diffraction method and observed a broad diffraction peaks at around 98.6º
for Gr.1 hydride and that at around 62.8º for Gr.17 hydride. According to the ASTM code 070370, Gr.1 hydride was determined as TiH1.971 and Gr.17 hydride as TiH2. Brittleness of Gr.17
hydride appears to be due to its higher hydrogen concentration. From Fig. 8, we note three AE
signals from the Mode-I cracks for Gr.17 hydride. This suggests that surface layer of the brittle
Gr.17 hydride suffers small exfoliations following the first Mode-I crack. Another example is in
the Fig. 5. We observed small multi-layer exfoliations in thin surfcae layer of the Gr.17 hydride.
Conclusion
In order to study the mechanical properties of hydrides produced in pure titanium (Gr.1) andtitanium-0.06 mass% paradium alloys (Gr.17) , a hybrid method of AE and FEM was utilized forindentation test.
164

Results obtained are summarized as below.
1) Gr.1 titanium emitted AEs by cracking at charges of more than 6 MC/m2, while Gr.17 Ti
emitted abrupt AE at 4.5 MC/m2. Thickness of the hydride layer of Gr.17 at 10 MC/m2
reached approximately 120 μm, while that of Gr.1 is 10 μm.
2) Stress-strain curves of Gr. 1 and Gr.17 Ti and hydrides were estimated by dual indentation
method. There observed a good agreement between the estimated and measured for thesubstrates.
3) Fracture strain of the hydrides were estimated using a new indnetation machine and FEM.
Critical indentation load to cause the Mode-I crack was correctly determeind by AE
monitoring. Fracture strain of Gr.1 hydride was calculated as 8.1%, while that of Gr.17
hydride as 4.3%. The Gr.17 hydride is more brittle than the Gr.1 hydride. Chemicalcomposition of the Gr.17 hydride was measured as TiH2, and that of the Gr.1 hydride as
TiH1.971 by the X-ray diffraction method.
References[1] Y. Taniyama, T. Matsuo, H. Cho, M. Takemoto and G. Nakayama, Progress of Acoutic
Emission XIII, 2007, pp. 295-302.
[2] G. Nakayama, N. Nakamura, Y. Fukaya, M. Akashi, H. Ueda, European Federation ofCorrosion Publications, Number 36, pp. 373-394, NANEY (2003)[3] M. Dao, N. Chollacoop, K.J. Van Vliet, T.A. Venkatesh and S. Suresh, Acta Materialia, 49,(2002) 3899.
[4] Ayman A. Salem, Surya R. Kalidindi, Roger D. Doherty: Acta Materialia, 51, (2003) 42254237.

165

ANALYSIS OF THE HYDROGEN DEGRADATION OF LOW-ALLOYSTEEL BY ACOUSTIC EMISSIONKRYSTIAN PARADOWSKI, WOJCIECH SPYCHALSKI,
KRYSTYNA LUBLINSKA and KRZYSZTOF J. KURZYDLOWSKI
Faculty of Materials Science and Engineering, Warsaw University of Technology,
02-507 Warsaw, Poland

Abstract
The hydrogen effect of low-alloy steels applied to the petrochemical, chemical and energy
industry is the phenomenon, which significantly influences the integrity and safety of industrial
installations. Hydrogen degradation with other operating conditions, the elevated temperature,pressure and stress, produced many material defects in micro- and macro-scale. It is critical todetermine the influence of degradation factors on mechanical properties of structural materials toassure safe operation. For the precise assessment by the AE, it is necessary to perform AE study
in laboratory conditions and collect experimental data, which will provide information foranalysis of AE measurements in the service conditions.
The study was performed on a 13CrMo4-5 steel with or without hydrogen charging. The AE
signals were measured during loading applied by a testing machine. The AE study was comparedto the investigation carried out by scanning electron microscopy, which showed many changes in
the microstructure of material after hydrogen charging. The hydrogen charging was realized bycathode polarization. This presentation shows the results of laboratory study on the application of
the AE testing during modified mechanical tests to evaluate the material degradation under thehydrogen influence and operating conditions.
Keywords: Low-alloy steel, degradation, hydrogen
Hydrogen Degradation
Systematic rise of interest in the use of hydrogen in different branches of the industry may beseen in recent years. Hydrogen is used for energetic purposes (as fuel, in synthetic fuelproduction processes involving coal liquefaction method, in refinery hydrogen processes) andnon-energetic (as a raw material in chemical industry, as reducing gas in metallurgy). In the caseof the oil refining industry, the desire of stepping up production of engine fuels is related to theinevitability of intensification of crude oil processing. It calls for starting new hydro-cracking orhydro-desulfurizing installations, and for launching new hydrogen generating plant. The material
applied must show high resistance to the destructive influence of hydrogen. The degree ofhydrogen degradation varies and depends on many factors (e.g., structural, mechanical). In thecase of steel, hydrogen damage may reveal itself as delayed hydrogen cracking, degradation of
mechanical properties, especially ductility, and can change microstructures and create bubbles
filled with molecular hydrogen, methane or hydrogen sulfide. Intensity of the material
degradation caused by hydrogen saturation, depends considerably on the speed of diffusion ofhydrogen in the given type of steel, as well as on the metal’s ability to dissolve hydrogen.
Hydrogen penetration through the low-alloy steels is very high because of high coefficient ofhydrogen diffusion through the crystal lattice of iron. Hydrogen can also be transported throughthe core of dislocation or grain boundaries, which greatly increases its penetration [1, 2]. The
J. Acoustic Emission, 25 (2007)166© 2007 Acoustic Emission Groupprocess of hydrogen destruction of metals and its alloys is complex and therefore it is notpossible to determine one form of hydrogen degradation. Predicting the form of hydrogendestruction or the degree of its intensity, and suggesting universal parameters allowing forprediction of deterioration of any material, without detailed experiments that match each case, is
not possible. Aggressive thermo-mechanical conditions, to which materials are subjected underreal operating conditions and the presence of hydrogen, create real danger of hydrogendegradation taking place. Those materials may undergo high temperature hydrogen attack, but
their hydrogenation may take place at ambient temperature as well, e.g., in petrochemical
installations where in real corrosive conditions, we also deal with hydrogen penetration.
Experimental Procedures and Results
The study was performed on a low-alloy steel 13CrMo4-5 (1Cr-1/2Mo). Results ofexperiments performed on the steel in the initial state, after cathodic hydrogen charging and after
exposure to operating conditions were compared in this study. The hydrogen charging was bycathode polarization in a 0.5M-H2SO4 solution with the addition of 5 mg/dm3 As2O3, to promotehydrogen penetration at ambient temperature. The current density used was 20 mA/cm2 for an
exposure time of 3 hrs. The samples in the exposed state were taken from cut-outs of anindustrial pipe, used for 10 years. This pipe was selected by performing non-destructive testing(NDT) that detected the reasonable hydrogen degradation. The substance transported through thepipe contained hydrocarbons, hydrogen, water and sulfur-containing fuel and had higher
concentrations of H2S. The maximum operating temperature of the pipe reached 60oC.
In order to show the effect of hydrogen on the microstructure, microscopic observations werecarried out by using a scanning electron microscope (SEM). Samples selected for examination
were chemically etched with 4% Nital. In order to determine the degree of material degradation,
hardness tests, tensile tests and fracture toughness tests were performed. Hardness tests wereconducted with Rockwell (A scale) and Vickers (HV5) methods. Tensile tests were performed atambient temperature, with a strain rate of 4.17 x 10-4 [s-1]. Fracture toughness tests were
performed at ambient temperature with compact-type samples.
Acoustic emission (AE) examinations were performed during two different, modified
mechanical tests, which were carried out with the help of the static loading machine of 5 kN
maximum force. The first of them consisted in loading of the tested surface of the material with
5–mm diameter ball indenter. In order to perform this test specially designed test set-up was
employed, which allowed for attaching AE sensor on the ball indenter. The maximum force
during test was 0.6 kN. The second test was performed on compact-type samples for plane strain
fracture toughness test with pre-cracks made before tests. Pre-cracks were made with the varying
load method at 20-Hz frequency. The lowering of the force amplitude below 3 kN was thecriterion for the pre-crack test end. The length of the pre-crack in the samples for AE tests was
about 5 mm. During AE measurements, two AE sensors (150-kHz resonant frequency) were
employed, mounted opposite on the sample surfaces parallel to the pre-crack plane. The
maximum force during tests equaled 5 kN. Schematics of the AE tests together with the results
are given later. Analysis of the registered AE signals is given as the diagrams of amplitude [dB]vs. time, which were correlated with the force graph. Those results were related to force vs.
cumulative number of events (AE hits) graph, in order to show Kaiser and Felicity effect.

167

Mechanical Tests
Hardness tests did not reveal differences between materials in the initial state and after
cathodic hydrogen charging and after exposure to operating conditions. Tensile test results
demonstrated slight decrease of plasticity of material after exposure to the operating conditionsof 5~10% in relation to the starting material. Yet all determined strength parameters of tested
materials fulfilled all standard guidelines for steel types tested. In the case of fracture toughness
tests, the sample size did not meet the criterion of plane strain state [3, 4]. For all cases, theplastic zone size at the crack tip was greater then 5% of the specimen thickness and crack surface
morphologies indicated significant ductility. Additionally, the material subjected to operating
conditions developed macroscopic structural defects, making further test impossible (Fig. 1).
Microstructural defects under the cyclic loads increased to the critical size and stopped the crack
propagation. Microstructure examination performed revealed numerous micro-structural changes
in the degraded materials. The microphotograph showing changes in hydrogen charged material
is displayed in Fig. 2.

50m
Fig. 1. Sample after fracture toughness
test, material taken from a pipe.

Fig. 2. Microstructural changes in hydrogen charged material.

60

600

55

500

50

400

45

300

40

200

35

100

hydrogen charging

0

30
20

70

120

170

220

270

320

370

50

Time [s]

60

70

80

90

100 110120 130

Cumulative AE hits

Fig. 3 AE measurement results of hydrogen charged material during indenter loading.

168

Load [N]Amplitude [dB]

In connection with problems described above, it was stated that quantitative determination of
the degree of degradation of studied materials with the help of the mechanical tests performed is
not possible.

600

55

500

50

400

45

300

40

200

35

Load [N]Amplitude [dB]

60

100

operating conditions

30

0
20

70

120

170

220

270

320

370

100

Time [s]

200

300

400

500

600

700

800

Cumulative AE hits

Fig. 4. AE measurement results of material after exploitation during indenter loading.
Acoustic Emission Study
AE study, using indenter, was performed during cyclic material loading in two measurement
sequences shown in Figs. 3 and 4. In the case of the hydrogen charged material (under laboratory
conditions), the number of accumulated AE hits was 123 (Fig. 3). The cumulative AE hits vs.
force curve (Fig. 3) shows the presence of the Felicity effect during repeated loading of thematerial between 320 s and 370 s of the test with Felicity ratio of 0.87. In the case of the material
after exposure, a higher acoustic activity was registered with clear Felicity effect (Fig. 4) duringsecond measurement sequence. Cumulative AE hits reached 758, and Felicity ratio equaled 0.35.
Material after exploitation characterized itself with higher acoustic activity during loading. In thecase of the material, in the initial state, the number of cumulative AE hits was 101 and the
Felicity effect was not detected.
In order to compare the results obtained for all three materials, relationship between severity
and historic index is shown in Fig. 5. This clearly reveals the difference between AE recorded for
degraded materials and material in the initial state.

Severity

1000

100

initial statehydrogen charging
operating conditions

10
0,9

1,4

1,9

2,4

2,9

25m

Historic Index

Fig. 5. Dependence of historic index on severity for materials loaded with indenter.

Fig. 6. SEM examination test of the hydrogen charged material, area under indentation with visible crack.

Though comparable number of signals recorded in the case of the hydrogen charged material
and material in the initial state, severity values show, that these signals are of higher energy thanthose registered in the material in the initial state. SEM inspections performed on the material
cross section under indentation surface revealed the presence of the cracks in the degraded169

materials (Fig. 6). Cracks recorded may be the potential reason of increased AE activity during
indentation tests.

90

5000

80

4000

70

3000

60

2000

50

Load [N]Amplitude [dB]

AE tests of the compact samples with pre-cracks were performed during loading of thematerial according to the schematic shown in Figs. 7 and 8. One should note that the samples
were subjected to hydrogen charging after pre-cracks were made. It could intensified thehydrogen-induced degradation at the pre-crack tip as comparing to specimens cut from thematerial after operating conditions. The atomic hydrogen during cathodic charging derived frommetal-environment surface reaction diffuses to microstructural heterogeneities at the crack-tip
such as voids and microcracks, recombines to the molecular form, and builds up a very highinternal pressure [5]. Felicity ratio of the hydrogen charged samples prepared for fracture
toughness test equaled 0.27.

1000hydrogen charging
40
0

200

400

600

800

1000

950

1050

Time [s]

1150

1250

1350

0
1450

Cumulative AE hits

90

5000

80

4000

70

3000

60

2000

50

Load [N]Amplitude [dB]Fig. 7. AE measurement results of the hydrogen charged samples for fracture toughness test.

1000

operating conditions
0

40
0

200

400

600

800

1000 100

Time [s]

200

300

400

500

600

Cumulative AE hits

Fig. 8. AE measurement, during loading, results of the samples for fracture toughness test fromthe material after exposure to operating conditions.
Characteristic of the material after exposure to operating conditions, as compared to othermaterials is the presence of AE during load decreases in the first and last cycles. Felicity ratio of
the material after exposure amounted to 0.43. In the case of the material in the initial state
number of AE hits recorded amounted to 83, and Kaiser effect was observed. Material afterhydrogen charging produced more AE signals in the first part of the test, than the material after
exposure. The different extent of the material degradation at the crack-tip could influence AE
activity during the first loading to the maximum. Higher number of cumulated AE hits 1412
170

(Fig. 7) relative to the material after exposure to operating conditions confirms this fact. This
number for the material after exposure was 573 (Fig. 8).
1000000

Severity

100000
10000
1000
initial state

100hydrogen charging

10

operating conditions

1
0,9

1,4

1,9

2,4

2,9

50m

Historic Index

Fig. 9. Dependence of historic index on
severity recorded on samples preparedfor fracture toughness tests.

Fig. 10. Fracture of the sample preparedfor fracture toughness test from the material after exposure.

Comparison of the test results performed on the compact samples is shown in Fig. 9, which
illustrates meaningful rise of severity value for material in the degraded state. In Fig. 10, one cansee fracture of the sample made of the material after exposure, with visible micro-cracks
characteristic of degraded material. Microscopic examination revealed change of the fracture
character from ductile for samples in the initial state, to locally brittle and mixed brittle – ductile(partly brittle for samples exposed to degradation).
SummaryStandard mechanical tests do not always allows the estimation of the degradation of thematerial exposed to operating conditions with hydrogen environment. The use of acoustic
emission in connection with modified mechanical examinations showed significant differences in
AE signals recorded. Suggested method of examination was devised on the basis of numerous
tests conducted on the material charged with hydrogen under laboratory conditions. On this basis
a series of tests were performed on the material taken from cut-out of the pipe operated underreal life conditions. This clearly revealed the differences in AE signals recorded in relation tomaterial in the initial state.
Acknowledgement
This work was supported by the Polish Committee for Scientific Research as research projectN507/033 31/0813.
References
1. P. Timmins, Solutions to Hydrogen Attack in Steels, ASM, Mat. Park, OH USA, 1997.
2. J.P. Hirth, in Hydrogen Degradation of Ferrous Alloys, ed. R.A. Oriani, J.P. Hirth, M. Smialowski, Noyes Pub. 1985, p. 131.
3. S.D. Antolovich, An Introduction to Fracture Mechanics, ASM Metals Handbook, Vol. 19,
1997, p. 375.
4. J.D. Landes, Fracture Toughness Testing, ASM Metals Handbook, Vol. 19, 1997, p. 402.
5. P.S. Pao, Mechanisms of Corrosion Fatigue, ASM Metals Handbook, Vol. 19, 1997, p. 186.
171

HYDROGEN RELATED BRITTLE CRACKING OF METASTABLE
TYPE-304 STAINLESS STEELHIDEO CHO and MIKIO TAKEMOTO
Faculty of Science and Engineering, Aoyama Gakuin University,
5-10-1 Fuchinobe, Sagamihara, Kanagawa 229-8558, Japan

Abstract
Among stainless steels, Type-316 steel is stable and shows high resistance to hydrogen-induced brittle cracking, but the meta-stable Type-304 steel shows a high susceptibility to
the hydrogen-induced embrittlement and cracking. Mechanism of hydrogen-induced brittle
cracking of the Type-304 steel was studied by AE monitoring under various combination of
static and dynamic elastic and plastic deformations in a charging solution at room temperatureand 80ºC. Here, the hydrogen was supplied before and during tensile testing. We detected anumber of AE signals from the Type-304 steel with hydrogen charging during dynamic plastic
deformation at room temperature and observed hydrogen-induced cracks, while no AE for the
Type-304 charged by hydrogen and deformed at 80ºC. Maximum load of the Type-304 steel withhydrogen charging decreased to 30% that of the steel without charging. Type-304 steel, hydrogen charged after being quenched in liquid nitrogen, produced few AE during tensile loading
at room temperature. We detected no AE from Type-316 steel during plastic deformation in
charging solution at both temperatures. Hydrogen-induced brittle fracture of Type-304 steel
was confirmed to be induced by both gliding dislocations and deformation-induced lath martensite. Hydrogen is supposed to be transferred to the martensite by gliding dislocations accompanying protons.
Keywords: Meta-stable austenitic stainless steel, hydrogen induced brittle fracture, strain induced martensite, gliding dislocation
Introduction
Meta-stable austenitic stainless steels are known to be susceptible to hydrogen embrittlement.
Many researchers reported that strain-induced martensite suffered hydrogen embrittlement [1, 2].
Fracture type was intergranular (IG). Carpenter et al. reported that AE signals from sensitizedType-304 steel, during tensile loading in a charging solution, were produced by the IG-cracks
caused by the grain boundary separation. Here the IG-cracking occurred when hydrogen charging was continued till the maximum load. They proposed a transport model by moving dislocations [3, 4]. Direct demonstration of this model is impossible at present, even if we use any
kind of advanced analysis equipments. Collection of reliable data, which can or cannot support
this model, is needed.
In this report, we studied the hydrogen embrittlement of Type-304 and -316 steels by monitoring AE during quasi-static loading in a charging solution at 25oC and 80oC.
Experimental procedure
We used commercially available Type-304 and -316 stainless steels.
J. Acoustic Emission, 25 (2007)

172

Chemical composition© 2007 Acoustic Emission Groupis shown in Table 1. Test specimens of Fig. 1 were prepared by an electro-discharge machine
to produce identical specimens. Width in gage section was controlled to 15 mm. A notchwith the tip radius of 0.2 mm was induced on one side. These samples were abraded by siliconcarbide abrasive papers of 150, 300, 400, 600, 800, 1000 and 1500 mesh, and mirror polished byaluminum oxide powder. The specimen was sensitized at 650oC for 24 hours in low-pressure
furnace after solution treatment at 1050oC for 1 hour, and quenched in water. The specimen
was sealed by rubber-based coating material, except for the exposure area of 15 mm x 10 mm.
Hydrogen was charged in an H2SO4 solution of pH:2, at cathodic current density of 1 mA/cm2
using a potentio-galvanostat.
Table 1 Chemical composition of austenitic stainless steel used (mass %).

We monitored AE using four small AE sensors (PAC, Type- PICO), which were mounted on
the sample as they make a line. Outputs of two sensors (channels 2 and 3 in the gage section)
were amplified by a pre-amplifier (Gain: 40 dB) and fed to a personal computer. Two moresensors (channels 1 and 4 on the shoulder) were used to discriminate the friction noise from thesignals. We monitored the crack propagation by a CCD camera.

Fig. 2 Load - time diagram for the sensitizedType-304 specimens with various crosshead
speeds in a charging solution.

Fig. 1 Schematic of specimen and sensor
location.

Experimental results
Tensile loading in charging solution
Tensile load was applied to Type-304 specimen in a charging solution using crosshead control method, in the following sequence: (1) Increase the load at constant crosshead speed. (2)When a small load drop (~0.1 kN, probably from the start of cracking) was detected, crosshead173was stopped and held. (3) Then, monitor the load decrease for 3 hour.
In these tests, hydrogen charging was started with the loading. The crosshead speed was varied between 0.05 mm/min to 0.4 mm/min. Figure 2 shows the load-time diagram at crosshead
speeds of 0.4, 0.2, 0.1 and 0.05 mm/min for the sensitized Type-304 specimen at 25oC. A finesmooth line indicates that of a non-charged specimen at crosshead speed of 0.1 mm/min. Five
specimens show different behavior in the plastic range. The specimens hydrogen-charged duringloading showed extensive load decay. Initial decay and the total load decay were larger at higher
loading rates. The curve of the charged specimen at crosshead speed of 0.1 mm/min agrees fairly
well that of the non-charged specimen, but deviates slightly at large loads. The maximum load
reached was 5 to 6.5 kN. The maximum load increased with an increase of crosshead speeds.
Figure 3 shows changes of cumulative AE counts with time. Detected AE signals were classified
by their frequency characteristics into two types (Type-A and -B) as shown in Fig. 4. Type-A
was detected after the maximum load and showed broad frequency components. In contrast,
Type-B was detected throughout loading. Frequency spectrum of the Type-B showed a limited
frequency component and resembles the noise from hydrogen gas evolution in SCC [5]. We
selected Type-A as the crack signal. We detected many signals during load increasing period,
but few signals during the initial portion of the load decay. For two samples with crosshead
speeds of 0.2 and 0.4 mm/min, we again detected signals during rapid load decay period after
incubation times. However, we did not detect AE during load decay for two specimens withlower crosshead speeds of 0.05 and 0.1 mm/min. Detail mechanism of AE generation rate andits dependency on the crosshead speed is a future problem.

Fig. 3 Cumulative AE counts for the sensitized Type-304 specimens with various crosshead
speeds in a charging solution.
Figure 5 shows CDD images of crack progression. White spots in the image are hydrogengas bubbles. Timing of crack progression from the notch bottom agreed roughly with the first AE
timing at 1,100 s, as shown by the photo (a) at 1,800 s. We observed a crack with large opening
at the maximum load (photo (c)), but after that, a fine crack during load decay (photo (d)).
Figure 6 shows post-test SEM of the sample with the crosshead speed of 0.1 mm/min. We
observed a number of IG-cracks and falling-off of grains around the notch. Here the falling-off
of grains was caused by the grain boundary separation. As these cracks were considered to be174

Fig. 4 Two types of AE signals detected and their power spectra.

Fig. 5 CCD images of crack propagation of the sensitized Type-304 specimen with crosshead
speed of 0.4 mm/min in charging solution. (a) 1800 s (after detection of first AE ) (b) 2300 s (c)2440 s (at max load ) (d) 13200 s.
175Fig. 6 SEM images of the sensitized type-304 specimen surface around the notch with crosshead
speed of 0.1 mm/min at 25oC in charging solution.

Fig. 7 Fracture surface of the sensitized sample with crosshead speed of 0.1 mm/min at 25oC in
charging solution. Both dimple and cleavage fracture surfaces are shown.
caused by strain-induced martensite, we measured ferrite amount near the main crack using aferrite meter. It was measured as 7%. Figure 7 shows fracture surface of another sample loaded
to final fracture in the same charging condition. We observed both dimple and cleavage fracture surfaces, and deep grooves along the elongated grains.
Effect of strain-induced martensite on hydrogen induced cracking
We studied effect of strain induced martensite on the hydrogen-induced cracking using
Type-304 and -316 steels. Transition temperature (Md) of austenite to martensite of the Type304 steel is 30oC, so we then compared the crack susceptibilities of the Type-304 steel at 25 and80oC. We deformed the sensitized Type-304 sample, with charging at 80oC (0.1 mm/min) anddetected no AE. The maximum load was not reached in loading up to 6 kN, while we observedhigh AE activity and the maximum load of 5.3 kN for the loading at 25oC (Fig. 3). Martensite
amount was measured as below 1.0%. This result implies that the martensite is a necessary factor
to cause the hydrogen-induced cracking.
The Md temperature of the Type-316 steel is -28oC, and this steel produces no strain-induced
martensite at 25oC. In order to confirm this, the sensitized Type-316 steel was tensile tested in176the charging solution at 25oC, by the same method as in the previous section. This steel produced
three AE during the loading to 6 kN, indicating no susceptibility to hydrogen-induced cracking,as shown in Fig. 8.
Effect of Hydrogen charging and thermal martensite
Experimental data of previous sections suggested an importance of both the martensite andgliding dislocations during plastic deformation. The test in this section intends to confirm these.
We attempted AE monitoring from the sensitized, and then nitrogen quenched and pre-charged
Type-304 steel during tensile loading in air. Hydrogen was charged for 144 hr at current density of 10 mA/cm2 (charge amount of 5184 C/cm2) prior to the loading. This specimen possesses thermally-induced martensite of approximately 15% near the notch bottom.
Figure 9 shows load-time curves of the quenched and pre-charged specimens in air. We detected no AE and no crack. Thermally-induced martensite increased the yield load by approximately 14% over that of the sensitized Type-304 steel without hydrogen charge, but is immune
from the hydrogen-induced cracking. This implies that simultaneous supply of protons andgeneration of dislocation are needed for hydrogen-assisted cracking.

Fig. 8 Load-time diagram and cumulative
AE count for sensitized 316 specimens
with charging at 25oC.

Fig. 9 Load–time diagram for the pre-charged sample quenched in liquid nitrogen in air. For comparison, sensitized sample without charging was shown.

Discussion
The martensite is a necessary condition for hydrogen-induced cracking but not the sufficientcondition, as demonstrated by the last section’s experiment using quenched samples. Figures 2
and 3 imply that both the gliding dislocation by dynamic plastic deformation and thestrain-induced martensite, and of course the diffusible protons, are necessary for hydrogen embrittlement. The grain boundary separation occurs when these three condition were simultaneously satisfied. These results agree with the results by Carpenter et al. [4]. We, however, could
not explain the complicated AE behavior during load decay in Fig. 3. These must be explained
based on the rate process of proton diffusion and martensite formation. Unsolved problems are1771) How the protons are transferred to the martensite at the tip of notch and growing crack, and2) How the martensite distributes in the austenitic grains.
Diffusion coefficient of proton in the austenitic phase is as low as 10-12 cm2/s, million times
smaller than (10-6 cm2/s) in the ferritic phase. Thus the proton can not quickly diffuse through theaustenitic grains. Fast and sufficient supplement of protons to the martensite will be possible
by gliding dislocation accompanying the protons or self-diffusion through the martensite. No
advanced analysis equipment at present can differentiate them, but we can compare the crack
velocity and proton diffusion rate under controlled stress intensity. The second problem includes a contentious martensite path for proton diffusion and the grain boundary separation.
The grain boundary separation can be explained by the martensite formation along the chromium
depleted zone. Distribution of the martensite, and possibly the initiation of crack, will be revealed by the advance analysis equipment.
We recently observed frequent grain boundary separations in polythionic APC-SCC of sensitized Type-304 steel [5]. AE were detected when the steel suffered the grain boundary separation. Detailed mechanism is not well understood. There is a possibility of hydrogen-induced
grain boundary separation in the APC-SCC.
Conclusion
In order to study the mechanism of hydrogen embrittlement of austenitic stainless steel, wemonitored AE. Results are summarized below:
1) We detected AE and crack for the sensitized type 304 when it is exposed to both the dynamic
plastic deformation and hydrogen charging simultaneously. Much AE were continuously
emitted during plastic loading at higher cross head speeds.
2) Experiment suggests that hydrogen assisted cracking occurs when sufficient protons weretransferred to the strain induced martensite. Diffusion path and trap site of protons have to
be studied in future.
References[1] M. L. Holzworth, Corrosion, 25, 1968, 107.
[2] N. Otani, S. Asano. Y. Fujishima and Y Yamamasu, J. Japan Inst. Metals, 37, 1972, 746.
[3] S.H. Carpenter and D. R. Smith, Metallurgical transaction A, 21A, 1990, 1933.
[4] S.H. Carpenter, K. Ono and D. Armentrout, Progress in Acoustic Emission XIII, 2006, p. 55.
[5] A. Yonezu, H. Cho and M. Takemoto, Progress in Acoustic Emission XIII, 2006, p. 489.

178

ANALYSIS OF ACOUSTIC EMISSION FROM IMPACT AND FRACTURE
OF CFRP LAMINATESKANJI ONO, YOSHIHIRO MIZUTANI1 and MIKIO TAKEMOTO2
Department of Materials Science and Engineering, UCLA, Los Angeles, CA 90095-1595, USA;
1
Tokyo Institute of Technology, Ookayama, Meguro, Tokyo 152-8552, Japan; 2Faculty of Science and Engineering, Aoyama Gakuin University, Sagamihara, Kanagawa 229-8558, Japan

Abstract
The impact damage of CFRP plates under impact load was evaluated using recording of AE,
load and strain history. The threshold or critical impact force to cause internal damage incross-ply CFRP laminates was determined using dynamic surface strain and Lamb-wave AE
along with the impact force history. Loading was conducted in SACMA-type impact tests at 0.8
and 1-m/s velocity. Four types of cross-ply CFRP plates ([0on/90on]s, n = 4-10) are impactedwith a spherical steel-tup. Lamb-wave AE signals were detected by small AE sensors on both
surfaces. Polarity of AE signals detected by both sensors are used in studying the progression of
fracture. Only impact-induced AE (or Impact-AE) is obtained when the impact tup contacts the
specimen at a lower speed. When internal fracture occurs, both Impact-AE and fracture-induced
AE (Fracture-AE) were detected. The nature of Fracture-AE is examined in this study in conjunction with ultrasonic C-scan and surface evaluation. AE monitoring on both surfaces enabled
us to clearly separate the S0 and A0 modes of Lamb-wave AE signals. Most of strong AE signals
correlated with matrix fracture source, while other damages had no associated distinctive events.
Keywords: Impact-AE, fracture-AE, CFRP laminates, impact damage threshold
Introduction
Carbon-fiber reinforced plastics (CFRPs) are useful in a wide range of applications due totheir high specific strength and stiffness. However, they are prone to impact damage even under modest hits. SACMA-type impact test is often used for investigating impact fracture of
CFRPs [1]. Only load history and tup velocity are taken during the test, after which samples are
subjected to compression testing. We added AE monitoring and strain sensing to the SACMA
tests and obtained the impact damage threshold for CFRP plates [2]. In so doing, in lieu of limited data, i.e., maximum impact load, impact duration and energy, we found details of the dynamic fracture processes in the form of Lamb-wave AE signals as well as displacement and surface strain history. Two types of AE signals were identified; Impact-AE and Fracture-AE. Onlya limited number of previous studies have been reported on impact damages with little materials
analysis [3-6], despite abundant CFRP studies of compression after impact are known.
In this study, waveforms of Lamb-wave AE signals due to internal damage in cross-ply
CFRP laminates are examined, in conjunction with dynamic surface strain and the impact force
history measured during SACMA-type impact tests. In particular, two-surface sensing [7-9]allowed us to evaluate mode types of detected signals. Additionally, failure modes were examined using microscopic studies to determine crack length and types and using ultrasonic C-scan
to reveal the extent of delamination. Results are compared to the observations during
quasi-static tests of the same CFRP.
J. Acoustic Emission, 25 (2007)179© 2007 Acoustic Emission GroupSpecimen and Experimental Setup
A large-size plate of ([0on /90on]s, n = 4, 6, 8, 10) was prepared by laminating pre-pregs. Carbon fibers were pitch-based XN-50 from Nippon Graphite Fiber with the nominal modulus of
490 GPa. Rectangular specimens with 150 mmL100 mmW were cut with the fiber directions(0º) on the top surface along the longitudinal (or X-) direction. Impact tests used Dynatup 8250
from Instron Corp., which satisfied SACMA SRM 2R-94 standard. Impact load was applied at
the center of a specimen via a hemispherical steel tup of 16-mm diameter, weighing 3.61 kg [2].
The edges of the specimen were clamped all around by steel flanges. Four small AE sensors(Physical Acoustic Corp.: Type Pico) were mounted on both surfaces at 32-mm from the impact
point in the X-direction. Polarity of AE signals detected by both sensors are compared for investigating the progression of fracture. A top AE sensor was used for determining the damage
initiation. Outputs of two of the AE sensors were amplified 40 dB and filtered by a band-passfilter of 200 kHz to 1.2 MHz for eliminating large-amplitude low-frequency AE due to impact.
The other two outputs were recorded directly at 10-1200 kHz bandwidth. As signal saturation
occurred with amplification, all the analysis here used only direct-recording data. These signals
were digitized by 2 PAC MISTRAS boards at the sampling interval of 500 ns over 10.2 ms, andstored in computers. The strain gage was attached on the top surface at the 32-mm from theimpact point. Outputs of the strain gage and the load cell on the impact tup were digitized at
the sampling interval of 1 s over 60-ms period. The strain gage data was used for deciding
impact damage threshold and timing. See [2] for other information. The tup displacement was
dynamically recorded with a strain-gaged leaf-spring and used for velocity measurement.
Impact tests for four types of cross-ply CFRP plates ([0on/90on]s) were conducted at two impact velocities (nominally 0.8 and 1 m/s) by controlling the height of the tup. Impact test for
the 40-ply plate at 0.8 m/s was omitted, because no damage was observed at 1.0-m/s impact test.
Details of quasi-static tests of similar CFRP plates were reported earlier and most fracture
mechanisms are expected to operate under dynamic conditions as well.
Two-surface Sensing
In thin plates, Lamb waves are usually detected as AE signals. Figure 1 gives examples of
20-μs-long waveforms from in-plane (IP) and out-of-plane (OP) pencil-lead breaks. Two Picosensors were placed on opposite faces of a 3.2-mm thick stainless steel plate, 50 mm from anedge. For comparison, the left figure is for an IP source with two sensors on the same side.

Fig. 1 Lamb waves from IP/OP pencil-lead break sources. Two Pico receivers were used.
Two Pico sensors on the same side showed similar wave character for the first 7 peaks, butdifferences became prominent after ~8 μs. The first 3.6-μs portion is S0 mode, but the arrival of
A0 mode cannot be discerned from these waveforms. IP source detected on two sides produced
180nearly same waveforms for the first 6 peaks (~8 μs), indicating this part is S0 mode. OP source (1mm from the edge) showed the initial 4 peaks at the same phase, followed by 5 peaks at the opposite phases. The opposite phase waves are A0 mode, while the initial part is S0 mode. Such OP
sources are often considered to generate primarily flexural modes, but this source was close to
the edge and reflection contributed to the symmetric mode, albeit weaker.
It is clear that, with the two-side monitoring, we can distinguish symmetric (typically S0)mode for the in-phase waves, while out-of-phase waves are asymmetric, flexural (typically A0)
mode. In CFRP plates used here, the calculated wave speed of S0 mode varies widely; it is only
about 15% faster than that of A0 mode moving over the entire thickness at 500 kHz, and here thetemporal separation of the two modes from a single source is not readily visible. However, fasterpropagating S0 mode along the 0º-fibers exists with the measured velocity of 8-10 mm/μs without much dispersion. When S0 mode or A0 mode is found by itself, a distinct source is expected
to have been active. See [7, 8] for more discussion on two-surface sensing.

Fig. 2 Impact force-displacement-velocity vs. t
for 16-ply CFRP at 1 m/s nominal velocity.

Fig. 3 Impact force vs. displacement plot of
16-ply CFRP at 1 m/s nom. impact velocity.

Impact History and Impact Damage
Measured impact force-displacement-velocity values are plotted against time in Fig. 2 for a
16-ply sample, hit at a nominal velocity of 1 m/s. The velocity at contact was 1.3 m/s, decreasing to zero at the peak load, from which point the tup reversed the direction as the load was reduced. The load vs. displacement plot is given in Fig. 3, which indicates the elastic loading and asudden slope decrease (–45%) at 600 N, where damage initiated. A corresponding loading rate
reduction is seen in Fig. 2. Most of bending displacement recovered upon unloading and the initial unload slope was unchanged from the load slope, implying minimal damage to the elastic
stiffness of the plate. Directly recorded AE signals from Pico sensors are shown in Fig. 4. Early
signals at 0.4 to 2.4 ms are Impact-AE (IAE) signals and have the flexural wave character. Fracture-AE (FAE) signals start at ~2.5 ms with 14 major events.
The first F-AE event coincides with the slope change of the load-time and load-displacement
curves (cf. Figs. 2 and 3). This signal consists of a pair of S0 and A0 packets at 2.54 ms (Fig. 5a).
The S0 packet was 20-μs long, indicating the presence of fast through-fiber mode. This signal has
a strong A0 component, which is six times stronger than the S0 packet preceding it (in p-to-p181

Fig. 4 Direct recording of AE signals from bottom sensor. 16-ply sample hit nominally at 1 m/s.
Most of top sensor signals are hidden.

Fig. 5 The first two major events in Fig. 4, each shown in 200-μs long segment for 2 sensors.
182values). Most (11 of 14) of the recognizable AE events seen in Fig. 4 are of the type with both S0
and A0 components (Fig. 5a) and are marked with an asterisk. This signal type is almost identicalto Type-D signal detected in the static tests of the same CFRP reported earlier [6, 7]. It was
attributed to matrix fracture, found on the bottom surface under the impact point, splitting
0º-fibers. This signal type (-D) was weaker, but similar in appearance to Type-A signal in thestatic testing, which was very strong (>2.5 Vpp) and was attributed to fiber fracture, which wasobserved on the top surface in real time using a video camera.
The second event starting at 2.77 ms (Fig. 5b) is mostly comprised of A0 packets when top
and bottom sensor signals have reasonable similarity (with inverting). We found three such signals, marked with an “o” in this test and referred to as Type-D’. Overall, these two events (Fig.
5a and b) resemble each other. The second type appears be of the same origin as the first except the weaker S0 portion was disturbed by interference from the remnants of prior waves.
In this sample, a large matrix crack (53-mm long) was found on the bottom surface. The
cross-sectional view of the plate (Fig. 6) shows this crack at the top. Delamination and fiber
fracture are also observed in the middle layer. Delamination was extensive according to the observation by an air-coupled ultrasonic C-scan image (Fig. 7). The extent of damage spreads to aslarge as 42 x 15 mm double-tree shape area.

Fig. 6 Cross-section of the 16-ply sample with arrow pointing to the matrix crack.

Fig. 7 Ultrasonic C-scan image of damaged area.
183

It is significant to note that, in dynamic tests, Type-D (and -D’) signals were prevalent, but
not so strong (0.6 Vpp maximum). This implies that the matrix crack proceeded stepwise underdynamic loading, although this is counter-intuitive. Stepwise development of the matrix crack
is likely to be induced by the underlying fibers, which may have to be fractured to allow matrix
crack propagation. In other test samples, delamination occurred between bottom-0º and 90º plies,
and stepwise matrix crack growth was implied from AE findings. This point needs more study.
Another notable fact is the lack of AE signals that can be traced to the large delamination
observed. All the major events are associated with the matrix crack and only the trailing parts ofsignals can explain delamination. It is feasible to recognize another signal type at 2.6 ms in Fig.
5a is, where a pair of S0 and A0 packets of similar amplitude are observed. This is characteristicof Type-C AE signals reported earlier for delamination between CFRP plies. This type is difficult to discern on waveform plots as many signals of various origins overlap as a rule. As theremaining waveform plots were surveyed, apparent S0 and A0 packets are also seen continually.
However, distinct patterns fail to emerge. Despite extensive delamination observed, we found no
distinctive AE type. It is likely that delamination developed continually without any sudden
displacement jump. Under static loading, in contrast, nearly 20% of identified signals wereType-C, attributed to delamination. Also missing are signals (Type-B) previously assigned totransverse cracks (37% in the static test). These appear to constitute mostly A0-mode signals that
followed strong Type-D signals. These differences between static and dynamic tests are expected
due to visco-elastic nature of polymeric fracture processes, which require further examination.

Fig. 8 F-AE signals of a 24-ply plate sample impacted at 1 m/s.
Three of six samples impacted at 1 m/s showed similar AE behavior described above. That is,over 60% of events were of Type-D and most of the remainder were Type-D’. The AE behaviorof two other samples (24-ply and 32-ply plates, also impacted at 1 m/s) was slightly different asshown in Fig. 8. (Last one, 40-ply plate did not produce F-AE.) The first F-AE signal was detected at 2 ms and was of Type D (denoted with *) as before and the strongest signal was also of184

Type-D (at 3.45 ms). In this sample, more A0-only Type-D’ signals were observed as marked byan “o”. In the 32-ply sample, the ratio of D’ to D was the same; i.e., 2 : 1. In both cases, durationof F-AE observation was shorter (3 or 2 ms) than the 16-ply samples (~4 ms) due to higher stiffness of thicker plates. Thus, there are more overlaps of signals between strong events, probably
converting Type-D into Type-D’.
Waveform of Type-D signal was previously simulated using the same CFRP and aline-focused laser pulse [6, 7]. Results are shown in Fig. 9, where the setup is at left, the waveform at center and its wavelet transform at right. The similarity of this waveform with that ofType-D is evident, including the period of the S0 packet preceding the A0 packet (20 μs) and theratio of A0 to S0 amplitude (5.4 : 1). The frequency of the S0 packet was higher in the simulation,reflecting the nature of the fast rise laser source. In the simulated waves, it is 500-600 kHz,
whereas the S0 frequency in Fig. 5 was 250-400 kHz. Observed A0 frequency of ~140 kHz wascomparable to the simulated A0 packet frequency. It is noted here that the frequency of A0 waves
due to I-AE was much lower at 20-40 kHz.

Fig. 9 Laser simulation of matrix cracking on the bottom surface using focused laser beam parallel to the 0º fibers. Pico sensor was 35 mm away (3 mm further than the present experiment).
Conclusions
This study reports on the utility of two-surface sensing in differentiating symmetric andasymmetric Lamb-wave modes. The method was applied to the monitoring of AE during theimpact of CFRP plates of four different thicknesses. Different failure modes were seen underdynamic loading in comparison to quasi-static loading. A large matrix crack on the back surface and extensive delamination were the main features. AE signals attributable to matrix crack
propagation were predominantly detected, while those due to delamination could not be discerned clearly. This was in marked contrast with the static case, where 4 separate failure mechanisms were correlated to 4 AE-signal types.
References
1) SRM 2R-94, SACMA Recommended Test Method for Compression After Impact, SACMA.
2) Y. Mizutani, K. Ono and M. Takemoto, Impact damage threshold of CFRP laminates andacoustic emission from impact and fracture, Progress in AE XI, 2002, JSNDI, Tokyo, pp. 23-30.
3) S.I. Ochiai, K.Q. Lew and J.E. Green, Instrumented impact testing of structural fiber-reinforced plastic sheet materials and the simultaneous AE measurements, J. of Acoustic
Emission, 1 (3), (1982), pp. 191-192.
1854) N. Liu, Q.M. Zhu, C.Y. Wei, N.D. Dykes, P.E. Irving, Impact damage detection in carbon fiber composites using neural networks and acoustic emission, Key Engineering Materials,
167-168 (1999), pp. 43-54.
5) W.H. Prosser, M.R. Gorman and D.H. Humes, Acoustic emission signals in thin plates produced by impact damage, J. of Acoustic Emission, 17, (1999), pp. 29-36.
6) A. K. Mal, F. Shih, and S. Banerjee, Acoustic emission waveforms in composite laminates
under low elocity impact, Proceedings of SPIE - Smart Nondestructive Evaluation and Health
Monitoring of Structural and Biological Systems II, Vol. 5047, 2003, pp. 1-12.
7) Y. Mizutani, K. Nagashima, M. Takernoto, K. Ono, Fracture mode classification in locallyloaded cross-ply CFRP coupons using wavelet transform, Proc. of AECM-6, ASNT, Texas,
pp.114-123.
8) Y. Mizutani, K. Nagashima, M. Takemoto and K. Ono, Fracture mechanism characterizationof cross-ply carbon-fiber composites using acoustic emission analysis, NDT & E. Intl., 33-2
(2000), pp. 101-110.
9) Y. Mizutani, Quantitative evaluation of impact force and damage of locally impacted fiber
reinforced plastics, Doctoral Dissertation, Aoyama Gakuin Univ., 2000. 137 p.

186

NEURAL NETWORK BURST PRESSURE PREDICTION IN COMPOSITEOVERWRAPPED PRESSURE VESSELSERIC v. K. HILL, SETH-ANDREW T. DION, JUSTIN O. KARL,NICHOLAS S. SPIVEY and JAMES L. WALKER II*
Aerospace Engineering Department, Embry-Riddle Aeronautical University, Daytona Beach, FL
32114 USA; *Non-Destructive Evaluation Branch, NASA Marshall Space Flight Center, AL
35812 USA

Abstract
Acoustic emission data were collected during the hydro-burst testing of eleven 380-mm(15”)-diameter filament-wound composite over-wrapped pressure vessels. A neural-network
burst pressure prediction was generated from the resulting AE amplitude data. The bottles
shared commonality of graphite fiber, epoxy resin, and cure time. Individual bottles varied bycure mode (rotisserie versus static oven curing), types of inflicted damage, temperature of thepressurizing medium, and pressurization scheme. Three categorical variables were selected torepresent undamaged bottles, impact damaged bottles, and bottles with lacerated hoop fibers.
This categorization along with the removal of the AE data from the disbonding noise between
the aluminum liner and the composite over-wrap allowed the prediction of burst pressures in all
three sets of bottles using a single back-propagation neural network. Here, the worst-case error
was 3.38%.
Keywords: Amplitude distribution, back-propagation, burst pressure prediction, composites,
filament-wound, graphite/epoxy, neural networks, nondestructive evaluation, pressure vessel.
Introduction
Acoustic Emission
Acoustic emission (AE) is a nondestructive evaluation method that involves instrumenting a
specimen with piezoelectric transducers and recording parametric representations of the waveform data from flaw growth activity in order to perform a structural integrity analysis. Analysisof the AE data allows for the determination of failure mechanisms that are active in the specimen. Consequently, it also contains information concerning the structural integrity.
Burst Pressure Prediction
The prediction of burst pressures in both damaged and undamaged filament wound composite pressure vessels has been previously accomplished using linear multivariate statistical analysis and back-propagation neural networks [1-3]. The goal of this research was to utilize a backpropagation neural network to make burst pressure predictions on 380-mm-diameter graphite/epoxy filament-wound composite over-wrapped pressure vessels (COPVs, otherwise known
as bottles) that were varied in the method of cure, type of damage, temperature, and pressurization scheme. What made this research different from its predecessors was that the disbonding of
the composite over-wrap from the aluminum liner generated multiple-hit AE data (noise), which
had nothing to do with the structural integrity of the vessels. This precluded a straightforward
solution similar to those obtained previously until the noise data were eliminated.

J. Acoustic Emission, 25 (2007)187© 2007 Acoustic Emission GroupNeural Networks
Artificial neural networks are a diverse set of robust mathematical tools used to classify data
into clusters, recognize patterns, process signals, and do predictive modeling and forecasting.
Here, an unsupervised SOM neural network was used to classify the composite failure mechanisms that occur during pressurization. The back-propagation architecture is a feed-forward design that was subsequently employed for making supervised burst pressure predictions.
Kohonen Self Organizing Map (SOM)
In composite structures, the amplitude frequencies [of occurrence] generated during damage
progression can be grouped and classified into failure mechanisms. For small data sets, thesemechanisms can be seen as “humps” in the AE amplitude distributions. Figure 1 shows the amplitude distribution for bottle SN002, an impact damaged graphite/epoxy COPV used for training
the back-propagation network, which appears to have four humps. This number of failure
mechanisms (four) was confirmed by classifying the AE amplitude data with a Kohonen selforganizing map (SOM) neural network.

Fig. 1 AE amplitude distribution for COPV SN002.
Back-propagation
The back-propagation neural network is a feed-forward, supervised neural network, that is tosay, it does not return a feedback signal to itself during each training pass, and it is necessary to
train the network on a known solution before applying it to a new case. This is called supervised
learning. A back-propagation neural network is typically constructed with an input layer, one ormore hidden layers (each composed of multiple neurons) for mapping, and an output layer.
Experimental Procedure
Pressure Vessels
Eleven 380-mm-diameter filament-wound COPVs were wet-wound on a filament winder.
The bottles were thin-walled aluminum cylinders over-wrapped with graphite fibers and epoxy
resin. The winding sequence was 3 inner-hoop plies, followed by 2 helical layers, and then 2outer-hoop plies. Eight of the bottles were rotated at slow speeds (rotisserie style) during ovencuring; three were oven cured without rotation. Four of the COPVs were tested at ambient188

temperature, while the remaining seven experienced cryogenic temperatures. Due to the natureof piezoelectric materials, it was thought that the large variation in temperatures would have asignificant effect on transducer output voltage, as well as adding to the brittle nature of the composite material.
Varying amounts of artificial damage were inflicted on the bottles in the form of impacts
from both blunt and sharp tups, as well as with the cutting of hoop fibers: five tows were cut inthe mid-hoop ply and five in the first outer-hoop ply. The strain rate was also varied nonsystematically in that the pressurization scheme used on each bottle test varied in both durationand number of pressurization ramps/holds; thus, no two bottles were pressurized alike.
The amount of diversity in some of these variables and the small number of bottles would notallow for statistical analysis of the effects of each variable. Therefore, neural networks were
employed as the primary method of data analysis. Table 1 summarizes the test variables alongwith the failure or burst pressures.
Table 1 Summary of graphite/epoxy COPV variables and burst pressures.
S/N
002
003
005
009
010
013
014
018
020
025
026

DamageImpacted
Impacted
None
None
None
None
None
Lacerated
Impacted
Lacerated
Lacerated

Test
Temperature
Cryogenic
Cryogenic
Ambient
Cryogenic
Cryogenic
Ambient
Cryogenic
Ambient
Cryogenic
Cryogenic
Ambient

Cure Type
StaticRotisserie
Rotisserie
StaticStaticRotisserie
Rotisserie
Rotisserie
Rotisserie
Rotisserie
Rotisserie

Burst Pressure[psig]
1880
2004
2960
2544
2460
2874
2390
2864
1967
2393
2675

AE Data Collection
Acoustic emission data were successfully collected from all seven of the bottles in the test
set. A multi-channel Physical Acoustics Corporation (PAC) AE analyzer was used to record the
AE flaw growth data from seven AE channels, each representing a transducer at a unique location on the test bottles. This data acquisition unit also allowed for a separate parametric input,
which was used to record a voltage signal representative of the pressure in the test specimen.
Figure 2 shows a schematic diagram of the test setup.
Six PAC-R15 AE transducers were mounted equidistant around the circumference of the top
and bottom hoop windings on each bottle. The seventh transducer was mounted near the upper
polar boss on the helically wound portion of the bottles. In the ambient temperature tests, hotmelt glue was used to bond each transducer and provide acoustical coupling between the transducer and the specimen. For the cryogenic tests, high-aqueous vacuum grease and a mechanicalhousing were used to couple the transducers to the bottles. The data sampling threshold was set
to record all AE hits that had an amplitude of 60 dB or greater.
AE Data Filtering
Even though constraints such as amplitude threshold, peak definition time (PDT=100 s), hit
definition time (HDT=500 s), and hit lockout time (HLT=500 s) were applied to the AE
189

sampling, considerable noise and multiple-hit data were still present in the raw data sets. HDT is
the minimum time that an AE event must have to be recorded, and the combination of HDT and
HLT determines the maximum time for an event before it is considered to be a multiple-hit event[4]. These settings work in real-time as data are recorded.
Multiple-hit data occur when many AE waveforms reach the transducer closely spaced intime, one after another (in a condition of buffer overrun or in the cases where HDT and HLT are

Fig. 2 Sensor positioning and test setup.

Fig. 3 Network architecture.
190

not properly set). This results in a long artificial waveform that is actually comprised of several
shorter waveforms, which will have vastly different AE parameters than single-hit data. In thiscase, the multiple-hit data are probably the result of composite disbonding from the aluminum
liner, a failure mechanism which should not affect the burst pressure.
In order to remove these multiple hits from the recorded data set, it was determined that any
AE hits having durations longer than 100 ms were to be removed. Rise time, the waveform parameter that represents the time-to-peak of the waveform, was used to further remove suspected
multiple hit data. Long rise times typically indicate multiple hits logged together. Thus, any hit
with a rise time of greater than 25 ms was also removed from the data set. The AE energy parameter is a measure of the area under the rectified waveform envelope. In the case of these
data, many hits were reported by the data acquisition system to have zero energy. These hits
were also discarded under the assumption that they were noise.
The final filter applied to the data was to select those data points to be used in the actual burst
pressure prediction. After removing all of the data prior to the start of pressurization (the acquisition hardware recorded AE parameter data before pressurization commenced), it was decided
that the first 2000 data points would provide a sufficient sample size for the neural network to
train on while still taking only those data that were acquired at or below 20% of the anticipatedburst pressure. In general, damage is inflicted on composite bottles during any pressurization
cycle; therefore, the goal was to predict on data taken at low proof pressures.
Using the final edited data set from each bottle, a frequency distribution of the amplitudes
was extracted for training and testing of the back-propagation neural network. The histogram
representation of an amplitude distribution for bottle S/N 002 can be seen in Fig. 1, and the amplitude distributions from the edited data for all eleven bottles are summarized in Table 2. The
neural network was trained to analyze the subtle differences in the distributions from each bottle
and match them to the damage type and burst pressures provided in the training set.
Table 2 Finalized amplitude distribution frequencies (with categorical variables in bold).
S/N

Amplitude Distribution Frequency Data

Burst Pressure [psig]

002

0 1 0 2 11 177 319 289 212 171 139 109 87 69 70 63 50 29 29 30 19 24 16 7
13 68 6 3 6 5 5 3 1 4 3 1 3 1 0 3 2 0 7 0

1880

003

0 1 0 2 66 452 461 268 171 130 89 76 44 37 40 25 35 20 13 17 11 4 10 7 2 41242000102000000141

2004

005

0 0 1 1 169 148 121 115 95 123 88 81 78 62 59 74 53 53 58 61 47 46 52 58
38 48 43 34 29 31 28 21 21 21 10 7 7 4 6 7 0 3 1 0 0

2960

009

0 0 1 1 39 360 452 285 177 119 88 82 70 46 42 31 40 28 32 20 13 9 8 8 6 4 6773143302000100040

2544

010

0 0 1 1 47 337 400 298 194 114 112 75 58 58 55 34 44 32 29 20 19 10 8 8 5
10 8 4 1 1 2 6 2 1 1 0 1 1 0 1 1 1 1 1 0

2460

013

0 0 1 1 71 401 380 254 169 96 76 67 61 42 44 49 42 29 23 33 16 16 16 21 15
15 12 9 7 4 10 8 7 3 0 3 0 0 1 0 0 0 0 0 0

2874

014

0 0 1 1 67 477 475 309 193 98 66 54 29 47 22 25 24 13 2 10 9 10 7 4 9 4 4 123134053111010213

2390

018

1 0 0 3 225 222 211 176 160 136 137 99 98 80 85 67 54 2 44 40 23 19 21 15
15 5 2 6 4 2 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0

2864

020

0 1 0 16 212 369 301 230 179 147 99 95 65 47 42 30 35 25 12 12 8 13 7 10 54051742312012000054

1967

025

1 0 0 3 30 224 309 294 216 176 132 102 99 76 63 39 48 36 26 19 18 15 12 10
13 8 6 5 0 3 0 0 1 0 2 5 0 1 1 1 1 0 0 7 2

2393

026

1 0 0 3 13 108 166 199 199 205 173 167 135 103 107 86 66 58 47 29 29 35
15 16 5 8 8 5 1 3 2 3 3 1 0 1 1 1 0 1 0 0 0 1 0

2675

191

Results
Network Architecture
The back-propagation neural network used herein [4] had the architecture shown in Fig. 3.
The input for each bottle consisted of a 1 x 44 dimensional vector with 3 entries representing thedamage categories (001 undamaged; 010 impacted; and 100 lacerated) plus 41 integers representing the frequency distribution of amplitudes from 60 to 100 dB (Table 2). The actual burst
pressure was also supplied as an input for error calculation at the output.
Each neuron in the hidden layer contains a hyperbolic-tangent activation or transfer functionthat can be used to approximate the shape of the amplitude distributions. A large number of neurons can be used together to approximate compound and/or discontinuous curves that will fit thetraining data well, but if trained too closely, the back-propagation neural network may not predict accurately on the test data. Too few neurons in the hidden layer will result in loosely fit
curves that will not correspond well to the training or test data. Using this approach, it was
found that 11 neurons in the hidden layer offered the network that would best fit both the training
and the test data.
Training the Network
The back-propagation neural network, generated using Neuralware’s NeuralWorks Professional II Plus software package, was trained on a total of seven COPVs, including bottles fromeach of the three damage categories (Table 3). The training set was chosen so that a high andlow burst pressure COPV from each damage category was included. The undamaged category
also included a midrange burst pressure bottle. Thus, the epoch size was seven or the total number of bottles in the training set.
After numerous experimental iterations, the optimum network architecture and input parameters were determined [4]. The learning rule was the normalized cumulative delta, and the optimal learning coefficient for the network was found to be 0.30 with a momentum of 0.40. This
relatively large learning coefficient allowed the network to train very quickly and to learn in only
71 cycles. Because the network trained so quickly, bias neurons were not employed to speed up
the training process. Transition points and learning coefficient ratios were not important either
because the default transition point of 5000 cycles was never reached, while the F’ offset was set
at 0.10.
A root-mean-square (RMS) error value on the seven COPVs in the training set is computedby the software after every training cycle to determine how well the network has trained. Herethe RMS error convergence was set at 7%. This means that training was considered complete
when the network-training curve was within an RMS error of 7% of the training data. It was
found that a higher convergence criterion left the network too loosely fit to the training data, anda lower convergence criterion forced tighter fitting of the training data but poorer fitting of thetest data.
Burst Pressure Predictions
Finally, the trained network was used to predict burst pressures for both the training and test
sets. Table 3 shows a summary of the prediction results on all the COPVs. The maximum prediction error in the seven bottle training set was -2.78%, and the maximum error in the four bottle test set was 3.38%. All of these values were well within the goal of predicting the burst pressures to within a ±5% error.
192

Table 3 Summary of training and test results

S/N
002
003
005
009
010
013
014
018
020
025
026

DamageImpacted
Impacted
None
None
None
None
None
Lacerated
Impacted
Lacerated
Lacerated

Purpose
Train
Train
Test
Train
Test
Train
Train
Train
Test
Train
Test

Burst Pressure [psig]
1880
2004
2760
2544
2460
2874
2390
2864
1967
2393
2675

Predicted
Burst Pressure[psig]
1827.597
1964.1152853.1882584.266
2432.273
2791.009
2358.271
2869.181
1999.875
2369.740
2643.174

Error
[%]
-2.78
-1.99
3.38
1.58
-1.12
-2.88
-1.32
0.18
1.67
-0.97
-1.19

Conclusions
The worst-case prediction error of 3.38% was low, and the network trained quickly in spiteof the many test variables involved. If there were any variations in the amplitude distribution
data due to cure mode, temperature, and pressurization scheme, they were automatically taken
into account by the back-propagation neural network. The fact that network training was accomplished in only 71 cycles attests to the effectiveness of preprocessing or editing the AE data
to remove the multiple-hit data and other noise.
References
1. E.v.K. Hill, J.L. Walker II, and G.H. Rowell, Materials Evaluation, 54, 1996, 744-748.
2. J.L. Walker, S.S. Russell, G.L. Workman, and E.v.K. Hill, Materials Evaluation, 55, 1997,
903-907.
3. M.E. Fisher and E.v.K. Hill, Materials Evaluation, 56, 1998, 1395-1401.
4. S.-A. T. Dion, MSAE Thesis, Embry-Riddle Aeronautical U., Daytona Beach, 2006.

193

ACOUSTIC EMISSION SOURCE LOCATION IN A THICK STEELPLATE BY LAMB MODES
M. A. HAMSTAD
National Institute of Standards and Technology, Materials Reliability Division (853), 325
Broadway, Boulder, CO 80305-3328 and University of Denver, School of Engineering andComputer Science, Department of Mechanical and Materials Engineering, Denver, CO 80208
Abstract
Through use of a validated finite-element modeling code, buried out-of-plane (twodimensional code (2D)) and in-plane (three-dimensional code (3D)) acoustic emission (AE) dipole-point-sources were operated at different depths below the top surface of a 25.4-mm (1”)
thick steel plate with large transverse dimensions. The depths ranged from a source centered at
1.25 mm below the top surface to a source centered at the mid-plane. Most of the cases were runwith a source rise time of 2.3 s. For each depth, the out-of-plane displacements were obtained
as a function of time at a series of propagation distances up to 1016 mm (40”) from the epicenter
position for the out-of-plane sources and up to 381 mm (15”) for the in-plane sources. The total
time for each signal was from the initiation of the source up to 580 s (2D) and 200 s (3D).
Since the displacements were obtained on both the top and bottom surfaces, results representing
sources at various depths over the whole plate thickness were available. The modeled signals
were examined with no filtering as well as with a 40-kHz high-pass filter or a bandpass filter of
100 kHz to 300 kHz. In order to correlate the AE displacement signals with Lamb modes, therelevant group-velocity curves were superimposed on wavelet transforms of the signals. Modal
regions that carried a significant portion of the AE energy were identified by mode, frequency
range and source depth. For sources located near the plate top surface, a Rayleigh wave was observed in the top-surface displacement signals. This wave was not present in the signals obtained
from the bottom surface or from sources not located near the top surface. Signal arrival times atdifferent propagation distances were obtained from the maximum wavelet transform (WT) coefficients at key frequencies of certain modes. Plots of propagation distance versus arrival times
were used to find group velocities for a key frequency/mode combination (102 kHz for either theA0 or S0 Lamb modes). These velocities were found to be very close to those obtained from
Lamb-wave theory. A method to identify the mode that led to the WT peak at the key frequency
was demonstrated for AE events that were detected at four sensors in an array. This method
could be automated to process digitized signals to significantly improve accuracy of source location in thick plates when nonresonant AE sensors are used.
Keywords: Acoustic emission arrival times, AE dipoles, finite element modeling, Lamb waves,source location, wavelet transform.
Introduction
The author and his co-workers have published a number of papers on the analysis of finiteelement-modeled (FEM) acoustic emission (AE) signals in a 4.7-mm thick aluminum plate [1 4]. In this thin plate, only the two fundamental modes (A0 and S0) were significantly excited over
the range of typical frequencies used in AE. One focus of this prior work was the use of a wavelet transform (WT) to obtain accurate arrival times of certain frequencies of the two fundamental
modes. Since these arrival times were all associated with specific known group velocities,
J. Acoustic Emission, 25 (2007)

194© 2007 Acoustic Emission Groupaccurate source locations could be calculated from the dispersive AE signals even in the presenceof significant electronic preamplifier noise [3].
Over the last several years we have been creating an FEM database of AE signals for a 25.4mm thick steel plate. The purpose of the research reported here was to apply a WT to identify
certain important Lamb-wave modal regions and to extract accurate signal arrival times fromthese regions for the purpose of the calculation of source locations. A recent publication [5] considered the question of the extraction of arrival times in nominal 15-mm thick steel determinedby use of the maximum magnitude of WT coefficients at certain frequencies. “Mathematicallycalculated” [5] waveforms that had been filtered to simulate a 150-kHz resonant sensor werestudied. Experimental results also were obtained from a cylindrical steel pressure vessel with anouter diameter of 800 mm and a wall thickness of 15.5 mm. Experimental waveforms from bothpressurization-activated sources and pencil-lead breaks were obtained with resonant (frequencyof 150 kHz) AE sensors. The results indicated some success and some difficulties for maximum
propagation distances of up to 360 mm.
Finite-Element Modeled AE Signal Database
To create the finite-element database used in this research, an axially symmetric (2D) code
was primarily used. This choice allowed propagation distances up to 1016 mm (40 in) without
extremely long parallel-processor computer runs. The domain size was large enough that reflections from the edges of the flat plate did not occur during the duration of the direct-path signal.
For particular comparisons, some results with a 3D code (with an appropriate large domain size)
were also used. The validations of the explicit finite-element codes can be found in publishedliterature [6, 7].
The entire FEM signals were either left unfiltered or numerically processed with a 40-kHz(four-pole Butterworth) high-pass filter or a similar bandpass filter of 100 kHz to 300 kHz. Subsequently, all the signals were resampled from the original time step (see Table 1) to 0.1 s per
point. This rate corresponds to typical sampling rates used to capture waveforms in AE experiments. The AE signals were examined out to 580 s after the source initiation time for the 2D
code and 200 s for the 3D code. The sources in the continuous mesh domain were dipoles (selfequilibrating forces from two single-cell body-force monopoles each acting on one cell, with one
cell between them) using the “equivalent body force” concept for displacement discontinuities[8]. The forces were applied with a “cosine bell” temporal dependence T(t) given by
0 for t < 0,T (t) = (0.5 – 0.5 cos [  t /  ] ) for 0  t   , and(1),
1 for t > 
where  = 2.3 s was the source rise time for most of the cases. One run was made with a 1.5-srise time to check for significant changes in the higher signal frequencies with a shorter source
rise time. No significant changes were observed. The finite-element calculations were madebased on bulk velocities and density values for steel (longitudinal velocity = 5940 m/s, shear velocity = 3220 m/s and density = 7.8 kg/m3) [9]. Based on our previous experience, the uniform
cell size was smaller for sources that were nearer to the plate surface for the 2D runs that wereprimarily used in this research. This change was made due to the presence of Rayleigh waves inthose cases. Table 1 shows the source orientations, propagation distances, source depths (depthof center of source below the top surface of the plate), uniform cell sizes, and time steps of thefinite-element calculations. The AE signals from single nodes provided the out-of-plane topsurface and bottom-surface displacement versus time corresponding to a perfect point-contact
195

sensor (pseudo-sensor) at a series of propagation distances. The use of bottom surface nodes extended the source depths shown in Table 1 without materially changing the code run times. For
example, the source at a depth of 1.25 mm below the top surface corresponds to a source at a
depth of 24.15 mm above the bottom surface when the bottom surface displacement signal wasused. In essence, one computer run provided two depths. In the case of the 3D runs, the pseudosensors were located in the zero-degree propagation direction (the in-plane x-axis direction atseveral propagation distances up to 381 mm (15”) from the source epicenter. The in-plane dipole
forces were also aligned in this direction.
Table 1 Description of finite-element database used in this study.
Source typeOut-of-plane dipole with propagation distances (in mm) of 127,
254, 381, 508, 635, 762, 1016
(2D code)In-plane and out-of-plane dipoles with propagation distanceof 381 mm (3D code)

Depth of source below top
surface (mm)
1.25
4.73
7.72
10.71
12.7
1.25
7.72
12.7

FEM: Cell size (mm) / time
step (ns)0.125 / 18.9
0.249 / 37.7
0.498 / 75.5
0.498 / 75.5
0.498 / 75.5
0.498 / 75.5
0.498 / 75.5
0.498 / 75.5

Wavelet Transform Information
Wavelet transform results were used to enhance the identification of the AE signal Lambmodes and to obtain from WT coefficient peaks the different mode arrival times at key frequencies. The WT results were obtained using the AGU-Vallen Wavelet freeware [10] with the key
parameter settings being: maximum frequency = 500 kHz, frequency resolution = 2 kHz andwavelet size = 600 samples. The Wavelet Time range settings for the number of samples (i.e.,
points) were up to about 5800 points, allowing the full-direct-arrival signal to be transformed forthe out-of-plane source runs. For the in-plane sources a total of about 2500 points was used so as
to fully correspond to the group velocity curves at the maximum propagation distance. In thecolor WT figures, the red color (surrounded by yellow) indicates the highest intensity region of
the WT coefficients. In a black-and-white print out of the color results, the darkest region inside
a lighter region indicates the high intensity region.
Group Velocity Curves for Thick Steel Plate
Using the bulk velocities and density provided above, the group velocity curves were calculated [11, 12]. The results are shown in Fig. 1 for the first five symmetric and anti-symmetricmodes. Due to the many potential modes that are present in the typical frequency range of AE
interest, this figure shows that there may be potential difficulties in mode identification in frequency regions where the group velocities are similar. This situation will be particularly true
when the propagation distances are not large, since for many frequencies the arrival times of thevarious modes will not be widely separated in time. Thus, it was initially desirable to study signals for propagation distances that were fairly large in order to more easily distinguish different
modes. Due to limitations on availability of computing resources for long runs, it was decided touse signals obtained from the 2D code for propagation distances up to 1016 mm (40”). The 2D
code can be used to calculate cases only for axisymmetric out-of-plane sources in plates, whereas
many real AE sources in metals can be primarily represented by in-plane dipoles. To establish
196

Fig. 1 Group velocities versus frequency for a steel plate of 25.4-mm thickness; first five symmetric and anti-symmetric modes shown.

Fig. 2 WT for out-of-plane dipole at a depth of
7.72 mm below the surface, 40-kHz high-pass,
3D code.

Fig. 3 WT for in-plane dipole at a depth of
7.72 mm below the surface, 40-kHz high-pass,
3D code.

whether the analysis results with the 2D code signals could be extended to in-plane source signals, we considered some comparisons of the displacement signals from out-of-plane dipole
sources with in-plane dipole sources at a propagation distance of 381 mm in the next section.
Modes Excited by Out-of-Plane versus In-Plane Buried Dipoles
Figures 2 and 3 respectively show WTs of the displacement signals (top surface) from out-ofplane and in-plane (both 3D code) buried dipoles at a depth of 7.72 mm below the top surface ofthe plate. The out-of-plane displacements were obtained at a propagation distance of 381 mm.
The WT results were obtained from the signals after applying a 40-kHz high-pass filter. The WT
results have been superimposed with the first three symmetric and anti-symmetric Lamb modes.
197

Examination of these two figures shows that the modal intensity distribution is quite similar, particularly for the three regions with the highest WT intensity. Thus, we can expect that certain results determined from the analysis of the signals with out-of-plane dipole sources can be transferred to the signals from in-plane dipole sources.

Fig. 4 WT for out-of-plane dipole at a depth of
1.25 mm below the surface, 40-kHz high-pass,
3D code.

Fig. 5 WT for in-plane dipole at a depth of
1.25 mm below the surface, 40-kHz high-pass,
3D code.

Fig. 6 WT for out-of-plane dipole at a depth of
12.7 mm below the surface, 40-kHz high-pass,
3D code.

Fig. 7 WT for in-plane dipole at a depth of
12.7 mm below the surface, 40-kHz high-pass,
3D code.

The comparison of the out-of-plane and in-plane source-generated signals at the same propagation distance for depths nearer the surface (1.25 mm) and at the mid-plane (12.7 mm) demonstrate some similarities and some differences. Figures 4 and 5 (both 3D code) show the signals
from the two orientations at a depth of 1.25 mm, and Figs. 6 and 7 (both 3D code) illustrate thedisplacement signals at a depth of 12.7 mm. At the 12.7-mm depth, the WT results are nearly
identical for the two source orientations. The main differences between the two source orientations appear for a source at a depth of 1.25 mm. Figure 4 (out-of-plane) shows a high-frequency
Rayleigh wave (see solid arrow) with the highest intensity at frequencies above about 340 kHz.
Also in Fig. 4, there is a region of lesser intensity associated with the A2 and S1 modes at frequencies above about 250 kHz. These two regions do not appear with high intensity for the in198

plane source orientation. But the lower frequency regions of highest intensity for the in-planecase (see dashed arrows in Fig. 5) do have similar regions for the out-of-plane case (see dashedarrows in Fig. 4). In Fig. 5, these lower frequency regions do not stand out, due to the higher
intensity of the high frequency regions. Thus, if only the lower-frequency regions are considered,
we can expect that most of the analysis results for out-of-plane source-based signals can be extended to in-plane source-based signals. In the later sections of this paper, the emphasis will beon lower frequency regions; thus the differences noted here will not be important factors in extending the results from out-of-plane sources to in-plane ones.
Displacement Signals and WT Results at 1016 mm Propagation Distance for Out-of-Plane
DipolesFigures 8, 9 and 10 respectively show at a propagation distance of 1016 mm the out-of-plane
displacement versus time (as a function of the out-of-plane source depth below the surface of thepseudo-sensor) for the conditions of no filtering, 40-kHz high-pass and 100-kHz to 300-kHz
bandpass (note that the fastest part of the S0 mode requires about 180 s to reach the 1016-mm
propagation distance). The corresponding WT results are shown in Figs. 11, 12 and 13 (for convenience in making comparisons arranged in the figures so that depths at symmetric distancesfrom the midplane are next to each other). Some general observations based on these figures follow. First, as can be seen in Figs. 8 and 11 for sources nearer either surface (depths (in mm) of
1.25, 4.73, 20.67 and 24.15), considerable energy was present in the lowest frequency portion of
the A0 mode (see circled regions in Fig. 11) when no filter was applied. This result is similar towhat we found in the 4.7 mm aluminum plate studies [13]. But there is an important difference.
The frequencies with significant energy in this portion of the A0 mode are less than 40 kHz in the
25.4-mm thick plate. These frequencies are below the frequencies normally used in AE monitoring. Thus, only in the case of a test environment that is free of significant low-frequency extraneous noise (coupled with the use of AE sensors with good sensitivity in the 10-kHz to 40-kHz
frequency range) could this portion of the A0 mode be exploited for AE monitoring.
Second, the signal displacements in Figs. 8, 9 and 10 show that the signal amplitudes increase gradually (from about 180 s) with increasing time, and they reach their highest amplitudes later in the signals. Thus, AE approaches that depend on threshold penetration for the determination of arrival times are likely to provide a set of arrival times that correspond to a wide
variety of propagation velocities as a function of different propagation distances and/or source
intensities. This result will be particularly true for larger propagation distances.
Third, even at the fairly large propagation distance of 1016 mm, there likely will still be difficulties in the determination of the mode that corresponds to a WT magnitude peak at frequencies above about 120 kHz due to multiple regions with intensities that change with different
source depths as shown in WTs of Figs. 11, 12 and 13. Thus, for a source location calculation, it
may not be clear as to which group velocity should be used for a certain set of WT-determinedarrival times at frequencies above 120 kHz.
Fourth, as indicated by the solid arrows in Figs. 8 and 9 (see also Figs. 11 and 12), theRayleigh wave (with frequencies above about 340 kHz) clearly appears in the displacement signals from sensors on a surface, only when the source is close to that surface (1.25 mm below the
top surface, in this case). In the WT results for a source depth of 4.73 mm, the Rayleigh wave
can still be observed (see solid arrows in Figs. 11 and 12), but it is not as easily observed in thedisplacement signals from the source at this depth.
199

Fig. 8 Out-of-plane displacement vs. time at 1016 mm, out-of-plane source at depths shown below the top surface (location of pseudo-sensor), no filter.
Fifth, sources near the midplane of the plate had a WT maximum at about 100 kHz for the S0mode, as can be seen in the WT Figs. 11, 12 and 13 at the source depths of 4.73 mm through20.63 mm (see dashed arrows). The relative intensity of this region becomes more dominant atsource depths nearer the plate midplane. Since a WT maximum in this frequency range occurs
for many depths and all three filter ranges, this frequency may be useful for the determination ofarrival times. Sixth, for some source depths nearer the midplane, the AE displacement signals
and their WTs become more similar for depths symmetric about the midplane of the plate.
The WT results shown in Figs. 11, 12 and 13 were examined to identify potential higher intensity mode and frequency regions that might be used to determine arrival times that could be
associated with particular group velocities. Table 2 summarizes this information with the highest
intensities listed first for each source depth. The table does not include high intensity regions
where the mode could not be clearly distinguished. This situation occurred most often at higher
frequencies for some source depths. Since a key goal of this research was to determine key frequencies associated with specific modes that could be used to determine accurate arrival times
corresponding to a known group velocity, the next section attempts to identify useful frequency/mode combinations for this purpose.
200

Fig. 9 Out-of-plane displacement vs. time at 1016 mm, out-of-plane source at depths shown below the top surface (location of pseudo-sensor), 40-kHz high-pass filter.
Table 2 Modes and frequency with larger WT magnitudes after 1016 mm of propagation.
Anti-symmetric modes: mode/frequency Symmetric modes: mode/frequency
(kHz)
(kHz)
SourceNo filter
40 kHz
100 to 300
No filter
40 kHz
100 to 300
depth (mm)high-passkHz
high-passkHz
1.25
A0/20;
A0/420;A0/318;
A0/420A0/52
A0/352
4.73
A0/374;
A0/374
A1/192
S0/102
S0/102
S0/102
A0/14
7.72
A1/186
A1/186
A1/186
S0/102
S0/102
S0/102
10.71
S0/102
S0/102
S0/102
12.7
S0/102;
S0/102;
S0/102;
mid-plane
S1/276
S1/276
S1/272
14.69
S0/102
S0/102
S0/102
17.68
A1/182
A1/182
A1/184
S0/102
S0/102
S0/102
20.67
A0/278
A0/186;
A0/266;
S0/102
S0/102;
S0/102;
A1/278
A1/186
S0/268
S0/256
24.15
A0/20
A0/54
A0/138

201

Fig. 10 Out-of-plane displacement vs. time at 1016 mm, out-of-plane source at depths shown below the top surface (location of pseudo-sensor), 100 to 300 kHz filter.
Useful Frequency/Mode Combinations to Determine Arrival /Times
Examination of the information in Table 2 demonstrates that the highest intensity (largest
WT coefficients) mode/frequency combinations change as a function of source depth. There are
four conditions that might be used to guide the selection of frequency/mode combinations that
could be most useful to determine arrival times. First is the selection of frequencies that would
be applicable for all three frequency ranges considered here. Second, frequency/mode combinations that cover a wide range of source depths for each combination are desirable. Third, themode corresponding to each of the selected frequencies should be separated from any alternatemode(s) with intensity at these frequencies by a considerable amount of propagation time (orequivalently have group velocities that are significantly different). Finally, as is obvious, the WT
maximum(s) at the frequencies selected should correspond to the mode arrivals.
To best meet the conditions listed above, the frequency selected was 102 kHz. This frequency can be associated only with the A0, A1 or the S0 modes, and it is possible to use thischoice for all three of the frequency ranges. Further, as will be seen below, the A1 mode did notprovide a peak of the WT coefficients at this frequency, and the group velocities associated with
the A0 and S0 mode peaks are very different at 102 kHz. It should be noted that the combinationof A0/102 kHz is not among the most intense modal regions at the 1.25 mm and 24.15 mm
202

Fig. 11 WTs of Fig. 8 with superimposed group velocities, at indicated depths, 180 μs to 570 μs
horizontal scale, 0 kHz to 500 kHz vertical scale.
depths. But, since earlier work [3] demonstrated that accurate arrival times could be determinedat low signal-to-noise ratios, the lower intensity of this combination at these depths is not expected to be detrimental to its use.
203

Fig. 12 WTs of Fig. 9 with superimposed group velocities, at indicated depths, 40-kHz highpass, 180 μs to 570 μs horizontal scale, 0 kHz to 500 kHz vertical scale.
For all the source depths and propagation distances the arrival times, Table 3 provides themode and the WT peak magnitude percentage (relative to the signal overall peak WT magnitude)
at a frequency of 102 kHz for the 40 kHz high-pass data. Figure 14 shows (from top to bottom) a204

Fig. 13 WTs of Fig. 10 with superimposed group velocities, at indicated depths, 100 kHz to 300
kHz, 180 μs to 570 μs horizontal scale, 0 kHz to 500 kHz vertical scale.
typical AE displacement signal, a plot of the WT coefficients at 102 kHz versus time that lead tothe maximum and associated arrival time (in this case from the S0 mode), and the corresponding
WT. The data in this figure are from a source depth of 7.72 mm and a propagation distance of205

Fig. 14 Example from top to bottom of the signal, plot of the WT coefficients that lead to thepeak arrival time (see arrow) at 102 kHz and the wavelet transform with group velocity curves at
a propagation distance of 762 mm and a source depth of 7.72 mm.
762 mm. In addition, Table 3 lists the group velocities as directly determined from the slope of aplot of propagation distance versus the arrival times in the table. Finally, these group velocities
can be compared in the table to the velocities (last column) determined from the group velocity
curves (theory) for the relevant modes. The results in Table 3 show that group velocities very
close to the theoretical values can be determined for the only two modes, A0 and S0 that produced
the peak in the WT magnitude at 102 kHz. Results similar to those in Table 3 were obtained forthe other two frequency ranges. The group velocity results for these two frequency ranges are
summarized in Table 4 along with the “theory” velocities from the group velocity curves. The
results show that the plots of propagation distance versus the arrival times again provide groupvelocities close to the theoretical values for these two frequency ranges. The maximum difference from the group velocity as determined by the group velocity curves and that from the slopeof plots of distance versus arrival time was less than 3% for all three frequency ranges. These
results imply that arrival times determined by the peak of the WT at 102 kHz accurately correspond to the relevant mode arrivals.
Of special interest was the fact that the data points for 127-mm propagation distance arrival
time fit directly on the linear line that defined the group velocity obtained from the FEM data.
Figures 15 (a) and (b) show typical plots (no filter data) for the propagation distance versus thearrival time for both the relevant mode cases, and they include the straight-line least-squares fit.
It is worth noting that, at the 127-mm propagation distance, the ratio of distance over the plate
thickness is only 5. It often has been assumed that a ratio of at least 10 could be required to fully
develop Lamb waves [14].

206Table 3 At 102 kHz, WT peak arrival times (s), mode and percentage of peak WT magnitude
for 40 kHz high-pass data.
Propagation distance (mm)Source
depth
(mm)
1.25
4.73
7.72
10.71
12.7
14.69
17.68
20.67
24.15

127

254

381

508

635

762

1016

41.9,
A0, 8164.7,
S0, 5464.7,
S0, 9564.7,
S0, 9564.7,
S0, 9564.7,
S0, 9564.8,
S0, 81
65.0,
S0, 5737.5,
A0, 5078.5,
A0, 98131.2,
S0, 74131.0,
S0, 97130.9,
S0, 99130.9,
S0, 99130.9,
S0, 99130.9,
S0,100131.2,
S0, 63
81.6,
A0, 67123.,
A0, 42
198.9,
S0, 55198.8,
S0,100198.8,
S0,100198.8,
S0,100198.9,
S0,100199.0,
S0, 87199.1,
S0, 62
122.3,
A0, 76

162.5,
A0, 55269.2,
S0, 88269.9,
S0, 96270.2,
S0,100270.2,
S0,100270.1,
S0,100269.6,
S0,100268.8,
S0, 60163.6,
A0, 59200.8,
A0, 52339.6,
S0, 68341.4,
S0,100342.2,
S0,100342.4
S0,100342.3,
S0,100341.6,
S0,100340.1,
S0, 60203.2,
A0, 80243.2,
A0, 55410.2,
S0, 61
411.2,
S0,100411.7,
S0,100411.8,
S0,100411.7,
S0,100411.1,
S0,100409.9,
S0, 67242.8,
A0, 74321.3,
A0, 48549.3,
S0, 65549.5,
S0,100548.8,
S0,100548.7,
S0,100548.8,
S0,100548.9,
S0,100549.5,
S0, 76323.1,
A0, 76Group
velocity(mm/s)
from
slope

Group
velocity(mm/s)fromtheory

3.16

3.18

1.83

1.78

1.83

1.78

1.83

1.78

1.83

1.78

1.83

1.78

1.83

1.78

1.83

1.78

3.13

3.18

Table 4 Summary of slope-determined group velocity results for no-filter and 100 kHz to 300
kHz data.
Source depth (mm)and mode of WT
peak at 102 kHz

Group velocity(mm/s) from slope
for no-filter data

1.25, A0
4.73, S0
7.72, S0
10.71, S012.7, S0
14.69, S017.68, S020.67, S0
24.15, A0

3.16
1.83
1.83
1.83
1.83
1.83
1.83
1.83
3.13

Group velocity(mm/s) from slopefor 100 kHz to 300
kHz filter data
3.15
1.83
1.83
1.83
1.83
1.83
1.83
1.83
3.10

Group velocity(mm/s) from groupvelocity curves(“theory”)
3.18
1.78
1.78
1.78
1.78
1.78
1.78
1.78
3.18

Further, Fig. 16 shows the wide variation in the signal durations for the different propagation
distances. The primary part of the signals varied in duration from about 50 s to about 400 s as
the propagation distance increased from 127 mm to 1016 mm. Further, over this range of distances The primary part of the signals varied in duration from about 50 s to about 400 s as the207

Fig. 15 Plot of propagation distance versus arrival time determined by WT maximum at 102
kHz; no filter data for an out-of-plane source depth of (a) 24.15 mm and A0 mode and (b) 7.72mm and S0 mode; and equation of “fit” with correlation coefficient.

Fig. 16 Dispersion of AE signals at propagation distances shown for source depth of 7.72 mm;
40-kHz high-pass data.
propagation distance increased from 127 mm to 1016 mm. Further, over this range of distances
the signal peak amplitudes decreased by 12 dB to 19 dB from the first distance to the last for thedifferent depths and filter ranges. These large changes in durations and signal peak amplitudes
did not affect the determination of accurate mode arrival times.
Two additional frequencies were examined (190 kHz and 50 kHz). In both cases, the modethat led to the WT peak magnitude was not constant at all the propagation distances for somesource depths. Thus these frequencies were not pursued in detail.
208

Fig. 17 Frequency of maximum WT intensity of the S0 mode as a function of plate thickness foraluminum and steel plates.
Since the region of the S0 group velocity curve that gave rise to the WT peak at 102 kHz is
the same region that provided accurate arrival times for this mode at 522 kHz in the 4.7-mm
thick aluminum plate [1], Fig. 17 was created to show how this frequency changes with plate
thickness for both steel and aluminum. Potentially this figure could be used to select appropriate
frequencies for plates of steel or aluminum of other thicknesses. These frequencies could be used
to determine accurate arrival times for the S0 mode (and at depths near a plate surface the A0mode, as demonstrated in this paper) by a WT or other equivalent approaches.
Application to Source Location and Related Mode Identification of the Single Frequency(102 kHz) Determined Arrival Times
Because in an actual experimental AE application the source depth for each AE event is unknown, a WT peak-determined arrival time at 102 kHz could be associated with either the A0 or
the S0 mode (Note as pointed out before, no WT peaks at 102 kHz were observed with the otherpossible mode, A1). Thus, we examined a possible approach that would allow determination of
the mode that resulted in the WT-determined peak arrival time for a particular experimental AE
event. It was assumed that the signals from the event were recorded at four different sensors in alocation array. Using the available different propagation distances, a series of cases was examined with different placements of the sensors around a fixed source location. Figure 18 shows aparticular case of the arrangement of the sensors around a source location (indicated by X in the
figure). The arrival time information in Table 3 (40-kHz high-pass data) was used to calculate
relative arrival times for the appropriate propagation distances. For each sensor array case, thearrival times selected corresponded to those for two different source depths that provided data forthe two possible modes (A0 and S0). Planar source location software [15] was used to calculate
the location by using each of the two possible group velocities (those found from the slope plots
referred to earlier). Thus, for each case, a total of four locations were calculated with the 102
kHz data. These calculations were: (i) A0 mode arrival times and A0 mode velocity, (ii) A0 mode
arrival times and S0 mode velocity, (iii) S0 mode arrival times and A0 mode velocity, and (iv) S0
209

mode arrival times and S0 mode velocity. From the results for each location calculation, the radius error from the actual known location to the calculated one was determined. It was found thatthe mode velocity that gave the least radius error corresponded to the known actual mode that
corresponded to the set of WT peak determined arrival times for that mode.
Table 5 Sensor coordinates, arrival times, radius errors and uncertainty (“Lucy”)*.
Radius error at specified mode
velocity(mm)

Relative arrival times at
Array of sensor coordinates x/yeach sensor for the(mm)modes, A0/S0 (s)
Case
no.
#2
#3
#4
A0
#1
#2
#3
#4
#1
1
1410
1011.1 1697
906.8
0 44.5 122.3 164.7 9.62$
1356.4 1204.8 1942.3 2219
0 67.7 208.4 279241$$
21117.6 1327.7 1443.7 737.4
0 36.6 158.9 201.3 8.65
1447.7 1294.4 2118
2114.5 0 66.5 274.9 345.5 
31121.9 1326.1 1595
886.6
0 36.6 81.1 120.6 4.6
1442.4 1293.6 1586.9 1908
0 66.5 134.2 204.5 166
41190.6 1385.2 719.4 2234.1 0 36.6 120.6 279.4 5.2
1647.7 1331.7 1433.2 1570.1 0 66.5 204.5 484.6 
51436.6 1092.9 1840.3 586.7 0 39.5 77.8 198.3 5.8
1211.1 1032
1656 2319.1 0 70.3 140.7 350.4 234
61175.6 1144.1 1585.4 1585.9 0 36.6 81.1 120.6 7.1
1643.3 1281.4 1419 1875.6 0 66.5 134.2 204.5 
71306.3 609.1
1759
823.8 0 158.9 201.3 279.4 4.7
1616.4 1347.9 986.1 2459.9 0 274.9 345.5 484.6 
81117.1 1467.1 913.3 1622.7 0 44.5 122.3 242.8
81756.6 1234.8 967.5 2456.4 0 67.7 208.4 418.1 
91444.1 1070.7 623.6 1983.7 084
122.3 242.8 5.2
1405.9 1038.2 1744.3 2193.2 0
138 208.4 418.1 68.3
101085
937.4 1702.6 1839.9 0 44.5
84122.3 9.1
1739.6 1267.6 1367.9 1658.2 0 67.7
138 208.4 142* Actual source coordinates for all cases x = 1219.2 mm and y = 1524.0 mm
 … No result$
With velocity 3160 m/s for A0 mode and arrival times for A0 mode$$
With velocity 3160 m/s for A0 mode and arrival times for S0 mode##
With velocity 1828 m/s for S0 mode and arrival times for S0 mode#
With velocity 1828 m/s for S0 mode and arrival times for A0 mode

S0
115#
2##
145
4.2
105
5.6
190
4.9
143
3.1
90
5.6
178
3.2
232
0.9
210
2
73
2.2

“Lucy”
values at
specifiedmode velocity
(mm)
A0
S0
2.2 17.6
15
1.6
0.30 55.3

1.01
5.4 33.6
103 1.5
1.7 152
 0.83
2.2 20.4
8.9 0.78
2.7 41.7
 0.93
0.7 181 0.730.3 145
1.9
4.7 95.4
326 0.2
0.55 31.1
49.4 0.22

Table 5 provides a summary from a series of ten cases. The table shows the coordinates ofthe four sensors in each array along with the relative arrival times for each mode. The table also
shows the radius errors for each of the two possible group velocities. Due to the nature of the database used for these calculations, the source location (x = 1219.2 mm, y = 1524 mm) was always the same for each case.
The results summarized in Table 5 indicate that accurate source locations (smaller radius oflocation errors, bold values) were consistent with the correct choice of the mode and associatedgroup velocity. In some cases in this table, when the incorrect group velocity was used, the210

Fig. 18 Arrangement of sensors (#1, #2, #3, #4) around source location X (x = 1219.2 mm, y =1524 mm). Scale shown as well as the directions of the x and y axis (origin not shown).
source-location software did not generate a result. Examination of the input data for those cases
showed that the equation of the hyperbola that should locate the locus of possible locations of thesource between each two-sensor set had a negative sign for the distance from the closest sensor
to the hyperbola. This situation typically occurred when the higher A0-mode velocity was usedwith the slower velocity S0-mode arrival times. In addition, significant errors in the calculated
source locations were the result of the use of the wrong mode and its associated group velocity.
These facts indicated that a measurement of the “uncertainty” level of the iterative source location calculation might be used to determine the correct mode and associated velocity for a particular set of arrival times from an experimental source at an unknown depth. The sourcelocation software [15] automatically provided an “uncertainty” value for each location calculation. Table 5 also shows this “uncertainty” parameter (called “Lucy” [for location uncertainty] by
the software developer) for the two possible group velocities in each case. According to thesoftware designer [15] “Lucy describes how well a calculated source position fits with the measured arrival time differences.” The value for “Lucy” is calculated from
Lucy = {(1/ [n–1]) * [ (Di – Pi)2]}1/2.

(2)

In this equation, the sum is from i = 2 to n, where n is the number of sensors hit by the signals
from each source event. The values of Di are calculated from
Di = dti * v,

(3)

where dti is the signal arrival time difference relative to the first hit sensor (n = 1), and v is thegroup velocity. The values of Pi are calculated from
Pi = Ri – R1,(4)

where Ri is the distance from the calculated source location to each of the sensors. “Lucy” can be
determined as part of the result of a location calculation only if an event consists of at least one
more hit than the minimum required to determine a location (in the cases considered here, threehits are the minimum for planar location). Clearly the table shows the “Lucy” value is the smallest (bold values) for the velocity that resulted in the least radius error. Thus, either the smallest“Lucy” value (directly identifies the correct mode) or no result (identifies the alternate mode velocity is correct) for the location calculation identified the correct mode and associated group velocity for the wide variety of sensor arrays examined.
211

Results equivalent to those in Table 5 were obtained for the other two frequency ranges (nofilter and 100 kHz to 300 kHz). These results (not included in this paper) were similar to thoseshown in Table 5, and the conclusions relative to the relation between the smallest “Lucy” values, smallest radius error and correct group velocity were similar. In addition for all three frequency ranges, the average radius errors for the slower velocity mode were only about one half
the average radius errors for the faster velocity mode. For example, the average radius errors respectively for the slow versus fast velocities were respectively 3.6 mm (standard deviation of ±1.7 mm) and 7.1 mm (standard deviation of ± 1.7 mm) for the 100-kHz to 300-kHz bandpass
data.

Fig. 19 Radius error and “Lucy” value versus group velocity in the vicinity of the correct velocity. Results for case 1 in table 5 with (a) for the S0 mode and (b) for the A0 mode.
The sensitivity to small errors in the velocity used in the approach (described above) to determine the correct mode and group velocity (for a set of arrival time differences) was studied.
Calculations of the radius errors and “Lucy” values were made when the velocity was in error
relative to the correct value for the mode. The velocity errors examined were ±2%, 5%, 10%, and
15% from the correct mode velocities. Figure 19 shows a typical result of how the radius error
and the “Lucy” values varied as a function of the group velocity used in the location calculation.
Figure 19(a) shows the results for the slow velocity mode (S0), and Fig. 19(b) shows the results
for the fast velocity mode (A0). These results for case 1 in Table 5 (using the 40 kHz high-passdata) show that the radius error and the “Lucy” values have minima near the correct velocity. It
is worth noting that the minima of both parameters are not at exactly the same velocity. The factthat the radius error and “Lucy” values have minimum values near the correct velocity indicatesthat in experimental situations where there may be small errors in the mode velocities used, thetechnique proposed here could be expected to continue to result in correct identification of themode that resulted in the set of arrival times for a particular AE event.
The method described in this section of the paper could be automated in software. Arrival
times at an appropriate frequency could be determined by advanced signal processing of the digitized signals from each sensor in a location array for each event. Then location calculations
could be made by using the two possible group velocities at the appropriate frequency. The
212

location calculation result that yielded the lowest “uncertainty” or the calculation that produced alocation (no location result for one velocity) could then be selected. Thus the use of nonresonant
sensors could improve the accuracy of source location even in thick plates, as has been shown inthin plates [16].
Conclusions
•
•
•
•
•

•

•

•
•

The wavelet transforms of signals from in-plane and out-of-plane dipoles at the same
depth demonstrates mostly similar regions of signal intensity.
For sources near the top or bottom surface of the thick plate, the most intense portion of
the A0 mode is at frequencies below those normally used in AE monitoring.
For sources near a surface, the signal from a sensor on that surface exhibits a Rayleighwave at frequencies above about 340 kHz
The signals from the 25.4-mm thick plate have multiple intense regions (combinations ofmode and frequency) as the depth of the source varies.
The choice of a frequency of 102 kHz provides wavelet transform peak determined arrival times for mostly the S0 mode as the depth of the source varies. For sources near aplate surface, the wavelet transform peak at this frequency corresponds to the A0 mode.
No maximum peaks were observed for the A1 mode at this frequency.
Over a wide range of propagation distances (127 mm to 1016 mm) and source depths, thewavelet transform peak at 102-kHz frequency provided accurate arrival times, since theslope of plots of the propagation distance versus these arrival times provided group velocities very close to the theoretical values.
For sources at an unknown depth recorded with at least a four-sensor array, the calculation of the planar location of the source from the WT peak arrival times at 102 kHz using
the two possible group velocities yields two different locations or in some cases only alocation with one of the two group velocities. The lowest value of the “uncertainty” or the
only velocity that produced a location result from the two source location calculations
corresponds to the correct location and the correct mode (and associated group velocity)of the arrival times.
This method for the determination of the correct mode of the arrival times and associatedgroup velocity could be automated to process digitized signals to significantly improveaccuracy of source location even in thick plates when non-resonant AE sensors are used.
This method is not dependent on the use of exactly the correct group velocity for the twomodes at 102 kHz.

Acknowledgements
The development of the finite element code and case runs by Dr. John Gary (NIST retired)
are acknowledged. Filtering of the FEM results by Ms. Abbie O’Gallagher, NIST Boulder, CO isappreciated. Also the analysis efforts of Mr. Matthew Papa (University of Denver, MechanicalEngineering undergraduate) are appreciated. Finally, the private communications listed in thereferences are highly appreciated.
Copyright NoticeThis paper is a contribution of the U.S. National Institute of Standards and Technology; not subject to copyright in the United States. Trade and company names are included only for complete
scientific/technical description; endorsement is neither intended nor implied.
213

References
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source location by a wavelet transform,” J. of Acoustic Emission, 21 (2003), 70-94 and A1-A7.
2. Downs, K.S., M.A. Hamstad and A. O’Gallagher, “Wavelet transform signal processing todistinguish different acoustic emission sources,” J. of Acoustic Emission, 21 (2003), 52-69.
3. Hamstad, M.A. and A. O'Gallagher, “Effects of noise on Lamb-mode acoustic-emission arrival times determined by wavelet transform,” J. of Acoustic Emission, 23 (2005), 1- 24.
4. Hamstad, M.A. and A. O'Gallagher, “Modal-based identification of acoustic emission sourcesin the presence of electronic noise,” J. of Acoustic Emission, 22 (2004), 1-21, A1-A7.
5. Bayray, Mulu and Franz Rauscheer, "Wavelet transformation analysis for experimental AE
waveforms on steel pressure vessel," J. of Acoustic Emission, 22 (2006), 22-43.
6. Hamstad, M. A., J. Gary and A. O'Gallagher, "Far-field acoustic emission waves by threedimensional finite element modeling of pencil breaks on a thick plate," J. of Acoustic Emission,
14(2) (1996), 103-114.
7. Hamstad, M.A., A. O'Gallagher and J. Gary, "Modeling of buried acoustic emission monopoleand dipole sources with a finite element technique," J. of Acoustic Emission, 17(3-4), (1999), 97110.
8. Burridge, R. and L. Knopoff, “Body force equivalents for seismic dislocations,” Bulletin
Seismic Society of America, 54 (1964), 1875-1914.
9. Kolsky, H., “Stress Waves in Solids,” Dover Publications, New York (1963).
10. Vallen, J., AGU-Vallen Wavelet transform software, version R2007.0309, Vallen-Systeme
GmbH, Icking, Germany (2007). Available at http://www.vallen.de/wavelet/index.html
11. Cho, Hideo, Private communication, Aoyama Gakuin University, Sagamihara, Japan (2006).
12. Vallen, Jochen and Gabriel Corneanu, Private communication, Vallen-Systeme GmbH, Icking, Germany (2006).
13. Hamstad, M.A., A. O’Gallagher and J. Gary, "Examination of the Application of a Wavelet
Transform to Acoustic Emission Signals: Part 1. Source Identification," J. of Acoustic Emission,
20 (2002), 39-61.
14. Sachse, Wolfgang and Yih-Hsing Pao, “Signals in the far-field of an AE source,” Ultrasonics
International 1981: Conference Proceedings, IPC Science and Technology Press, Guildford,
Surrey, UK, 1981, pp. 116-122.
15. Vallen, Jochen, Private communication, Vallen-Systeme GmbH, Icking, Germany (2001).
16. Ziola, Steven M., "Source location in thin plates using crosscorrelation," Ph.D. Thesis, Naval
Postgraduate School, Monterey, CA., (1991).
214

NOVEL ACOUSTIC EMISSION SOURCE LOCATION
RHYS PULLIN, MATTHEW BAXTER, MARK EATON, KAREN HOLFORD
and SAM EVANS
Cardiff School of Engineering, The Parade, Newport Road, Cardiff, CF24 3AA, UK

Abstract
Source location is the most attractive aspect of acoustic emission (AE) monitoring. Source
location relies on the ability of several sensors to detect an AE event and locate the event using
simple triangulation methods based on the time of arrival of the fastest propagating wave mode.
Two assumptions are made in any source location calculation; the wave speed remains constantand that there is a direct path between sensor and source. This is rarely the case with many structures containing change in thicknesses, resulting in changes in propagation velocity and discontinuities, such as lugs, that alter propagation paths.
A novel technique, Delta-T mapping, that overcomes the problems of thickness changes anddiscontinuities has been developed. An outline of the novel technique and the results of an artificial source investigation and two fatigue investigations one with a complex propagation path anda further specimen with varying thickness changes are presented. All investigations demonstrated
that the Delta-T mapping has a great improvement over the current TOA technique.
Keywords: Source location, composites, aerospace materials, complex geometries.
Introduction
Source location is possibly the most attractive aspect of acoustic emission monitoring. If thelocation of an event is known, the number of possible source mechanisms is reduced as only certain mechanisms are associated with particular geometric features and conditions. In addition
identifying the location of an AE event can allow other non-destructive evaluation (NDE) techniques, such as dye penetrant or ultrasound, to be utilised.
Current source location techniques rely on the ability of several sensors to detect an AE eventand locate the event using simple triangulation methods based on time-of-arrival (TOA) of thefastest propagating wave mode [1,2]. However these source location methods are based on two
assumptions; that wavespeed remains constant from source to sensor and that there is a direct
wavepath between source and sensor.
When considering plate waves, invariably the case in bridges, pipes, pressure vessels andaerospace landing-gear modules, the velocity is dependant on thickness of the plate it is travelling in, demonstrating that wavespeed throughout a structure will rarely be uniform. Furthermore, in composite structures wave velocity varies with the angle of propagation depending on
the direction of travel when compared with composite lay-up. In addition geometric features
such as holes, lugs and structural discontinuities, often seen in real structures, can dramaticallyalter the wavepath. Indirect paths may include reflection, refraction, diffraction and paths depending on the geometry of the component again.

J. Acoustic Emission, 25 (2007)215© 2007 Acoustic Emission GroupThese assumptions introduce errors in a source location measurement. In simple cases, these
problems can be overcome with expert knowledge, assessment of wavepaths and intelligent sensor locations. However, these are estimations and cannot provide accurate results.
Recent methods aimed at improving the accuracy of the source location technique by alleviating the problems inherent in selection of a single wave velocity include modal analysis, amethod based on energy attenuation and acoustic tomography.
Single sensor source location [3,4] relies on the measurement of the time difference between
the arrivals of the two primary modes, however it requires a detailed understanding of the dispersive nature of AE signals, which can be open to user interpretation and the arrival of the fasterpropagating S0 mode needs to captured and visible when compare with background noise.
Nivesrangsan et al. [5] have investigated the use of energy attenuation. The methodology follows TOA, location replacing time difference with energy difference and wave velocity with anattenuation coefficient. However, as this method is based on the same principals as TOA source
location, similar error sources apply.
A further method based on computerised tomography (CT) that was developed for medical
applications has been presented by Schubert [6] and utilised algebraic reconstruction techniques(ART). The method is as follows• Set-up an array of sensors around an area of interest• Divided the area into a grid, with each grid square assigned an initial wave velocity.
• Conduct a series of simulated events, either by pulsing each of the sensors or by generating events within the grids.
• Use the known wavepath and elapsed time from source to sensor to re-evaluate the wave
velocity assigned to each of the grid squares that the wavepath intersects using algorithms
such as ART.
• Repeat the previous step until the wave velocity in each grid space has suitably converged. It is possible to use the wave velocity map to accurately locate and future events.
Any geometric features, such as holes, will cause an altered wave velocity to take account of
the change in wavepath. However, in a further paper [7], results using this technique from an aluminium plate with a saw cut were presented. The array used was unable to detect the cut until itintersects at least one of the wavepaths between sensors, a disadvantage of this method.
In this paper, a novel method of source location where an AE location array is mapped withan Hsu-Nielsen (H-N) source at known positions is presented. A more detailed description of the
Delta-T technique can be found in [8]; however, a summary of the technique is provided below
in five steps:• Determine area of interest. Though Delta-T source location can provide complete coverage of a part or structure, it is best employed as a tool to improve source location around
specific areas of expected fracture, which could be identified via finite element modelling.
• Construct map system. A grid is placed on the component over the area of interest andwithin which AE events will be located; the higher the resolution of the grid the greater
the accuracy. It is possible to increase the resolution of the grid around features of interest, however it should not be smaller than one wavelength, as this is the minimum loca-

216

•

•

•

tion resolution. It should be noted that sources are located with reference to the grid andnot the sensors.
Apply artificial source event to obtain time-of arrival data. Artificial sources [9,10] conducted at the nodes in the grid provide AE data for each sensor. Several sources at eachnode are required to provide an average result and to eliminate erroneous data. It is notessential to have AE data from every node in the grid because missing data points can beinterpolated from surrounding nodes.
Calculate Delta-T Map. From each artificial source, a difference in time of arrival or
Delta-T is calculated for each sensor pair (an array of four sensors has six sensor pairs).
The average Delta T for each sensor pair at each node is stored in a map. These maps canbe displayed as contour plots of equal Delta-T.
Compare Actual Data. To locate an actual AE event, the Delta-T for each pair is calculated. A line or contour on each map corresponding to the calculated Delta-T can be identified. By overlaying results from each of the sensor pairs, a convergence point can be
identified; the source location. As with time of arrival, a minimum of three sensors is required to provide a point location and more sensors will improve the location. In theory
all lines will intersect at one location, however, in reality this is not the case. Therefore toestimate the source location, all of the convergence points can be calculated and a cluster
analysis can be conducted on the points to determine the final location.

This paper presents three case studies demonstrating the developed novel source detection
method.
Experimental Procedure
An artificial source investigation was completed on a 1-m2 steel plate with a 250-mm ‘roughcut’ hole in the centre. Eight sensors were mounted on the plate, four in a regular square arrayand four in a random irregular array (Fig. 1). These two arrays allow comparison between a regular and irregular sensor groups. A 400-mm2 grid with a 50-mm grid density centred on the cutout was used (Fig. 1). Ten artificial sources were conducted at each node within the grid. Ten test
locations were randomly selected within the grid and five artificial sources conducted at eachlocation (Table 1).
Table 1: Artificial source coordinates and source location errors.
Location
1
2
3
4
5
6
7
8
9
10
Average

Source Position [mm]
X
Y
20
100
70
230
100 300
130
90
200 330260
90
300 300
320 160350
50
330 390n/an/aDelta-T
Error [mm]
All Reg Irreg
6
8
14
38
52
24
4
3
5
21
22
25
10
12
25
24
38
70
1
13
23
35
53
24
31
26
38
5
2
14
18
24
28
217

TOA
Error [mm]All
Reg Irreg
20
385
6
55
113
62
108
36
16
51
103
63
117
52
530
47
73
103
92
120
217
44
42
33
69
74
108
4
72
n/a
62
107
129

All
70
30
96
59
92
48
99
20
56
-7
71

Error
[%]
Reg
Irreg
98
-133
54
61
91
68
78
60
76
95
48
32
89
89
-26
29
65
65
97
n/a
77
78

Fig. 1. Artificial source investigation showing regular and irregular array.

Fig 2. Specimens for validation of developed source location technique (a) disruption of source
to sensor propagation path, (b) thickness change resulting in changes in propagation velocity (alldimensions in mm).
218

The specimen with numerous holes was made from 3-mm mild steel plate, but plate thicknesses were increased to 9 mm at the loading pins by the addition of two extra plates bolted toeither side of the plate, to avoid failure at the loading pins. Physical Acoustics Limited (PAL)
resonant sensors were mounted to the plates (as shown in Fig. 1 by filled circles), grease wasused as an acoustic couplant and the sensors were held in position with magnetic clamps. The
sensitivity of the mounted sensors was evaluated using the H-N source technique.
A 180 x 130-mm grid with a grid density of 10 mm and a 140 x 160-mm grid with a griddensity of 20 mm were selected for the hole and step specimens, respectively. Five H-N sourceswere conducted at each available node to form the Delta-T grids. The specimens were fatigued
under a load of 3.5 to 35 kN until failure. All AE feature data was recorded using a PAL MISTRAS system.

Fig. 3. Example of Delta-T source location maps for both specimens.
219

Results and Discussion
Response of all sensors to the H-N source in all investigations was above 98 dB, demonstrating that all sensors were mounted correctly. A wave velocity for the first threshold crossing wasdetermined and used for all TOA source location calculations. The data from the H-N source
events was used to create the Delta-T contour maps, as discussed previously. It is possible to create similar, theoretical maps for TOA using the above first threshold crossing wavespeed. Figure
3 displays a comparison between the Delta-T and TOA source location maps, for one sensor pair,
for the three specimens. By examining the Delta-T map it is evident that the wavepaths are interrupted by the holes in the plates, which will cause errors in any source location calculation.
Figures 4 to 6 and Table 1 display the results from the artificial source investigation. Figure 4
shows the results from using all eight sensors. This array produced the most accurate results forboth the TOA and Delta-T location methods. This is expected as the use of eight sensors produces 28 sensor pairs and increases the likelihood of direct wavepaths between source and sensor, thus reducing the effect of the 250-mm cut-out. Delta-T had an average error of 18 mm (38mm maximum, 1 mm minimum) compared with 62 mm using TOA (117 mm maximum, 5 mm
minimum), a 71 % reduction.
Figure 5 and 6 and Table 1 presents the results from the regular and irregular arrays respectively. Both location methods show an increase in error associated with the reduction in numberof sensors. There is little difference between the regular and irregular arrays when locating with
Delta-T. A difference in the two arrays is apparent in the TOA location methods, with an average
error 107 mm for the regular array and 129 mm for the irregular array. It was not possible to locate source 10 (Table 1) using TOA for the irregular sensor array, however the Delta-T method
located this source to within 14 mm. The Delta-T technique improved the location in both thesearrays by 71-78%. The results show that it was more accurate (> 54 % reduction in error) to use afour sensor array with Delta-T location than to use TOA location with an eight sensor array whenlocating artificial sources in this plate.

Fig. 4. Comparison of location methods of artificial sources using all 8 sensors.
220

Fig. 5. Comparison of location methods of artificial sources using regular array.

Fig. 6. Comparison of location methods of artificial sources using irregular array.
Figure 7 shows a comparison of the TOA with the Delta-T location techniques of the detected signals for both fatigue specimens. The specimen geometry and the site of fracture initiation have been superimposed. The plots demonstrate how the Delta-T technique shows significant improvement. In the hole specimen, the TOA results (Fig. 7a) show four peaks of activity ofabove 70 events, whist the Delta-T results (Fig. 7b) have only one such peak. Furthermore, thelocation of the signals is more compact, demonstrating that a source cluster analysis of the source
221would be achieved earlier using the Delta-T technique. The position of the peak cluster is alsomuch closer to the position of fracture. Figure 7c shows the TOA plot for the change in thickness
plates, and it can be seen that there is no single peak in close proximity to the fracture region. In
Fig. 7d, the Delta-T technique clearly identifies the fracture position, although it is not the peakof events.
Based on the location of the initiation of fracture and the closest peak cluster, the TOA and
Delta-T techniques show errors of 15.5 and 8.0 mm, respectively, for the plate with holes, demonstrating a reduction in error of 48%. It is not possible to determine an error for the step plate as
there is no cluster in reasonable proximity that could be defined as coming from the fracture.

(a)

(b)

(c)

(d)

Fig. 7. Comparison of source location results from specimen with disruption of source to sensor
propagation path (a) Hole: Time of arrival, (b) Hole: Delta-T technique, (c) Step: Time of arrival,(d) Step: Delta-T technique.
A further advantage of the Delta-T technique is that it has a tighter source clusters, as this canbe seen when comparing the cluster sizes in Fig. 7a and b. Using the TOA technique (Fig. 7a)there are three clusters of >70 events whilst the Delta-T technique (Fig. 7b) has only one clusterof >140 events. This suggests that if a spatial cluster were to be used as an indication of developing damage, the Delta-T method would identify it earlier.

222

Conclusions
Delta-T source location provides a novel approach for overcoming particular problems associated with source location in complex structures with some current techniques (TOA and modal
analysis). All investigations have demonstrated that the Delta-T mapping technique is superior tothe current TOA technique.
Acknowledgements
The authors wish to acknowledge the funding of this work by Messier-Dowty the leading
manufacturer of landing gears and landing gear components and EPSRC. In addition the technical support of Physical Acoustics Limited is acknowledged.
References[1] Miller, R. K. and McIntire, P. Acoustic Emission Testing NDT Handbook, Volume 5, American Society for Non-destructive Testing: p. 652, (1987).
[2] Rindorf, H. J. “Acoustic Emission Source Location in Theory and in Practice”, Bruel andKjaer Technical Review. 2, 3-44 (1981).
[3] Pullin, R., Holford, K. M. and Baxter, M. G. “Modal Analysis of Acoustic Emission Signals
from Artificial and Fatigue Crack Sources in Aerospace Grade Steel”, Key Engineering Materials, 293-294, 217-224 (2005).
[4] Holford, K. M. and Carter, D. C. “Acoustic Emission Source Location” Key Engineering Materials, 167-168, 162-171 (1999).
[5] Nivesangsan, P., Steel, J. A. and Reuben, R. L. “AE Mapping of Engines for SpatiallyLocated Time Series”, Mechanical Systems and Signal Processing, 19(5), 1034-1054 (2005).
[6] Schubert, F. (2004) “Basic Principles of Acoustic Emission Tomography” 26th European
Conference on Acoustic Emission Testing, Berlin, Germany, pp. 693-708.
[7] Schubert, F. (2006). “Tomography Techniques for Acoustic Emission Monitoring” European
Conference on Non-destructive Testing, Berlin Germany.
[8] Baxter, M. G., Pullin, R., Holford, K. M. and Evans, S. L., “Delta T Source Location for
Acoustic Emission” Mechanical Systems and Signal Processing, 21(3), 1512-1520 (2007).
[9] ASTM, Standard guide for determining the reproducibility of acoustic emission sensor response, American Society for Testing and Materials, E 976 (1994).
[10] N.N. Hsu and F.R. Breckenridge, Characterization and Calibration of Acoustic Emission
Sensors, Materials Evaluation, 39, 60-68 (1979).

223

ACOUSTIC EMISSION SOURCE LOCATION ON AN ARBITRARYSURFACE BY GEODESIC CURVE EVOLUTION
G. PRASANNA, M. R. BHAT and C. R. L. MURTHY
Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560 012, India

Abstract
Location of an acoustic emission (AE) source is generally obtained by assuming simplified
velocity models on surfaces and solids, for which a definite parametric representation exists.
These attempts employ triangulation to form distance equations using time-differences obtained
from experimental data, which are solved analytically or numerically to get to the location ofsource. This approach may not be suitable for complex geometry components. Also, the problem
gets compounded if the material of the structure is anisotropic warranting complex analytical velocity models. Hence, there has been a need to obtain a practicable source location solution in amore general setup on any arbitrary surface containing finite discontinuities. The approach suggested here is based on the fact that the wave takes minimum energy path to travel from a source
to any other point in the connected domain. An AE signal takes this path from the source to an
AE sensor array. By propagating the waves in reverse virtually from these sensors along theminimum energy path and by locating the first intersection point of these waves, one can get thesource location.
Keywords: Source location, geodesics, geodesic evolution
Introduction
One of the major advantages of AE technique as an on-line monitoring tool is its capability tolocate active defects in larger structural components without having to physically scan them. Different methods used for source location include zonal location, computed location, and continuous location. These approaches are affected by the signal attenuation and dispersion due to inhomogeneity and geometry of the material [1]. An alternative location technique uses the concept of ‘the first sensor hit by an AE event’ to identify a more generalized region around eachsensor, from which the AE signal likely originated. In this case, one can determine which one of
the several sensor regions on the test specimen has more concentrated AE activity [2]. Some AE
systems determine signal arrival times using fixed threshold techniques [3] and because of theaforementioned complications, such AE systems measure arrival times for signals using various
portions of the AE signal, which travel at different velocities.
The approach suggested here is based on the fact that the wave takes minimum energy path
to travel from a source to any other point in the connected domain. In isotropic media, minimum
energy path gets reduced to shortest distance path, which can be seen mathematically as shortest
geodesics. Hence, by allowing geodesic waves to propagate from multiple sensors and identifying their point of meet, one can obtain the source location. In an object with complex geometry, agraph-theory based concept can be employed to determine the shortest path using Dijkstra’s algorithm [4] for finding discrete geodesics, which are propagated from each sensor location till
the source is reached.

J. Acoustic Emission, 25 (2007)

334© 2007 Acoustic Emission GroupOur approachThis conceptual view can be visualized as in Fig. 1, which shows waves propagating in all
possible directions from a defect location, along the minimum energy path. With imposed material limitations, this path is generated by geodesics. Once the geodesic paths are extracted in agiven geometry, the defect location is reached by back-propagating along those paths, from thesensor locations, as shown in Fig. 2. To start with, it can be assumed that AE sensors detect only
Rayleigh waves, which in turn means that the study is restricted to 2-D surfaces.

Fig. 2: Conceptual view – back-propagation
to defect location.

Fig. 1: Wave front propagation – from defectlocation.

It is to be noted that even the triangulation method utilizes the same approach, only that it has
an inherent assumption that the geometry is a 3-D continuum resulting in simple distance equation based on Pythagorean theorem, which is solved analytically or numerically.
Since our approach builds on the fact that a wave takes minimum energy path, when the assumption of isotropy in the media is imposed, the shortest energy path reduces to shortest distance path, which is provided by the evaluating the geodesics. Mathematically, the energy along
a path is seen as weighing function defined along the path. Hence, the minimum energy path isgiven byEnergy along path,L =  w(x)dx ,Min. Energy,min(L) = min(  w(x) dx )If isotropic, Min Energy path, min(L) = w. min(  dx )where, min(  dx ) is the required geodesic.

So, the minimum-energy-path problem is equivalent to shortest-distance-path problem. The intersection of geodesic wave fronts from multiple sources gives the location of the source.
Formulation - Wave Propagation Approach:This approach involves discretizing the domain as curved or planar simplical-complex
chains, followed by finding local geodesics in each of the simplex and finally gluing them together to get the required global geodesic. There are many suggested methods to calculate225

discrete geodesics and it is still an active area of research investigation [5-7]. Dijkstra [4] proposed an algorithm for the same and most of the present techniques is built over it.
The source location formulation proceeds as follows:
D( S1  S )

D( S 2  S )= V .dt

which is recast into the implicit form, (D , V , dt ) =0

The geodesics ‘D’ in above formulation is arrived from ‘wave-propagation’ perspective usinggraph-theory based Dijkstra algorithm.
The governing equation is subsequently further recast asD( S1  S ) ± V .dt = D( S 2  S )This leads to the view that the solution lies in the boundary of the Voronoi diagram and the exact
location is the intersection of two or more boundaries.
With above observation and understanding, the algorithm was implemented and tested forshortest path extraction over some simple cases and also over Stanford Bunny (Fig. 3), proving
convincingly the wave-propagation based construction.

Fig. 3: (a) Surface model.

(b) Generated by geodesic propagation.

The bunny in Fig. 3(b) was generated by flow of wave along the shortest cost path from an arbitrary vertex in the mesh. The thick lines in the figure are samples of shortest paths between two
points on the bunny surface (which are the required ‘geodesics’).
This proves the ability to extract discrete geodesics on arbitrary surface, which is the firstpart of geodesic evolution approach. The second part of solution is the construction of Voronoilike diagram to locate the intersection of wavefronts, which is discussed in the following section.

226

Voronoi Construction:
Taking a case of 3-sensor setup provides 3 sets of time-difference equations. Using the recast formulation, all the points that are equidistant from sensor locations are found (using distance map calculated based on Dijkstra algorithm along with V.dt corrections).
When information from only two sensors is available, then only one equation is formulated
and hence there exist multiple solutions meeting the distance criteria. This is depicted in Fig. 4(a)with the jagged thick line passing between sensors S1 and S2 indicating all points that are equidistant (which is the Voronoi diagram). An important observation is that this line passes throughthe defect location (AE source) and hence we need to search only along this line for getting to
the source.
When information from one more sensor (S3) is considered, then 3 equations are formulated,from which the other lines in Voronoi diagram are constructed. All these lines intersect exactly at
the AE source, as in Fig. 4(b). The geodesic lines joining the 3 sensors give the Delaunay triangle, which is the dual of conventional Voronoi diagram.

Fig. 4: Voronoi diagram considering (a) 2 sensors; (b) 3 sensors.
Above construction is trivial to implement in form of set operations. For a given mesh,• Let nk be the kth node in the mesh and• D(nkSi) be the distance between kth node and ith sensor and• Dk(Sij) be the difference in distances of a node k from sensors Si and Sj, i.e.,Dk(Sij) = D(nkSi) – D(nkSj), then•the Voronoi line between any two sensors Si and Sj is formed by nodes which satisfy thecondition that Dk(Sij) = Vdtij
where, dtij is the hit arrival-time difference between the sensor Si and Sj and D’s are geodesic
distances and the corresponding line can be seen as set of these nodes which is given by
Lij = { nk | Dk(Sij) = V.dtij }•Hence for 3 sensors we get, L12, L13 & L23 as shown in Fig. 4. The intersection point is
the intersection node in the set Lij given by227

Source, S = { n | (L12  L23  L13 )•For surfaces, which are intrinsically 2-dimensional in parametric space, only two of theabove sets are to be included for getting the source node.

Experimental Evaluation
The trial is initially made for a curved planar structure. Source location was attempted using
both numerical-continuous and numerical-discrete (wave-propagation approach). It was followedby experimentation on an odd geometry component containing sharp changes and discontinuities. The AE setup that is used (MISTRAS) has an auto-sensor-test (AST) mode where each sensor
acts as source and emits an AE pulse, which is received by other sensors. With known sensor
locations, time-difference equations are formed and solved to get the velocity of the AE in thetest component. AE sources were also simulated by pencil-lead breaks at known locations andthe AE data was continuously recorded. The extracted data is sent to source location algorithm(coded in MATLAB) for evaluation.
Cylindrical Geometry:
The setup is shown in Fig. 5(a). Based on the dimensions, a sector of the object was meshedusing ANSYS (Fig. 5b) and mesh information was imported to MATLAB. Wave propagation algorithm was applied over the mesh to find the location of artificially created sources. The program
output shows a deviation of approximately 7.5% from the actual, as shown in Table I.

Fig 5 (a) Cylinder - AE setup (b) ANSYS mesh of a sector.
Table I: Source location in cylindrical geometry.
SensorLocation
S-1
S-2
S-3

Measured Value
X
Y
Z
105
0
0
-80.430-67.49-80.43360-67.49228

Program output
X
Y
Z
105
0
0
-80.43
27.69-67.49-80.43332.31-67.49

Fig. 6: Source location on a clamp with holes and sharp discontinuities.
Figure 6 shows the geometry as 3D model, the test setup, the ANSYS mesh and the MATLAB
output (with a thick line showing the path from sensor location to the source). See Table II for
the results. Error in this case is less than 10%.
Table II: Source location in clamp.
Location
L-1
L-2
L-3
L-4
L-5

Measured Value
X
Y
Z
31
25
39
45
8
39
73
33
31.5
114
30
28
55
0
30

Program output
X
Y
Z
24.5
24.5
39.0
50
14.8
39.0
66
34
30.5
106
37
30.5
50
0
30

% Error(Euclidean)
6.4
8.4
6.8
4.0
6.9

Conclusion
The approach presented in the study used a property of wave by which it tends to take minimum-energy path to travel between points on domain. This minimum-energy path was proved tobe equivalent to shortest-distance path marking the birth of geodesics. The relation of geodesics
to source location problem was established by proving that location of source is the first intersection point of multiple geodesics. This was implemented and proved by using Voronoi like diagram construction. The approach was experimentally validated on curved planar and odd geometry component. The solution based on two methods – conventional Numerical method and229

Geodesic Evolution method was presented. It can be asserted that the geodesic curve-evolution
method hold great promise for versatile implementation catering to non-conventional geometries.
By the very nature of approach, extension of it to inhomogeneous and anisotropic geometry appears feasible.
References
1. M. Ohtsu. AE Theory for Moment Tensor Analysis, Res. Nondestrutive Evaluation, vol. 6:
1995. pp. 169-184.
2. M.A. Hamstad and K.S. Downs, On Characterization and Location of AE Sources in RealSize Composite Structures - A Waveform Study, J of Acoustic Emission, 13 (1-2), 1995, 31-41.
3. Bernard Castagnede et al., Location of Pointlike AE Sources in Anisotropic Plates, J. Acoust.
Soc. Am., 86 (3), 1989, 1161-1171.
4. E. Dijkstra, A note on two problems in connection with graphs. Numerical Math. 1, 1959.
269-271.
5. K. Polthier et al., Straightest Geodesics on Polyhedral Surfaces, International Conference on
Computer Graphics and Interactive Techniques, ACM-SIGGRAPH courses, pp. 30-38, 2006.
6. G.V.V. RaviKumar et al., Geodesic Curve Computations on Surfaces. Computer Aided
Graphics Design, Elsevier, 2003, pp. 119-133.
7. Vitaly Surazhsky et al., Fast Exact and Approximate Geodesics on Meshes, Proceedings ofInternational Conference on Computer Graphics and Interactive Techniques, ACM SIGGRAPH
2005 Papers, pp. 553-560, 2005.

230

PROBABILITY OF DETECTION FOR ACOUSTIC EMISSIONADRIAN POLLOCK
Physical Acoustics Corporation, 195 Clarksville Road, Princeton Junction, NJ 08550

Abstract
Probability of detection (POD) is a discipline within the field of nondestructive testing, thatbears on the reliability of the inspection and has been of great value for selecting and validating
NDT procedures, qualifying NDT personnel, and even establishing the acceptability of structuraldesigns. This paper shows a way to bring the POD concept to AE technology. It reports the development of a model, which quantifies each step in the classic AE process – stress stimulus,
flaw growth, wave release, wave propagation, sensing and detection. In its first implementation,
the model treats fatigue crack growth in pressurized cylinders. A small percentage of the fracture surface is assumed to be formed by cleavage, the source of the AE. With the help of theParis law for fatigue crack growth, the source theory of Wadley and Scruby, the power-law amplitude distribution and other relevant theory, it is possible to determine the probability of detecting AE from a flaw of given initial size, in a given vessel subjected to a given pressurization
schedule. Because of attenuation the POD depends on the source position, so the findings arepresented with POD encoded as color on a map of the vessel. Use of this POD concept will open
the way to engineered AE inspections, better quality AE tests, and better integration of AE withother NDT methods to meet industrial needs for structural integrity.
Keywords: Probability of detection
Introduction: Probability of Detection (POD) in the NDT Industry"Probability of detection" (POD) is a quantitative discipline with the field on nondestructive
testing, addressing the topic of inspection reliability. In the late 1960's and early 1970's, it wascommon knowledge in the NDT industry that many inspections were producing incorrect results
and that some inspection teams were more reliable than others. POD methodology was developed to address these facts and to furnish tools for securing adequate reliability of NDT on highvisibility, high-cost structures, such as the space shuttle [1]. Continuing to develop during thelast 30 years, the POD discipline has delivered value in many activities especially selecting andvalidating NDT procedures, qualifying NDT personnel, and even establishing the acceptabilityof structural designs.
POD is the probability of actually detecting a given flaw that is assumed to be present in agiven nondestructive inspection. POD is of obvious significance to structural integrity andsafety. POD is a statistical quantity. The probability of detecting a given flaw depends on manyfactors including flaw characteristics (e.g. type, size, shape and orientation), test method andprocedure, and personnel proficiency. In the early years of POD development, the approach wasto collect together a large number of test objects with flaws and a substantial number of inspectors (sometimes teams of inspectors); to have the inspectors perform NDT; and to analyze theresults in terms of how many flaws were detected. The outcome of this kind of analysis is conventionally presented as a graph showing POD as a function of flaw size. Work of this kind has
provided valuable support to many programs, especially in the aerospace industry where thePOD concept has been a good partner to the concepts of damage tolerance that were being
J. Acoustic Emission, 25 (2007)231© 2007 Acoustic Emission Groupdeveloped at the same time. The main setback to the empirical approach to POD (multiple test
specimens, multiple inspection teams) is that it is laborious and costly. As the use of POD increased, techniques for modeling the NDT processes were brought in to supplement the empirical approach. By using models to handle some of the variables, the numbers of test specimens
and inspectors needed to estimate POD could be reduced, resulting in a less costly and more efficient analysis.
Model Developed for Estimating POD for AE
A model for the probability of detecting flaws by means of AE was developed in the context
of a project for monitoring a set of high-pressure gas cylinders during a planned 30-year service
life. AE was under consideration both for the pre-service hydrotest and for in-service monitoringof these vessels. Availability of POD numbers for AE would support the project through the design approval process and lead to a more economical design. The model was developed first for
the in-service monitoring scenario. The POD model is essentially a quantification of the classic
path from AE source to processed data. All the variables along the path are quantified – stressstimulus, source behavior, wave propagation, sensor sensitivity, instrumentation gain and detection threshold. Figure 1 is a block diagram of the model, in which the variables are arranged in a
way that facilitates the desired calculation.

Fig. 1 Method for determining the probability of detecting a flaw with AE.
232

POD is determined at the convergence of two lines of inquiry, related to AE generation and
AE propagation/detection respectively. The operative questions are:1. "How many cleavage-crack events will take place at the specified flaw under the loading
program under consideration, and what is their size distribution?"
2. "How big is the smallest cleavage-crack event that will be detected by the AE equipment,at the distance under consideration?"
Given the answers to these two lines of inquiry, it is not hard to find the probability of detecting
the flaw during execution of the specified loading program, using the specified equipment setup.
Because of attenuation, the probability of detection is naturally a function of source position. It is
assumed that the AE is produced by "cleavage-crack events" that form a definite percentage ofthe fracture surface area. This assumption is a key feature of this particular case; i.e., fatigue
crack growth in these metal cylinders. However, it is not essential to this POD methodology in
general. Other cases could be based on different assumptions about AE source mechanisms.
Indeed, this is a major area for the future growth of this POD methodology. To show how theprocess works, the less obvious steps of Fig. 1 will be discussed in turn.
1. Determine crack growth associated with a given flaw and loading program.
The loading program is a key part of any AE test [2]. For the case of fatigue crack growth in
the high-pressure gas cylinder, the assumption was made that a flaw was growing according to
the Paris law. A computer program and graphic user interface were developed to calculate theamount of flaw growth with user-entered initial flaw sizes and loading programs. The graphic
user interface is shown in Fig. 2. The upper part shows the vessel dimensions, the pressures applied and the cycling process to be monitored. From the dimensions and pressures, the hoop andaxial stresses and stress intensity factors are calculated by conventional techniques. Then, theprogram calculates the flaw growth cycle by cycle, finally arriving at the total new crack area
opened up during the entire specified loading program.
2. Characterize the cleavage increments (AE events) that will make up this crack growth.
The model used here for fatigue crack growth is a synthesis of insights from several sources.
The pioneering work of Pelloux [3] showed that in Stage II fatigue, the new surface is createdmainly by slip-like dislocation movements; but, occasionally, this process is punctuated byabrupt transgranular cleavage events that can even be seen on fractographs. The studies of Wadley and Scruby on emission amplitudes from various source mechanisms, summarized in [4], indicate that such cleavage events would give useful AE signals while the dislocation movement
would not. The early observation of Harris and Dunegan [5], that peak load emission does notoccur on every cycle until the crack is growing relatively fast towards the end of its life, also fits
our story that just a certain fraction or percentage of the fracture surface area is formed by cleavage, and that this is the source of the AE. This fraction or percentage is an input parameter to the
POD model.
This gives us a total cleavage area resulting from the loading program under consideration.
The next step is to break up this total area into discrete increments – AE events. This is donewith the help of the amplitude distribution power law [6], whose exponent b is an input parameter to the POD model. It is assumed that the smallest event is single-grain cleavage, while large
events correspond to cleavage extending over multiple grains simultaneously. Using the powerlaw, the number of AE events F(A) is calculated as a function of A, the cleavage area of the individual event. This completes the first line of inquiry, Steps 1 and 2 in Fig. 1.

233

It is generally agreed that different flaw types have different AE characteristics. Therefore,
the above argument is only a specific, limited prototype of this generic step in the POD model.
Fresh thought will be needed when the scope of the methodology is extended to cover flaw typesother than cracks, and/or loading regimes other than Stage II fatigue.
3. Specify instrumentation characteristics, sensor layout, attenuation.
The specified instrumentation characteristics include the sensitivity of sensor and preamplifier, the preamplifier gain and the detection threshold. These obviously influence whether awave of given strength will be detected by the instrument. Another instrumentation characteristic that comes into the model is a characteristic frequency of the sensor. This is used to convert
the stated sensor sensitivity from V/(m/s) to V/m. This approach does not consider the full complexity of sensor spectral responses or directional effects, but it does allow a calculation of thesmallest displacement at the sensor that will be detected at the characteristic frequency.
4. Source theory and wave attenuation.
The focus of this step is to determine the smallest cleavage increment detectable by the sensor. First, this requires some source theory, which is drawn from reference [2]. This source theory relates the cleavage event area to the resulting AE motion (displacement) as a function of
distance in an infinite medium. We apply this source theory at a reference distance of one plate
thickness from the source. Then, the measured attenuation from that distance outwards is used toinfer the smallest detectable cleavage event area at any arbitrary distance.
5. Apply Poisson statistics to calculate POD for a given source-sensor distance.
This is an interesting step in the model. Here the stochastic nature of the AE phenomenon
leads naturally to a stochastic (probabilistic) outcome for the detection process. Here also,
evaluation criteria are considered. For now, detecting a single AE event is considered equivalent
to detecting the flaw. Other evaluation criteria can be found in industrial practice. In tube trailer
testing, for example, the evaluation criterion is framed in terms of a cluster of several events, located close together. The POD concept described here can be readily extended to such cases.
To conclude the present analysis: we have a probability distribution of cleavage event areas
and we know the smallest cleavage event area that will be detected. The expected number of detectable events  is immediately found. Then, this expected number  can be used as the "rate"
parameter of the Poisson distribution. This distribution (a function of ) tells about the probability of actually detecting 0, 1, 2, ... events in a given test when the expectation number is . The
probability of detecting no (zero) AE events is e-. So the probability of detecting at least one
AE event, is (1 – e-). This is the probability of detecting the flaw; a good simple result to closeout the POD model calculations.
Illustrative Results
Figure 2 shows input data used to generate the POD results in a typical run of the POD software. The structure being modeled is a cylindrical pressure vessel being subjected to 2000 pressure cycles. The flaw being modeled is a crack, initially 1.0-mm deep. AE is monitored using a
50-dB threshold setting. There are many adjustable parameters but to illustrate the functioningof the model, just these three will be varied while the others are held constant.
Figure 3 is map of calculated POD for the test setup detailed in Fig. 2. Six sensors are placedon the vessel, two rings of three sensors each. The POD values are encoded in color as shown in234

Fig. 2: Setup for illustrative runs of the POD-POL software.

Fig. 3: 1-mm flaw, 2000 cycles, 50-dB
threshold.

Fig. 4: 1-mm flaw, 1000 cycles, 50-dB
threshold.

the key beside the map. The color is mostly green, indicating high POD (80-100%) for flaws
almost anywhere on the vessel surface. Midway between the sensor rows, the POD is lower (6080%). The model also displays some statistics of the flaw growth - the number of AE events occurring at the crack, the total area of cleavage, and the number of grains cleaved – but these cannot be shown here for the lack of space.
Figure 4 shows the result of only monitoring 1000 cycles instead of 2000, with the same initial flaw size. The POD drops to 40-60% over most of the vessel surface. The POD is only highif the flaw is close to a sensor. This kind of result can be useful in engineering an AE test, because it shows how the reliability of the test will depend on the duration of the monitoring.

235

Fig. 5: 0.4-mm flaw, 2000 cycles, 50-dB
threshold.

Fig. 6: 0.4-mm flaw, 2000 cycles, 40-dB
threshold.

Figure 5 reverts to 2000 cycles of loading, but the initial flaw is only 0.4-mm deep instead of
1.0-mm. Now, the POD is only 30-55%, over most of the surface area. The flaw has to be very
close to a sensor if it is to be detected with high reliability. This kind of result is useful because
it shows how the AE test results will depend on the initial flaw size.
In Fig. 6, the detection threshold is lowered to 40 dB. This renders the 0.4-mm flaw detectable with much higher probability, 70-80% and better, over most of the vessel surface. In a real
test, of course, lowering the threshold would also increase the likelihood of picking up noise
sources.
Finally, Fig. 7 shows a logical extension of the POD concept: Probability of location (POL).
This is readily incorporated in the model. Figure 6 shows how the POL is greatest when thesource is in the open space surrounded by sensors. POD, in contrast, is greatest when the sourceis close to the sensors. POD and POL can both be considered when planning the strategy for an
AE test or test series.
Discussion and Conclusions
These results illustrate how POD depends on the many variable parameters involved in the
AE test. With this model, the effects of changing sensor layouts, sensitivities and test parametersin general can be assessed quickly, easily and quantitatively. AE test planning often involves
tradeoffs around cost, technical difficulty and so forth. With this model, quantitative expectations about test results can now be included in this planning process. This quantitative planningcapability will also make AE more viable in the eyes of reliability engineers, who are considering its use in their structural integrity assurance programs.
References
1. W.D. Rummel, Probability of Detection as a Quantitative Measure of Nondestructive Testing
End-to-End Process Capabilities, Materials Evaluation, 56, 1, 1998, 29-35.
2. A.A. Pollock, Loading and Stress in Acoustic Emission Testing, Materials Evaluation, 62, 3,
2004, 326-333.
236

Fig. 7: Probability of location (POL).
3. R.M.D. Pelloux, The Analysis of Fracture Surfaces by Electron Microscopy, Boeing Scientific
Research Laboratories, Report Z254824 (available from Storming Reports), 1963.
4. R.K. Miller and P. McIntire (eds.), Acoustic Emission Testing, Nondestructive Testing Handbook, 2nd ed., Vol. 5, American Society for Nondestructive Testing, 1987, pp. 77-78.
5. D.O. Harris and H.L. Dunegan, Continuous Monitoring of Fatigue Crack Growth by Acoustic
Emission Techniques, Experimental Mechanics, EXMCA, 14, 2, 1974, 71-81.
6. A.A. Pollock, Acoustic Emission Amplitude Distributions, International Advances in Nondestructive Testing (ed.: Warren J. McGonnagle), Vol. 7, 1981, pp. 215-240.

237

PLASTIC-REGION TIGHTENING OF BOLTS CONTROLLED BY
ACOUSTIC EMISSION METHODYOSHIHIRO MIZUTANI, TADASHI ONISHI and MASAMI MAYUZUMI
Dept. of Mech. Sci. & Eng., Tokyo Institute of Tech., Meguro, Tokyo 152-8552, Japan
Abstract
We have proposed a control method for plastic-region tightening of bolts using AE method.
In this paper, in order to investigate the validity of the tightening control by AE method, thetightening test in simulated factory environment was conducted and both tightening accuracy
and variations were compared with those by conventional control methods. The results showthat AE method is more effective for plastic-region bolt tightening control than the conventional methods. Finally, a new system using a Maharanobis distance for controlling plastic-region bolt tightening by AE method was developed.
Keywords: Plastic-region bolt tightening, tightening control, Maharanobis distanceIntroduction
A plastic-region tightening method used in the automotive industries is known as a good
method for preventing fatigue of bolts. However, the most common tightening-control
method by using a torque wrench (called torque-control bolt tightening method) cannot apply toa plastic-region tightening [1]. Both an angle control method and a torque-gradient control
method are regulated in JIS (Japanese Industrial Standards) [2] for a plastic-region tightening
control, although the method requires a special torque wrench and complicated procedures.
We have proposed a new control scheme using AE method, but the accuracy and validity were
not examined. In this paper, in order to investigate the accuracy and validity of the tightening-control in field, the tightening test was conducted. The tightening-accuracy and variation by
AE method were found superior to those controlled by conventional methods.
Experimental Setup and Method
When a tightening is performed in field, noise due to friction on bearing surfaces wasmonitored with AE. In order to confirm whether AE method can be utilized in an industrial
condition, tightening test in simulated factory environment was performed. Moreover, accuracy and variation of the tightening control by AE method were compared to those by conventional methods (angle control method and torque-gradient control method). Figure 1 shows
photograph and schematic illustration of experimental setup used for the test. The axial loadof a bolt was measured by a load cell (TEAC, TU-NR-C50kN), which was placed between twosteel plates (SKS93, thickness: 10 mm). We prepared two sets of steel plates, of which surface roughness (arithmetical mean surface roughness) were less than 3.1 µm (plate 1) and 25µm (plate 2).
A torque was measured with a digital torque wrench (Nakamura,
DTC-N1000EX). A potentiometer (Nidec Copal Electronics Corp., M-22E10-050 1k) was used
for measuring the rotation angle. A socket of a torque wrench was equipped with a timing pulley, rotation angle of a bolt was transmitted to the potentiometer with the timing belt, and therotation angle was measured. An AE sensor (PAC, Type: PICO) was attached to a bolt head.
J. Acoustic Emission, 25 (2007)238© 2007 Acoustic Emission GroupAE signals were filtered by high-pass filter of 100 kHz and amplified 66 dB by AE analyzer(NF Electronic instruments, Type: 9501). By using this experimental setup, a torque, an axialload, a rotation angle and AE can be measured simultaneously during bolt tightening.

Fig. 1 Photograph and schematic illustration of tightening test.
Conventional Control Methods
We first investigated tightening accuracy and variation of conventional methods. One ofthe plastic-region tightening control methods regulated in JIS B 1083 is an angle control
method. According to the JIS, desired angle (θfA) for a plastic-region tightening is determinedby a following equation.
1! fy " ! fA " (! fy + ! fu )(1)
2Here, θfy is rotation angle corresponding to the yield point of bolts, and θfu is that of the ultimate
point. Angles of θfy and θfu are determined by load-rotation angle diagram as shown in Fig. 2.
Desired angle-range for plastic-region tightening (Eq. (1)) and axial-load-range controlled by
the method are shown in the figure as Δθf and ΔFf, respectively.
Next, we conducted several experiments and the accuracy and variation of tightening control were examined. Since the axial loads of yield point and ultimate point varied with torqueapplied to bolts and the applied torque is changed by tightening conditions (even if bolts withsame lot are used), it is needed to normalize axial load when the data of different experiments
are compared. The following normalized axial-load F* is introduced for this purpose:239

F *min =F fA min ! F fy
F fu ! F fy

F *max =F fA max ! F fy
F fu ! F fy

(2)Here, Ffamin and Ffamax are axial loads correspond to minimum and maximum of desired angles
by Eq. (1). Ffu and Ffy are the yield load and ultimate load determined by θf-Ff diagram forevery experiment. Therefore, when a symbol F* becomes 0, axial load of bolt reaches theyield point, and when a symbol F* becomes 1, axial load reaches the ultimate point. Resultsof 8 experiments (1-5th experiments used plate 1, 6-8th experiments used plate 2) are shown in
Fig. 3. When a desired tightening angle is set to a minimum in the range defined by the Eq.
(1), the variation of axial loads for every examination becomes large, although, while a desired
angle is set to a maximum, the variation becomes small and axial-loads are controlled at near
the ultimate load. Axial tensions of each examination are always set in the range of 0 to 1, andit can be said that the angle control method is an accurate control method for plastic-region
tightening. The other plastic-region tightening control method regulated in JIS B 1083 is atorque gradient control method. According to the JIS, desired torque gradient (dTf/dθf) for aplastic-region tightening is determined by the following equation:
1 " dT f %3 $# d! f '&" dT f % 1 " dT f %($(d! f '& 2 $# d! f '&
#
max
max

(3)

Fig. 2 Axial tension vs. tightening angle.
Torque, torque gradient and axial tension with tightening angle monitored during 1st experiment are shown in Fig. 4. Desired range for plastic-region tightening (Eq. (3)) and axial-load range controlled by the method are also shown. In order to discuss tightening control
accuracy for each experiment and variation for every experiment, the normalized axial load ofeight experiments are investigated (see Fig. 5). Tightening variations were large compared to240

Fig. 3 Accuracy and variations of axial load.
those by angle-control method (Fig. 3). Furthermore, the result shows that there is a possibility that the axial load will not be managed in a plastic region in some cases. Therefore, thiscontrol method is inaccurate.

Fig. 4 Tightening range of torque-gradient method.
241

Fig. 5 Accuracy and variations of axial load.
Acoustic Emission Method
Variations of AE event count rates and Ib-values with tightening angle during tightening isshown in Fig. 6. Axial tension vs. tightening angle diagram is also shown.

Fig. 6 AE activities during bolt tightening.
242

Fig. 7 Accuracy and variations of axial load.
We observed a large number of AE events in the initial stage of the test. These AE signals
are noise due to friction on the bearing surfaces. In spite of having measured many noise
events, this result clearly shows that AE event count rates have a peak near the yield point andIb-values reach the peak just beyond the yield point. Therefore, AE signals due to plastic deformation can be monitored even in real tightening conditions as the indicator of yielding.
Both AE event count rates and Ib-values will be useful parameters for controlling plastic-region
tightening. Figure 7 shows normalized axial load controlled by AE method for eight experiments. Maximum position in each experiment is determined by the peak in Ib-values, and theminimum position by the AE event count rate peak. Variations for the experiments are similar
to that of angle control method (when desired angle is set to the maximum value in Eq. (1)).
The axial loads of experiments were controlled in the range of 0.2 to 0.7, and the average variation was ±0.11. This compares favorably with ±0.16 and ±0.30 for angle control andtorque-gradient control methods, respectively. This demonstrates that AE method is the mostaccurate control method for plastic-region tightening among the three examined here. Although roughness of the bearing surfaces differs between the 1st-5th experiments and the 6th-8th
experiments, AE method has controlled the axial load appropriately. The experimental results
show that AE method is more effective in plastic-region bolt tightening control than the conventional methods.
Development of Plastic-region Bolt Tightening Control System
In the previous section, we showed a feasibility of controlling plastic-region tightening by
AE method. However, it is difficult to judge the peak of AE parameters due to plastic deformation automatically by a computer system. Thus, we developed a method in lieu of the peakdetection of AE event count rates and Ib-values. The new method relies on the Maharanobis
distance and a new index for judgment is defined; i.e., the inverse of Maharanobis distance.
Here, Maharanobis distance of AE signals detected is defined as follows [3, 4]:243

((x " x ) ! ) # R # ((x " x ) ! )
T

D=

"1

p

(4)

Here, x = [x rate , x Ib ] is a two-dimensional vector consisting of the AE event count rate and the Ib
value of the comparison data. x = [x rate , x Ib ] is a two-dimensional vector, consisting of the averages of the reference AE parameters. We used the event count rate and the Ib-value thatwere detected at a yielding range of θ = 164o-180o in the 1st experiment for the angle-control
method as the reference AE parameters. p is the number of the dimension and is two for thisstudy. R-1 is an inverse matrix of a sample correlation matrix.
The inverse of Maharanobis distance during 4th and 6th experiment is shown in Fig. 8. The
axial load-tightening angle diagram is also shown. The inverse of Maharanobis distance became large when the axial load reaches the yield point. This can be used to judge the yield point
during tightening. Note that the conditions for 6th experiment tightening are not the same as
those for the reference data (plate No. 2 was used); thus the absolute value of the inverse of theMahalanobis distance is smaller than that of 4th experiment.
Based on the experimental results, the judging criteria of the plastic-region tightening wasdetermined as follows;
1) The torque become larger than 20 [N·m].
2) Inverse of Maharanobis distance become larger than 5.

Fig. 8 Inverse of Maharanobis distance during the test
(left: 4th experiment, right: 6th experiment).
We developed a control system for plastic-region tightening as shown in Fig. 9. The equipment consists of a digital torque wrench, an AE monitoring device, and an analysis system. AE
sensor was attached to the head of a bolt. AE parameters and torque were monitored and analyzed by the developed software. When correct tightening conditions are reached, the LED in
the software-screen is turn on. Figure 10 shows the axial load, torque and inverse of Maharanobis distance when tightening is conducted by the developed system. As we used plates inwhich the surface roughness was not regulated, tightening conditions differed from those of the244reference data. The absolute values of the inverse of the Mahalanobis distances were smaller
than those of Fig. 8. Although ratchet mechanism of wrench was used during tightening, appropriate judging was performed by the developed system.

Fig. 9 Developed control system for plastic-region tightening.
Conclusion
Fundamental study of plastic-region bolt-tightening control by AE method was conducted.
Results are summarized as follow:
1) Tightening test in simulated factory environment was conducted and both the accuracy
and variations of tightening were compared with those by conventional control methods.
It was found that AE method is superior to conventional methods for plastic-regionbolt-tightening control.
2) A new control system for plastic-region bolt tightening was developed. A new index
for judging the yield point was implemented in the analysis system. The utility of the developed system was demonstrated by conducting tightening tests successfully.
References
1. T. Hattori, Failure analysis and prevention of mechanical, adhesive and welded joints, paperdelivered at study group on the accident/failure analysis method of JSME (2002).
2. JIS B 1083-1990 General rules for tightening of threaded fastener.
3. Y. Tanaka and K. Wakimoto, Methods of Multivariate Statistical Analysis, 14th ed., Gendai-Sugakusha, (2004).
4. http://en.wikipedia.org/wiki/Mahalanobis_distance245

Fig. 10 Axial tension, torque and inverse of Maharanobis distance during the test.

246

REAL-TIME DENOISING OF AE SIGNALS BY SHORT TIME FOURIER
TRANSFORM AND WAVELET TRANSFORM
KAITA ITO and MANABU ENOKI
Department of Materials Engineering, The University of Tokyo,
7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan

Abstract
Laser AE method is a non-contact AE detection technique, which utilizes laser interferometer as sensor. This method is less subjected to environmental difficulties and can be adaptive for wider target than conventional PZT sensors. However, laser AE method has to improve
its sensitivity because the noise level of laser interferometer is higher than PZT sensors. In thisstudy, a method to reduce the noise component of output signal of laser interferometer is investigated. Output signals from laser interferometer were recorded continuously with 10-MHz
sampling frequency and transformed into spectrogram (time-frequency-magnitude data) by
time-frequency analysis method. After noise component in this spectrogram is cut, inverse
transform is applied to obtain clear AE signals in time domain. Whole signal processing can bedone in real time. Simulated AE detection test was conducted to confirm the effectiveness of
this noise reduction scheme. In the result, weak AE signals buried in noise could be recovered
and the improvement of sensitivity of laser AE system was demonstrated. Current real-timeprocessing can extract about 100 events/s.
Keywords: Noise reduction, continuous waveform recording, short-time Fourier transform,
wavelet transform
Introduction
Conventional PZT AE sensors are difficult to use under some of severe environments. For
example, conventional AE sensor needs waveguide or some of heat avoidance mechanism if
the testing temperature is higher than the Curie temperature of PZT. In another case, PZT sensors often cannot be attached to the target material during manufacturing process. Therefore,
laser AE method [1] has been developed to realize a direct AE measurement under such severe
environments by non-contact detection of AE with laser interferometer [2, 3]. However, laser
AE method has to improve the sensitivity because the noise component of output signals froma laser interferometer is larger than the conventional PZT sensors. Frequency filtering is one of
the most important techniques to improve the sensitivity. However, conventional analog filter
is not so effective for the output waveform of laser interferometer because the noise component
is closed to the frequency range of effective signal component. Digital signal processing (DSP)
system can do more powerful and flexible noise reduction than analog system. In this study, ahigh performance method for noise reduction of the output signal from laser interferometer wasinvestigated. The waveform from laser interferometer was continuously sampled and oncetransformed into spectrogram by time-frequency analysis methods. Effective noise reduction
process was examined using this spectrogram.

J. Acoustic Emission, 25 (2007)247© 2007 Acoustic Emission GroupAnalytical Method
Time-Frequency Analysis
Time-frequency analysis is a method to convert a waveform (time-voltage data) into a spectrogram (time-frequency-magnitude data). In this study, short time Fourier transform (STFT)
and wavelet transform (WT) were used as time-frequency analysis techniques. In STFT method,a long waveform is split into short sections and each section is processed by Fourier transformusing the following formula:+F ( , f ) =  s (t )w(t   )e  2tf dt ,

(1)



where s(t) is the sectioned waveform, w(t-) is window function, f is frequency and F(, f) is theresult of STFT. Meanwhile, in wavelet transform method, the original signal is expressed assummation of scaling and shift of certain short waveform called “mother wavelet” as+W (a, b ) =  s (t )ha*,b (t )dt ,

(2)



where h(t) is mother wavelet, a is the scaling factor, b is the shifting factor and W(a, b) is theresult of WT. Fourier transform loses the time information in the transform section because itsupposes ordinary wave. Then, if the section length becomes shorter, the time resolution of
STFT improves, but the frequency resolution degrades. Thus, the overall resolution of STFT
cannot be so high by this trade-off relationship. Meanwhile, wavelet transform can get highresolution, but the calculation amount is larger than STFT, so wavelet transform of very longcontinuous waveform is not realistic. Thus, less-intensive calculation and low-resolution STFT
is better for rough estimation of long waveform, and intensive calculation and high-resolution
wavelet transform is better for strict estimation of short waveform. Tactful use of both methods
is important.
Noise reduction
Figure 1 shows a spectrogram of output waveform of laser interferometer and contains two
main components as discrete AE signal component and continuous noise component. The noise
appeared in a broad frequency range between about 200 kHz and 500 kHz, and this range partially overlaps with the effective signal component. Furthermore, often the noise component islarger than the effective signal component in laser AE methods. In such a waveform,
conventional frequency filter which utilizes moving average or pulse reaction is ineffective. In
this study, pruning method and soft-thresholding methods are adopted as noise reduction
process. This combination of the two processes was already reported in voice processing andultrasonic testing area [4]. At first the spectrogram of recorded waveform is filtered by pruning
method:for f < f 1 , f > f 2
0,(3)
W2 ( f , t ) = 
W1 ( f , t )for f1 < f < f 2

where W1 is waveform before processing, W2 is waveform after the processing, f is frequency, t
is time, f1 and f2 is cut-off frequency. Pruning method works as frequency filter and cuts off
sharply. After that, remained white noise component was cut by soft-thresholding method:
0
W2 ( f , t ) =  W1 ( f , t ) 

for W1 ( f , t ) < 

for W1 ( f , t )  ,(4)where  is the threshold of noise and signal. Originally, these methods are used for wavelet
transform result; however, it can be used for STFT result too. After these noise reduction
248Frequency, f / kHz

700
600
500
400
300
200
100
-2

0

2
4
Time, t / ms

6

8

Fig. 1 Sample spectrogram of output signal of laser interferometer.
processes, the spectrogram was reconverted into waveform by inverse STFT or inverse WT.
Real time signal processing
The CWM system basically can do a real-time noise reduction process based on the above
theories by a combination of high-speed STFT and high-resolution WT. At first, the whole recorded waveform is converted into spectrogram by STFT. The noise component in the spectrogram is reduced by pruning method and soft-thresholding method. Then, the processed spectrogram is reverted by inverse-STFT with Hanning window function to connect smoothly at theconnection points of inverse-STFT period. After that, AE events are extracted from the noise
reduced time domain data. However, the time resolution of this waveform may not be enoughbecause this waveform is processed by STFT-based method. Therefore, a waveform with a few
milliseconds length is clipped out from as-recorded waveform around AE event and processedagain by WT-based method to get precise time information. The WT-based method is the samemanner as the STFT-based method.
In particular, the current CWM system with two 2.2 GHz processor (Athlon64 X2 Processor,
AMD Inc.) can process 2-channel AE waveforms of 10-MHz sampling in real time by
STFT-based method and can extract about 100 events/s by WT-based method.

Fig. 2 Experimental equipments of simulated AE signal detection.
249

Experimental Procedures
Simulated AE detection test was conducted to confirm the above noise reduction method.
Figure 2 shows the experimental equipment. Specimen was SUS304 disk of 30 mm in diameterand 5 mm in thickness. Simulated AE was generated by thermal stress of pulse YAG laser system (Tempest-20, New Wave Research, Inc.), measured by He-Ne laser heterodyne interferometer (AT3600S and AT0022, Graphtec Corp.) and analyzed by our CWM system [5]. The
output power of YAG laser was 40 mJ/pulse or 20 mJ/pulse and pulse length was about 4 ns.
The maximum detectable frequency of the laser interferometer was about 400 kHz. CWM sampled the output signal of AT3600S continuously during the whole test with 10-MHz frequency
and 12-bit resolution. The noise level was about 30 mV in RMS.
Results and Discussion
Figure 3 shows the result of 40-mJ pulse. The pulse was strong enough to register as an AE
event from as-recorded waveform (Fig. 3(a)). The spectrogram (Fig. 3(c)) of this waveform also
contained strong signal component and weak noise component between 200 kHz and 500 kHz.
In order to reduce this noise component, only a frequency range between 100 kHz and 250 kHz
remained after applying pruning method. The white noise was reduced by soft-thresholding
method with 6% of maximum magnitude as the threshold value (Fig. 3(d)). A clear waveform(Fig. 3(b)) was obtained by inverse STFT in comparison to the original waveform.

Fig. 3 Noise reduction of simulated AE signal by 40-mJ pulse; (a) recorded waveform, (b)processed waveform, (c) STFT result of recorded waveform, (d) spectrogram after noise reduction.
Figure 4 shows the result of 20-mJ pulse. Pulse was very weak and AE event was not visible in as-recorded waveform (Fig. 4(a)). However, a weak signal component was discernible in250

the STFT result (Fig. 4(b)). Therefore, only a frequency range between 75 kHz and 175 kHz
was remained by pruning method, and the white noise was reduced by soft-thresholding methodwith 6% of maximum magnitude as the threshold value. The processed waveform (Fig. 4(c))
could be detected as AE event.

Fig. 4 Noise reduction of simulated AE signal by 20mJ pulse; (a) recorded waveform, (b) STFT
result of recorded waveform, (c) noise reduced waveform.

300

Voltage, V / mV(b) 400

300

Voltage, V / mV(a) 400
200
100
0

100
0

-100

-100

-200
-300

-200
-300
-400
-100

200

0

100

Time, t / μs

-400
-100

200

500

500

400
300

50%

200

Frequency, f / kHz

100% (d) 600

Frequency, f / kHz(c) 600

100

Time, t / μs

200

100%

400
300

50%

200
100

100
0
-100

0

0
-100

0%
0

100

200

Time, t / μs

0%
0

100
Time, t / μs

200Fig. 5 Noise reduction of simulated AE signal by 40-mJ pulse; (a) recorded waveform, (b) noise
reduced waveform, (c) WT result of recorded waveform, (d) spectrogram after noise reduction.
The noise-reduction processing was also conducted with WT. Figures 5 and 6 show the
waveform and spectrogram of the same AE event in Fig. 3. The noise component below 100
kHz was relatively strong in the spectrogram of the as-recorded waveform, but this noise can be
cut by pruning method (Fig. 5(d)). The noise-reduced waveform (Fig. 5(b)) is clear in the time
domain and the major characteristics of waveform are kept as the original waveform. Figure 6
shows the comparison of a rising waveform by the STFT-based and WT-based noise-reduction
processes. WT shows sharper rising waveform than STFT. This good time resolution of WT iseffective for the location of AE events.

251

Fig. 6 Results of (a) STFT-based and (b) WT-based noise reduction process.
Conclusion
A noise-reduction process to improve the sensitivity of laser AE method was investigated.
The output signal of laser interferometer was continuously recorded and the recorded waveform was processed to reduce noise component by time-frequency analysis and combination ofpruning method and soft-thresholding method. Simulated AE detection test was conducted toconfirm the effectiveness of the noise reduction process. Weak AE signals buried in noise canbe recovered and the improvement of sensitivity of laser AE system was demonstrated. Current
CWM system can process 2-channel AE waveforms of 10-MHz sampling in real time, extracting about 100 events/s.
References
1. M. Enoki, M. Watanabe, P. Chivavibul and T. Kishi, Sci. Technol. Adv. Mater., 1, 2000, 157-165.
2. M. Watanabe, M. Enoki and T. Kishi, Mater. Sci. Eng. A, 359, 2003, 368-374.
3. S. Nishinoiri, M. Enoki, T. Mochizuki and H. Asanuma, Mater. Trans., 45, 2004, 257-263.
4. A. Abbate, S. C. Schroeder and P. Das, IEEE Trans. on Ultrasonics, Ferroelectronics, and Frequency Control, 44, 1997, 14-26.
5. K. Ito and M. Enoki, Mater. Trans., 48, 2007, 1221-1226.

252

MONITORING THE EVOLUTION OF INDIVIDUAL AE SOURCESIN CYCLICALLY LOADED FRP COMPOSITESRUNAR UNNTHORSSON, THOMAS P. RUNARSSON and MAGNUS T. JONSSON
Department of Mechanical & Industrial Engineering, University of Iceland, Reykjavik, Iceland

Abstract
An experimental methodology for tracking the locations of multiple individual sources of
acoustic emission (AE), in cyclically loaded fiber reinforced polymers (FRP), is presented. The
approach is developed, discussed, and demonstrated using AE data measured during the fatigue
testing of an assembled CFRP prosthetic foot. The results are presented as 2D images where
paths show evolution of AE sources. From these paths, the locations of the AE sources in eachfatigue cycle can be tracked. Hence, this methodology makes it possible to monitor damage
growth and to identify AE sources. The technique forms the basis for further study which otherwise would be difficult to accomplish due to the overwhelming number of sources with similar
characteristics. As a result a useful tool for monitoring and evaluating the health of a composite
is developed.
Keywords: Source tracking, carbon fiber, composites, fatigue
Introduction
Carbon Fiber Reinforced Polymer (CFRP) composites have many interesting properties suchas high strength-to-weight ratio and excellent corrosion and fatigue tolerance. Despite this, damage in composites develops early in service [1-4] and continues to accumulate throughout theservice life. The fatigue tolerance can be attributed to resistance to inhomogeneous damage
growth, which is a property of highly inhomogeneous materials [3]. The high damage toleranceof composites means that composites are able to meet their in-service requirements for prolonged
period of time while damage accumulates and grows. Consequently, there is a definite need todetect, monitor and evaluate individual damages. A system, which enables successful diagnosis
and prognosis, can possibly be used to extend the service life and to prevent catastrophic failure.
The type and the location of damages are random and the fatigue behavior changes with different lamina sequences, geometry and loading conditions. For these reasons, and the fact that
testing of composites shows a high experimental scatter [3, 5], fatigue modeling of composites isgenerally a formidable task. Existing fatigue models are based on certain materials, laminate sequences and loading conditions [1, 6, 7]. Due to these restrictions, the models cannot generally
be extended to composites, which have different lay-up or are used under different loading conditions. Under repetitive or cyclic loading conditions, AE is emitted from both damage progression and from cumulated damage; i.e., rubbing of delaminated surfaces. As a result, multiple AEtransients with varying amplitude, duration, and frequency are emitted in each cycle and manyare emitted simultaneously. Furthermore, the values of the AE signal features from cumulated
damage usually fall in the same range as the ones from damage growth [4, 8]. Waveform based
parameters such as ring down count, amplitude, and energy have been used for the detection ofdamage; i.e., delamination, matrix cracking, debonding, fiber cracking, and fiber pull-out [8-12].
For this reason, an intuitive approach to monitoring is to keep track of one or more waveform
parameters, which characterize the AE from the source of interest. Each parameter will follow aJ. Acoustic Emission, 25 (2007)253© 2007 Acoustic Emission Groupprobability distribution, which changes when the source (damage) changes. Because of the parameter fluctuations and the high rate of AE with similar parameter values, waveform parametersalone are not sufficient for distinguishing between sources. Additional indication is therefore
needed. If each source emits an AE at the same load level in each cycle, then the load level of
AE occurrence is sufficient to distinguish between the sources. However, as the delamination
cracks grow, the load level of occurrence changes.
The methodology presented in this paper uses both the time of occurrence and the AE amplitude in each cycle for monitoring the evolution of many individual AE sources simultaneously.
From the resulting images, one can locate interesting AE sources for further study, or for tracking, which otherwise would be difficult to accomplish due to the overwhelming number ofsources with similar characteristics.
The remainder of the paper is organized as follows. In section 2 the proposed methodology
is developed and in section 3, it is demonstrated and discussed by applying it to AE data, which
has been studied before by the authors in [13]. Finally, a conclusion of the work is presented.
Methodology
This section describes the proposed experimental methodology, which is a graphical tool developed for tracking the locations of individual AE sources. It was designed for monitoring objects subjected to repetitive loading conditions. By using the images generated by the methodology, one can monitor the evolution of individual AE sources and locate interesting AE signals
for further study. Figure 1 shows a schematic overview of the methodology.

Fig. 1. Schematic overview of the proposed experimental methodology.
The first step of the approach is to split the AE signal into segments of length equal to the period of one cycle. If the AE signal is not continuously acquired, i.e., measured periodically, thencare must be taken to ensure that the segments all start at the same phase of each cycle. A reference signal, such as displacement or load, can be measured simultaneously and used for segmenting. In the next step, each segment is bandpass filtered into N subbands. The user selects254the type of filtering, number of subbands (N), and the individual subband bandwidths. Figure 2
illustrates these two steps.
In the third step, a feature vector is generated from each subband segment. Each feature vector is generated in two stages. In the first stage, the AE feature of interest is extracted from thesegment. Both the positions within the segment and the feature values are logged.

Fig. 2. The AE signal is segmented and each segment is split into N subbands.

Fig. 3. For each subband segment, new feature vector is computed by first rectifying the signal,then computing a piecewise constant envelope, and finally down sampling the envelope.

255

In the second stage, the segment is partitioned into K intervals and the features extracted
within each interval are processed. The user selects the number of intervals, K. The results fromthe processing are output as a feature vector (K1). Depending on the processing in the second
stage, the first stage can in some cases be omitted; i.e., when the energy in each interval is computed, or the maximum amplitude. In order to illustrate how a feature vector is generated, themaximum amplitude in each interval was chosen. Figure 3 explains the procedure. Each subband segment is first rectified and partitioned into K intervals and then the maximum amplitude
within the interval is found; i.e., a piecewise constant envelope is generated. The envelope isthen down-sampled by a factor L/K, where L is the length of the subband segment. The resulting
feature vector contains one sample from each interval of the envelope. The amplitude filteringand down-sampling process extracts the amplitude of the strongest transient in each interval.
Hence, the tracking capability is limited by this filtering. However, since the filtering is performed in all the subbands the tracking ability is improved because the AE energy from different
sources often resides in different subbands. The down sampling also helps keep the data manageable since the number of samples acquired during one fatigue cycle can be high.

Fig. 4. For each subband, new feature vectors are appended to previous vectors and an intensity
image is generated.
In the fourth step, each new feature vector is appended to previous vectors from the same
subband and an intensity image is generated. Figure 4 illustrates this procedure. In the fifth andthe last step, image processing is performed in order to enhance the images and to make the paths
more prominent.
Experimental Procedures and Results
In order to demonstrate the methodology outlined above, it was applied to an experimental
data measured during fatigue testing of an assembled CFRP prosthetic foot made by Össur hf.
The foot was placed in the test machine and two actuators were used to apply amplitude controlled cyclic loading at 1 Hz. One actuator applied load to the forefoot and the other to the heel.
The foot was cyclically tested until a 10% change in displacement, with respect to initial value,256

was observed for either actuator. Figure 5 shows a schematic representation of the experimental
setup.

Fig. 5. A schematic representation of the experimental setup.
The AE signal and the position of the forefoot’s actuator were measured simultaneously for2.2 seconds every 5 minutes throughout the test. The sampling rates were set to 1.25 MHz and
500 Hz, respectively, for the AE and the position measurements. The L-Gage Q50 infra-red displacement sensor from Banner Engineering was used for the position measurements of the actuator loading the forefoot. For measuring the AE signals, the VS375-M AE transducer and the
AEP3 preamplifier from Vallen Systeme GmbH were used. The preamplifier was equipped with
110-kHz high-pass and 630-kHz low-pass filters. The gain was set to 49 dB. The output fromthe preamplifier was fed to a 16-bit A/D converter for a full waveform digitization. For further
information about the experimental setup the reader is referred to [13]. The position measurements were used for reference when the AE signal was segmented. Each AE measurement was
trimmed so that it represented exactly one fatigue cycle, starting at the lowest position of theforefoot’s actuator. Elliptical bandpass filters, each with 33-kHz bandwidth, were used to decompose the AE signal above 100 kHz into subbands. The bandpass filtering was performed using a phaseless filtering in order to avoid phase delay [14].
Each subband segment was divided into K = 200 intervals before computing the piecewise
constant envelope. Figure 6 shows the resulting intensity image for the 133-166 kHz subband
and also the evolution of the AE energy and the AE hit count from each segment. Intensity images are a convenient way to visualize the range of data; i.e., the higher the amplitude, thebrighter the image pixels. The energy and the hit counts were computed from the unfiltered AE
signal. The AE energy is the sum of the signal's values squared. The AE hits were determinedusing a short-term Fourier transform-based approach [13]. By comparing the evolution of theenergy, and the AE hit count, to the subband images, one can gain better understanding of thechanges, which are occurring in the material. An example is the energy spike, which occurred atsegment no. 130. This spike was due to the formation of a new damage. After this spike, thesignal’s energy provided no further information about the damage evolution. However, from thesubband images, the beginning of two new paths can be observed at segment 130 and their257

evolution can be monitored up to segment no. 239 where an apparent increase in AE amplitude
can be seen. At segment 239, a half of the foot delaminated but the 10% displacement failure
criterion was not met.

Fig. 6. The resulting images for the 133-166 kHz subband.
Figure 7 shows the resulting images for the 266-300 kHz (left) and 366-400 kHz (right) subbands. The evolution of some of the sources is present in both images; i.e., these sources emit
AE signals, which lie in both subbands. The evolution of other sources is present in either image
and not matched in the other. By studying different subbands, one can possibly detect bandlimited AE and distinguish between two damages that emit AE signals at the same time but are
evolving in different directions, as can be seen by comparing the circled paths in the left andright images of Fig. 7.

Fig. 7. The resulting images for the 266-300 kHz (left) and 366-400 kHz (right) subbands.

258

Conclusions
An experimental methodology for tracking the evolution of individual AE sources while
monitoring cyclically loaded objects was presented. The decomposition of the AE signal into
subbands enables the detection of band limited sources which otherwise would possibly go undetected. The methodology is a graphical tool, which aids with the monitoring and interpretationof AE data and should be a welcome addition to the toolbox of any engineer involved in AE
monitoring of cyclically loaded objects.
Acknowledgements
The authors gratefully acknowledge Össur hf. for providing samples and access to their testing facilities. The work of the first author was supported by grants from: The RANNIS ResearchFund, and the University of Iceland Research Fund.
References
1. J. Degrieck and W.V. Paepegem, Applied Mechanics Reviews, 54, 2001, 279-300.
2. W. Van Paepegem and J. Degrieck, International Journal of Fatigue, 24, 2002, 747-762.
3. H.G. Halverson, W.A. Curtin, and K.L. Reifsnider, International Journal of Fatigue, 19, 1997,
369-377.
4. Y.A. Dzenis and J. Qian, International Journal of Solids and Structures, 38, 2001, 1831-1854.
5. J. Baram, Experimental Mechanics, 33, 1993, 189-194.
6. K.P. Dyer and D.H. Isaac, Composites Part B: Engineering, 29, 1998, 725-733.
7. M. Knops and C. Bogle, Composites Science and Technology, 66, 2006, 616-625.
8. D. Tsamtsakis, M. Wevers, and P. De Meester, Journal of Reinforced Plastics and Composites,
17, 1998, 1185-1201.
9. M. Giordano, A. Calabro, C. Esposito, A. D'Amore, and L. Nicolais, Composites Science andTechnology, 58, 1998, 1923-1928.
10. M. Wevers, NDT and E International, 30, 1997, 99-106.
11. H. Nayeb-Hashemi, P. Kasomino, and N. Saniei, Journal of Nondestructive Evaluation, 18,
1999, 127-137.
12. E.R. Green, Journal of Nondestructive Evaluation, 17, 1998, 117-127.
13. R. Unnthorsson, T.P. Runarsson, and M.T. Jonsson, International Journal of Fatigue,
doi:10.1016/j.ijfatigue.2007.02.024, 2007.
14. C.A. Mercer. Prosig Inc., 2001.

259

ON USING AE-HIT PATTERNS FOR MONITORING CYCLICALLYLOADED CFRPRUNAR UNNTHORSSON, THOMAS P. RUNARSSON and MAGNUS T. JONSSON
Department of Mechanical & Industrial Engineering, University of Iceland, Reykjavik, Iceland

Abstract
This paper presents the results of a study where AE-hit patterns are used for monitoring thelocation of AE sources. The AE-hit patterns contain information about the spacing between hits
and other additional information about the hits, e.g., the amplitude. A methodology for detecting
AE hits and coding the information is developed. A procedure for searching for AE-hit patterns
in the coded representation and for processing the search results is also presented. The approachis demonstrated using experimental AE data acquired during fatigue cyclic testing of an assembled CFRP prosthetic foot. The results of this study show that the AE-hit patterns can be used to
track the evolution of AE sources. Additionally, the results show that by adding the peak amplitude of AE hits to the hit patterns, the tracking of AE sources is improved significantly.
Keywords: Source tracking, hit patterns, carbon fiber, composites, fatigue
Introduction
The research idea behind this study is to investigate whether damages in carbon fiberreinforced polymers (CFRP) generate characteristic sequences of AE hits, which could be used
to monitor damage progression. In [1] the authors used the peak hit amplitude for tracking thelocations of evolving AE sources within fatigue cycles. The purpose of this study was to evaluate whether better results could be obtained by monitoring the temporal AE-hit patterns.
One of the fundamental elements of music is rhythm. Rhythm can be determined by the relation between note accents (attack) and the rests between notes [2]. The term can be used to refer
to either a repetitive pulse, or a beat, which is repeated throughout the music or a temporal pattern of pulses. The modeling and the interpretation of temporal patterns are of interest to people
working in different disciplines. In the field of music information retrieval rhythm-based features have been extracted from audio and used for classifying music styles [3], i.e., blues, disco,polka, etc. In the field of computational neuroscience, patterns in spike trains are studied in order to understand the “language of the brain”[4].
The first step of working with temporal patterns is to determine the pulses. Once they havebeen determined, the rest, or duration, between them can also be determined and the results canbe coded into features and processed. The detection of pulses starts usually with the processingof the signal in order to make the detection more accurate. The resulting signal is called a detection function and it can be in any domain of interest, e.g., time domain, frequency domain, andtime-scale/time-frequency domain. An example of a detection function is one made using amodel called a rhythm track [5]. The rhythm-track model is based on the assumption that theaudio can be considered to be a random signal and the signal’s energy increases significantly
when a pulse occurs. The rhythm track can be created using several methods; e.g., by using thesignal’s instantaneous energy in the time- and the frequency domains [5, 6]. The pulses are detected from the detection function. The peaks in the detection function do not all correspond to
J. Acoustic Emission, 25 (2007)260© 2007 Acoustic Emission Groupthe onset of pulses, consequently, several approaches have been used to identify those that do;e.g., by using fixed threshold and peak-picking [6]. The approach taken here uses the signal’s
envelope as the detection function and hits are determined by peak-picking.
Interpreting hit patterns is a non-trivial task. Similar patterns can be generated differently,
e.g., closely spaced hits can either be due to rapid AE release or due to multiple AE simultaneously emitted from the numerous AE sources in the material. In addition, the signal can be affected by processes, such as reflection and attenuation. The attenuation can be caused by geometric spreading, dispersion, internal friction, and scattering [7]. During the progressive degradation process of a cyclically loaded CFRP composite material, the damage mechanisms in thematerial grow and some evolve into different mechanisms [8]; i.e., delamination as a result of the
coalescence of matrix cracks. By monitoring the evolution of hit patterns, and detectingchanges, the inception of critical damage mechanisms can possibly be detected before the onsetof catastrophic failure.
Methodology
This section describes and discusses the methodology used for locating the AE hits and theprocedure for coding the results into a compact form. The procedure for searching and locating
hit patterns in the coded representation is also described and discussed.
The aim is to monitor the location of evolving AE sources within fatigue cycles. Hence, thefirst step is to split the AE signal into segments of length equal to the period of one fatigue cycle.
The AE hits are then determined from a detection function. The detection function is generated
by converting the AE signal into decibels (dB). The logarithm transformation changes the dynamic range of the signal by enhancing low values, while compressing high values. The transformation is useful when the transducer cannot be placed at the location of damage and the AE
signal suffers from high attenuation. Furthermore, the transformation makes it possible to use
one setting for detecting both high and low amplitude hits.
The hits are detected and located by peak picking of the detection function. The peak picking is performed by an algorithm, which incrementally removes peaks and valleys that havepeak-to-peak values below a threshold, Tpp, starting with the smallest ones [9]. The threshold
controls the sensitivity of the approach. If the sensitivity is increased then smaller pulsations in
the AE signal will be detected as hits.
Once the hits have been located, the results are coded into a coding vector. The structure of
the coding vector can be accomplished in many different ways. Figure 1 outlines one possible
structure of the coding vector, in which the coded inter-spike intervals (ISI) can be used with additional information. The information is coded and put in a vector, which is placed in between
each coded ISI. Hence, each hit is represented by a substring in the coding vector.
In this study, two different coding approaches are investigated. The first uses only ISI coding and the second combines the ISI coding with a coded representation of the peak amplitude.
The coding of the ISI is made by first logarithmically transforming (Log10) the time between thepeak amplitudes of successive hits, measured in milliseconds. The results are then shifted,
scaled, and rounded so that each interval is represented by integers between NISI and 1. The
coding of the peak amplitudes is performed similarly. The amplitudes are logarithmically261

transformed (Log10), shifted, scaled, and rounded so they are represented by integers ranging
from 1 to NAMP.

Fig. 1. The generation of the coding vector illustrated using substrings with coded amplitude andcoded ISI.

Fig. 2. The procedure for finding hit patterns and processing the results into a feature vector.
The procedure for searching for hit patterns in the coding vector is illustrated in Fig. 2. Before starting the search, the user must choose the lengths of the hit patterns. The lengths must be
integer multiples of the substring length. The locations where a pattern, HP, is observed arestored in an observations vector, OP, where P = 1 ... NP and NP is the number of hit patterns. The
observation vector is of same length as the original AE signal segment and initially contains only262

zeros. The locations where a pattern, HP, is observed within the AE signal are indicated byones (1) in the observation vector. The vector is then partitioned into K intervals and the numberof observations in each interval is counted. The results are put into a feature vector which has K
entries.
Results.
The test specimen, the test setup, and the experimental procedure used in this study, were described in references [1] and [9]. The experimental data and the graphical tool presented in [1]were used for studying the evolution of AE hit patterns. The same settings were used for thesubband filtering. The peak-to-peak threshold, Tpp, was adjusted for each subband, so the average number of amplitude pulses in the first 5 measurements was around 10.000; i.e., averagepulse duration was 0.1 ms. The values of NISI and NAMP were both set to 10 and K = 200 intervals were used when computing the feature vectors. Two different coding representations werestudied: one using only ISI information and another using both ISI and the peak amplitude of thehits.
ISI coding
Approximately 60 patterns were obtained for each subband using ISI coding. However, only
a handful of the patterns provided images which could be used to track the locations of the AE
sources within the fatigue cycles. The two intensity images in Fig. 3 show the results obtainedby logging the locations where two different patterns were observed. The prominent paths observed in both images show that the ISI is a characteristic feature of certain AE sources and it
can be used to locate interesting AE sources for further study, or for tracking. Half of the footdelaminated at segment no. 239, but the displacement failure criterion was not met [6]. Due tothe rubbing of the delaminated surfaces high number of AE hits with similar ISI were emitted.
As a consequence, only few paths can be identified from the remaining segments.

Fig. 3. The resulting images for two patterns using only ISI coding. The image on the left shows
the results for the 333-366 kHz subband and the one on the right shows the results for the 133166 kHz subband.

263

ISI/peak amplitude coding
By combining the ISI information with the peak amplitude of each hit, the total number ofpatterns increased up to approximately 600 patterns for each subband. The increase is a functionof the number of quantization levels used for the amplitude, NAMP. By visually inspecting theevolution of patterns and selecting patterns that show evolving trends, a composite image can bemade which enables better understanding of the evolving damages in the material.
Figure 4 shows such an image, made by picking out, overlaying, and enhancing paths from
28 patterns. The patterns were handpicked from all subbands. The image on the right side ofFig. 4 shows the evolution of the AE energy in each segment. The AE energy was computedfrom the unfiltered AE signal by summing up the signal's values squared.

Fig. 4. A composite image made by overlaying and enhancing the results from 28 patterns (left)
and the evolution of the signal’s energy from each segment (right).
A comparison of the intensity images in Fig. 3 to the intensity image in Fig. 4 reveals that theaddition of the peak amplitude makes it possible to track the evolution of several AE sources after segment no. 239. The tracking improvement can also be observed by comparing the 7651000 ms region of the images. In this region of the fatigue cycle, the initiation of 3 paths can beobserved (circled). These three paths can be monitored up to segment no. 239 where half of thefoot delaminated.
In the 765-1000 ms interval strong AE signals were emitted from splinters [9]. Splinters, ortear outs, on the sides were commonly observed on the foot component in the area from the midfoot to the ankle. The reason for the formation of splinters was the varying width of the footcomponents. The ends of the cut fibers were only held together by the matrix, which eventually
failed due to repeated compression and tension loading. The splinter for this foot was located at
the lower half of the foot component, i.e., below centre line. This caused the splinter to push out
during upward movement of the toe’s actuator (loading) and snap back during unloading, often
producing an audible sound.
264

The reliability of the additional paths obtained by the combining the ISI and the peak amplitude in the pattern coding can be evaluated by comparing the paths, which can be obtained using
both coding approaches. Based on this evaluation method, the additional paths must be considered to be significant.
Conclusions
In this paper, we developed a methodology for detecting AE hits and for coding information
extracted from the detected hits, i.e., the duration between hit peaks. We also developed a procedure for searching for AE-hit patterns in the coded representation and for processing the search
results. The approach was then demonstrated using experimental AE data acquired during thefatigue testing of an assembled CFRP prosthetic foot.
The results show that the information provided by the inter-spike intervals (ISI) of the AE
hits alone can be used to track the locations of AE sources. This is especially interesting since
the study of ISI as a feature to track AE sources has, to the best of our knowledge, not been studied previously. In [1] the authors proposed a graphical tool for tracking the locations of AE
sources in cyclically loaded objects. The tool was demonstrated by using the maximum amplitude in each interval of the cycle as a feature to track. This tool was also used here for tracking
AE sources. The results obtained by using only ISI information are similar to the results presented by the authors in [1].
A significant improvement in the source tracking was accomplished by combining the ISI information with the peak amplitude of each hit. The addition of the peak amplitude makes it possible to track sources when there is very high AE activity, for example once delamination has
occurred. The reason for this is the additional information provided by the amplitude of the AE
hits, which produces a higher number of AE-hit patterns. This increase in the number of patterns
is because the observations of patterns with certain ISI coding were divided between several patterns; i.e., patterns with the same ISI coding but different amplitude coding. Hence, the amplitude addition worked like a filtering.
Currently, selecting useful hit patterns for AE tracking is done manually by visual inspection.
It is unlikely that the same patterns will be useful for other test specimens. It would be, therefore, useful to develop a technique for the automatic selection of useful patterns.
Finally, the experimental data used for this study was obtained during multi-axial cyclic loading of an assembled (bolted) complex-shaped composite. Hence, it is concluded that the approach used here can be successfully applied in a real world setting.
Acknowledgements
The authors gratefully acknowledge Össur hf. for providing samples and access to their testing facilities. The work of the first author was supported by grants from: The RANNIS ResearchFund, the Icelandic Research Fund for Graduate Students, and the University of Iceland Research Fund.

265

References
1. R. Unnthorsson, T. P. Runarsson, and M. T. Jonsson, in "Proceedings of the Sixth International Conference on Acoustic Emission" (Kanji Ono, ed.), AEWG, 2007, Lake Tahoe, Nevada,USA.
2. H. F. Olson, Music, physics and engineering, Dover Publications 1967, 460 p.
3. S. Dixon, E. Pampalk, and G. Widmer, in "International Symposium on Music Information
Retrieval", ISMIR, 2003.
4. F. Rieke, D. Warland, R. de Ruyter van Steveninck, and W. Bialek, Spikes: Exploring the
Neural Code (Computational Neuroscience), The MIT Press, 1997, 395 p.
5. W. A. Sethares, R. D. Morris, and J. C. Sethares, Speech and Audio Processing, IEEE Transactions on 13, 2005, 275-285.
6. J. P. Bello, L. Daudet, S. Abdallah, C. Duxbury, M. Davies, and M. B. Sandler, Speech andAudio Processing, IEEE Transactions on, 13, 2005, 1035-1047.
7. W. H. Prosser, in "Proceedings of the SPIE conference on Nondestructive Evaluation Techniques for Aging Infrastructure and Manufacturing: Materials and Composites", December 2-5,
1996, Scottsdale, Arizona, pp. 146-153.
8. E. K. Gamstedt and B. A. Sjögren, Composites Science and Technology, 59, 1999, 167-178.
9. R. Unnthorsson, T. P. Runarsson, and M. T. Jonsson, International Journal of Fatigue,
doi:10.1016/j.ijfatigue.2007.02.024, 2007.

266

AE MONITORING OF SOIL CORROSION OF BURIED PIPEHIDEO CHO and MIKIO TAKEMOTO
Faculty of Science and Engineering, Aoyama Gakuin University,
5-10-1, Fuchinobe, Sagamihara, Kanagawa 229-8558, Japan
Abstract
Most pipelines in Japan have been used for the past 30 years or more. AE method is expected
to estimate the corrosion zone. We first studied AE activity of the rust produced by 10-week soil
corrosion. Next, we studied attenuation of cylinder wave by the rust-prevention tape and wet soil.
Finally, we report AE activity of the steel pipe subjected to soil corrosion for 20 years. For thispipe, AE signals were monitored for a short period using low-frequency AE sensors mounted
close to the rust. We, however, cannot predict the active period of the rust. Thus, AE monitoringfrom the rust fractures of underground pipes is limited for short distances less than one meter. It
takes a long time to monitor the AE signals from soil-produced rust.
Keywords: Underground pipe line, soil corrosion, cylinder wave, attenuation, rust fracture
Introduction
Underground pipelines in Japan were constructed during the time before Tokyo Olympics in
1964. Corrosion of the underground pipes is becoming serious problems both in chemical plants
and public utilities. Japanese companies are recently inspecting the wall reduction of the underground pipelines using a guided-wave inspection system. This system generates strong F- or Tmode cylinder waves using piezoelectric or electromagnetic transducers and detects the waves
reflected by wall reduction. The guided waves, however, cannot propagate more than 2 m forunderground straight pipes due to large attenuation of the cylinder waves [1, 2].
Acoustic emission (AE) technology cannot monitor the wall reduction, but can estimate thecorrosion zone, if we can detect AE signals from rust fracture. Strong requirement to the AE
technique is the detection of localized corrosion of the pipelines, due to local damage to the rustprevention tape and asphalt cloth. AE activity of the rust produced by soil corrosion (soilproduced rust, hereafter) is considered to be different from the rust produced by atmosphericcorrosion (atmosphere-produced rust). We first compared AE activities of the soil- and atmosphere-produced rust using steel plates exposed to weathering. AE activities of these plates undermechanical loading and thermal cycles were studied. Next, we studied attenuation of the cylinderwaves by wet soil and new rust-prevention tape. Lastly, we monitored AE signals from a steel
pipe with thick rust produced by underground corrosion for 20 year.
Structure of the Rust Produced by Soil and Atmospheric Corrosion
We prepared 250 steel plates (45-mm wide, 90-mm long and 2-mm thick), subjecting them tosoil or atmospheric corrosion for 2, 5 and 10 weeks. The soil-produced rust was prepared byburying the plates in two types of soils. One is the so-called “red-soil” and another “black soil”.
The former is pasty and the latter is muddy when they absorb water. These soils correspond to
two layers of the four-layered loam of volcanic trass in Tokyo area [3]. The soil is slightly acidic.

J. Acoustic Emission, 25 (2007)

267© 2007 Acoustic Emission GroupThe plates are coated by the rust-prevention adhesive tapes except the center portion of 20-mm
square on one side, and buried in the soils.

Fig. 1 Distribution map of elements in the soil-produced rust produced by soil corrosion.
The soil-produced rust was very hard and well adhered to the steel even after brushing by afine brass wire brush. Thick rust of 1 mm or more were produced over 60% of the exposed area.
In contrast, the plates exposed to the atmospheric corrosion produced thin rust, which were easily
removed by brushing. Thick rust more than 0.5 mm were produced on only 5% of the exposedarea.
As shown in Fig. 1 (transverse EPMA of the soil-produced rust), the rust contains a numberof soil elements (SiO2, Al2O3, Fe2O3 and less CaO, MgO, NaO and K2O) in addition to the ironoxides (Fe2O3 and Fe3O4) shown as [Fe and O]. We observed a number of branched cracks in thesoil-produced rust.
AE Activities of the RustAE activity by three-point bending
AE signals during three-point bending with the span distance of 30 mm were monitored byfour small resonant sensors (PAC, Type-PICO) mounted on four corners of 30-mm square.
Strain at loading point was measured by a strain gage mounted on the base metal, and is increased up to 0.2%. Sensor outputs were amplified 60 dB, and stored in a computer as digital
data.
Figure 2 shows nine diagrams of cumulative AE event counts and strains for three rust produced by 2, 5 and 10 weeks of exposure. Average temperature of June was 22˚C with 140 mm
rain and that of July was 25˚C with 170 mm rain. Average amplitude of the F-mode Lamb waves
is also shown in the diagrams. The plates with atmospheric rust produced 100 events after 5week exposure, while those buried in the red soil produced 600 events after 2-week exposure.
Vertical arrows near the curves indicate the threshold strain, above which AE signals were268

Fig. 2 Cumulative AE counts with applied strain for the steel plates exposed to soil and atmospheric corrosion.
continuously detected. The threshold strains are much smaller for the plates buried in the soils.
This plate produced AE signals continuously at strains above 0.022%. It is noted, however, thatthe amplitude is almost identical for the atmosphere- and soil-produced rust. AE activities interm of the event counts are much higher for the soil-produced rust. This suggests that the external loading such as internal gas pressure to the underground gas pipelines is effective for monitoring the integrity loss.
AE activity the rusts by wet and dry cycles
Any steel plates, dried in a room after removal from exposure site, did not produce AE during thermal cycles between 25˚ to 40˚C. These results suggest that drying of the rust must be
avoided to measure the AE activity. Unfortunately, we dried all samples in room air and kept
them in a desiccator, so we wondered what type of test methods we should use. After trials of
many types of tests, we decided to measure the AE activity after immersing them in water for
one day. We detected a few AE during the first heating, but no AE during second heating.

269

Fig. 3 Changes of event counts and average amplitude of AE signals from rusts.

Fig. 4 Changes of cumulative event counts and amplitude during drying.
Figure 3 shows changes of the cumulative AE counts and average amplitude with exposure
time. These show a strange change. Strong AE signals were produced after 5-week exposure,while the amplitude of AE signals decreased at 10-week exposure. There also observed no clear
difference in both the amplitudes and event counts of the rust produced by atmospheric and soil
corrosion. Atmosphere-produced rust produced strong AE, as seen in 5-week data. It is again
noted that the result obtained by the proposed method does not necessary represent the AE activity of the rust as they are in natural weathering. AE activity must be measured at the site of astructure.
As the rust grows slowly and is generally brittle, large cracks rarely occur just after frequent
large cracks. This is studied by another test and shown in Fig. 4. This figure shows change of270

cumulative event counts and amplitude of Lamb waves during natural drying of the rust produced by black soil by 4-week exposure. Drying is done in air-conditioned room at 27˚C. We
observed vigorous AE signals with large amplitude during natural drying. This strongly indicatesthat AE must be monitored during the drying period of grown rust. It is, however, almost impossible to estimate the drying timing of the rust in the soil. Underground pipelines (1-m depth) arealmost always in wet condition. If we dry the pipeline using inert hot gas, we can perhaps monitor the integrity loss of underground pipelines. This technique was once used for the field test of
corroded pipes with cooperation with steel-making company. Result was inconclusive since thecorroded pipes were once dried before being buried.
Attenuation of the Cylinder Waves by Rust Prevention Tape and Wet Soil
We measured the attenuation. Accurate attenuation measurement of the cylinder waves is difficult. We have to use an advanced method for calculating the energy of the separated mode [4].
We, thus, measured an apparent attenuation using the amplitudes of the L- and F-modes. Even inthis method, a problem often arises for the F-mode wave, since the amplitude of the F–modechanges along the pipe circumference [5]. Figure 5 shows a method for measuring the attenuation. Method A is for measuring the attenuation by the rust-prevention tape (JIS Z1901, 0.4-mm
thick polyvinyl chloride adhesive tape), and method B for that by the wet black soil with 40
mass% water. The cylinder wave was excited by a 100-kHz PZT transmitter of 80-mm diameter
mounted to the right end surface and monitored by three AE sensors (PAC, Type-PICO)
mounted at angle of 0˚, 120˚ and 240˚. Attenuation was measured by changing the width of the
double wrapped tape and wet soil in the right portion.

Fig. 5 Method for measuring the attenuation of cylinder wave due to the rust-prevention tapeand wet soil.
The left of Fig. 6 shows changes of the amplitude of L- and F- modes with the length of thetape. Surprising is that the average attenuation (-12.7 dB/m) of the L-mode by the tape is larger
than that (-6.8 dB/m) of the F-mode wave. The attenuation by the wet soil is much smaller thanthat by the rust-prevention tape. Another interesting finding, for the wet soil, is that the averageattenuation (-3.8 dB/m) of the F-mode wave is larger than that (-0.8 dB/m) of the L-mode.
These data suggest that the attenuation by the new tape becomes hazard to AE monitoring. In
another word, if AE is detected, it means the rust-prevention tape is aged. This timing may
271

Fig. 6 Output of L- and F-mode cylinder waves vs. length of the attenuator. The left is by the
corrosion-prevention tape and the right by the wet soil.
coincide with the timing of corrosion initiation of the coated pipeline. We also attempted tomeasure the attenuation with the asphalt cloth, but could not measure it due to too large attenuation.
AE Monitoring of the Steel Pipe Buried for 20 Years
This pipe was made of plain-carbon steel, 39-mm diameter, with polyethylene coating. The
pipe was buried in soil for 20 years and some parts had through-wall corrosion. A 1-m section of
the pipe with thick rust was tested. Figure 7 shows the steel pipe after removing the coating. It
had ~2-mm thick rust over 100 mm long at the mid portion. The center 300 mm of this pipe wasagain buried in wet black soil for 4 months using a plastic box before AE monitoring. The soil
was allowed to naturally dry, but was kept wet by supplying 1000 ml water every 2 weeks. This
operation was meant to revive the rust activity.

Fig. 7 A steel pipe buried for 20 years (the upper) and monitoring method of AE from the pipe inblack soil. White dots indicate located AE.
272

AE was monitored using three types of sensors, i.e., PAC Type R15 (150 kHz resonant frequency, channels 1 and 6), PAC Type-PICO (450 kHz, channels 2 and 5) and JT (broadband
sensor up to 1 MHz, channels 3 and 4). All sensors were located within 450 mm from the thickrust. Outputs of the JT sensor were amplified 60 dB, but those of other sensors were amplified 40
dB.

Fig. 8 Corrosion potential and AE of the rusted pipe from natural drying of wet soil.

Fig. 9 Corrosion potential, temp. and AE from the rusted pipe during thermal cycles.
We monitored AE from the pipe wrapped by the rust-prevention tape for 2 days, but detected
no AE. Then we monitored AE of bare pipe. Figure 8 shows change of cumulative event counts
and corrosion potential with time over one day. Vertical open arrows indicate the timing of water supply of 300 ml. We detected few AE after water supply at 5-10, 40-50 and 75-80 ks. Thissuggested that we cannot monitor the integrity loss of the pipe by short-time AE monitoring.
273

Next we attempted another experiment 2 weeks later. We monitored AE during thermal cycles. The soil was 15 times heated and cooled by on-off operations of an infrared lamp during 2.5
days. Both the corrosion potential and AE were monitored simultaneously. Water (300 ml) was
supplied 3 times (vertical open arrows). Figure 9 shows change of corrosion potential, temperature and cumulative event counts during thermal cycles. We detected an abrupt AE at around140-160 ks, after 10 heat cycles, but no AE after this. This timing corresponds with shifting of
corrosion potential to noble direction. However, we cannot predict this timing. Difficulty indamage monitoring of the underground structure is the estimation of such timing.
Source locations of AE, estimated using the arrival times of the F-mode waves, are shown in
Fig. 7. 27 of 70 events were located in the zone with thick rust as shown by white circles in theupper photo. Waveforms of the AE detected at 122 ks are shown in Fig. 10. Amplifications areshown in each figure. Output amplitudes of the sensors (Ch. 1, 2 and 3) mounted on the left halfof the pipe are higher than those on the right half (Ch. 4, 5 and 6), indicating the rust fractures
occurred at the left portion of the 100-mm long rust. This gives us important information, i.e.,large attenuation of the cylinder wave by wet soil. This attenuation appears to be much larger
than that (-3.8 dB/m) in the Fig. 6. The reason is not well understood, but appears to be soil
thickness. It was 50 mm in Fig. 5 and 300 mm in Fig. 7.
Other information on the resonance frequencies of the sensors is as follows. Amplitude of thetwo PICO sensor outputs is much weaker compared to those of the R15 sensors. Frequency components of the F-mode detected by the JT sensor are from 80 to 100 kHz. In order to monitor AE
from underground pipes, we need to use sensors with resonance frequency lower than 80 kHz.
Another important point is wrong sensor layout. We mounted all sensors on the upper line of thepipe where the rust is produced. This layout appears to be wrong. We have to determine the sensor position, at which we can monitor the strong AE in advance.

Fig. 10 Waveforms of cylinder wave signals detected at 122 ks by 6 sensors mounted at different locations near the rust.
274

Our conclusion from these tests is that AE monitoring of underground pipes over long distance is very difficult due to the large attenuation of the cylinder waves and the capricious natureof rusts. Monitoring distance of AE for the underground pipe may be limited to less than 1 meven if we use high amplification with resonant sensors at lower frequencies. When we detect
AE from underground pipes, it means the pipes are under severe corrosion attack. Source location is not necessary since the sensors must be mounted close the corrosion zone.
Conclusion
We studied structure and AE activities of rust produced by soil corrosion and measured attenuation of the cylinder waves by wet soil and rust–prevention tape. Monitoring of AE from thecorroded pipe with thick rust taught us important information on the integrity loss monitoring ofunderground pipe by AE. Results obtained are summarized below.
1) The rust produced by the soil corrosion grows thicker including soil elements and emits AE atbeing subjected to mechanical loading. The rust loses their AE activities once they weredried. AE activity of the survived rusts, however, changes in quite complicated manner, independent of the exposure period.
2) Attenuation of the cylinder waves by new rust-prevention tapes is larger than that by the wet
soil. As the amplitude of the F-mode changes by sensor position on the pipe circumference,
we have to determine the appropriate sensor position, which can measure the cylinder waves
with higher sensitivity.
3) We monitored AE from the steel pipe with thick rust produced by the soil corrosion for 20
years. Few AE were detected in a limited period during thermal cycles. However, we cannot
estimate the timing of rapid emissions of AE. Integrity loss monitoring of the undergroundpipe by conventional AE technique appears to be difficult due to large attenuation and capricious nature of the rust, and needs advanced inexpensive system, which makes a long-termmonitoring possible. External loading may be effective for exciting the rust by fracturing.
References[1] Report by research committee on guided wave testing, J. Jpn. Soc. Nondestructive Inspection(in Japanese), 54, 2005, 596-602.
[2] T. Ikeda, R. Kinbara, M. Miyazawa, I. Matsuoka and M. Fujiwara, J. Jpn. Soc. Nondestructive Inspection (in Japanese), 54, 2005, 595.
[3] Earth Science Group, Cyclopedia of Earth Sciences. Heibon-sha (1996).
[4] H. Kasama, M. Takemoto and K. Ono, J. Jpn. Soc. Nondestructive Inspection (in Japanese),49, 2000, 269.
[5] H. Nishino, S. Takashina, F. Uchida and M. Takemoto, Jpn. J. Appl. Phys. 40, 2001, 364.
[6] D.C. Gazis, J. Acoust. Soc. Am. 31, 1959, 568.

275

THIRTY YEARS EXPERIENCE OF INDUSTRIAL APPLICATIONS OF
ACOUSTIC EMISSION TESTING AT TÜV AUSTRIAPETER TSCHELIESNIG
Institute for Technical Physics, TÜV Austria Services, Deutschstr. 10, A-1230 Vienna, Austria

Abstract
Since 1977 the TÜV Austria (till 1990 TÜV Vienna) has tested different pressure equipmentwith acoustic emission. The aims for these tests have been different for various applications suchas: first hydrotests, requalification tests of pressure containments, leak detection and the detection and evaluation of corrosion. The different equipment technologies shall be mentioned andtheir influence on the test results shall be pointed out. As examples for successful applications
the following test objects shall be mentioned:• Gas storage spheres,• Flat bottomed storage tanks (leakage and/or corrosion testing),• Steam drums (dryers) in paper mills,• Leak detection with an acoustic pig,
• Automatic testing of receptacles during the filling procedure,• Corrosion tests of oil tankers.
The importance of standardization beginning within the EWGAE, and now with CEN/TC138
WG7 "Non-destructive testing - Acoustic Emission Testing" as an accepted method in Europe
will be pointed out. At the end the rules for the training and certification according to the EN473
and their implementation within the three NDT societies from Germany, Austria and Switzerland
will be presented. The three societies give common training courses and certification examinations.
An outlook in the future of the on-coming application, based on EC funded project, shall be
provided. This outlook will show that AE testing will become in the years ahead more and more
important for the competitiveness of the European industry to make the inspection of pressure
equipment cost-effective and safe.
Keywords: Pressure equipment, integrity test, leak detection, training and certification
Introduction
Since 1977 the TÜV Austria has tested different structures, especially pressure equipment,
storage tanks and pipelines with acoustic emission. Till today more than 30.000 different structures were tested and although the methodology has changed tremendously over the time I will
review some typical applications, which were performed in the last 30 years. During this survey,
I will point out the different technology and equipment used. Especially the rapid developmentof the electronic data acquisition and treatment has changed the possibilities and application of
AE testing tremendously.
Our first contacts with AE were in 1975, when TÜV was nominated from the Austrian government to supervise the AE testing of Exxon Nuclear (with Dwight Parry) of the pressure vesselof the Austrian nuclear power plant at Zwentendorf. Two years later, we started our first own
J. Acoustic Emission, 25 (2007)276© 2007 Acoustic Emission Groupmeasurements with an Exxon Nuclear 2-channel equipment on different small structures even in
the above-mentioned nuclear power plants. Our main tasks were pumps, penetrations through thepressure containment, valves and other installations. Although this equipment had no real-timedata storage and mainly engaged to collect AE hits, it provided a t-measurement for linear location. With this tool, we were able to detect and also roughly locate different defects, leaks andnon-reported repairs in the tested structures. Even then, perhaps more so, it was very important to
combine the AE results with other information of materials science and conventional NDT applications. Based on the meaningful results, we decide to buy a 24-channel equipment from AE
International (Richland, Washington, USA) in 1979. With this system, we were able to acquireand process data coming from 4-channel arrays. The analog information was stored on a multichannel tape recorder with a dynamic range of 26 dB at 100 kHz, which was at that time remarkable [1], although this would for some of colleagues sound like a tale from the Stone Age. With
this equipment we reached a complete new stage in the application of AE.

Fig. 1: Storage sphere (left) tested with acoustic emission; location map of acoustic emission testwith active areas indicated by colored rectangles (right) at weld seams of the sphere.
Examples over the Years
Gas storage spheres
In 1979/80 the Viennese gas supplier built 2 natural gas storage spheres with a diameter of 32
m and a service pressure of 1 MPa [2]. The hydrotests before service were monitored with AE,
where we found stress concentrations based on improper welding, because parts of the spherewith different wall thickness were not welded according the neutral phases. Both spheres have
had small inside-surface cracks, mainly caused by the bending over the support during filling.
Although all welds had to undergo an NDT before the hydrotest, one big slag-inclusion was detected and located and has to be repaired.
It was remarkable, that we found many problems in the spheres, although the contemporary
AE equipment had only a few features. This shows, that the normal hydrotest with its binary
information (pass or not) is not a sufficient acceptance test for pressure equipments. The repetition tests every 6 years with different AE technology verified the results.
We have monitored during pneumatic loading two gas storage spheres of 33-m diameter at
West Berlin in 1982, and in 1991 a natural gas storage sphere with 39-m diameter at servicepressure of 1.2 MPa (the biggest storage capacity for natural gas within Europe). The AE tests
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were performed mainly for safety reasons (early alarm tool) as the structures were situated withina residential area, but also its NDT ability was used [3]. See e.g., Fig. 1.
Leakage testing of flat bottomed storage tanks
In 1981, we started, after laboratory tests and on-site trial tests, AE monitoring of the flat bottoms of storage tanks for leaks [4-6]. This testing method uses the turbulences of the fluid media
in the gap and some secondary effects between the bottom and the basement. Over the time, we
tested more than 300 tanks and were able to improve the method, especially for the discrimination between leak source and background noise and also developed special location software. In
those days, every test was a combination (comparison) of a low-liquid level (1–2 m) and highliquid level (nearly full) test.
A good example for the successful application of the method was the leak within the bottomof a product tank. This was not detected even by vacuum boxes and could only be verified bypenetrant testing (PT) of the weld between the sump and the bottom. Further development of themethod led to the application of the cross-correlation method, which also gave us the opportunity
to use the continuous AE for the detection and location of leaks. Some changes in the Austrian
legislation and the demands of the customer to get more information about the status of the bottom led to the development of the corrosion testing, within an European Commission (EC)funded project in the second half of the 1990’s. See Fig. 2.

L

Fig. 2: Leakage testing of storage tanks; AE sources indicate position of leakage.
Corrosion testing of flat bottomed storage tanks
Beside the prevention of leaks, the corrosion problem was a pressing problem for the petroleum and petrochemical industry. Because it seems to be possible, but not proven, that corrosion
can be detected with AE, the TÜV Austria together with a consortium of Vallen Systeme, (AE278

equipment manufacturer), CISE (AE testing company) and Shell Global Solution and Dow Germany (Tank user) proposed in 1995 a research and development project to the EC, which was
accepted in 1997.
By extensive research works during laboratory tests and real tank tests, the consortium wasable to prove, that corrosion and leakage can be detected but it is also possible to distinguish bothtypes of AE sources. To make the distinction, the normal AE parameters were inadequate. Therefore, we applied a frequency-domain pattern recognition system (VisualClass®) on the complete
transient time signal. Based on the different physical events, the system was able to distinguishthe different signals on a statistical basis. Of course, beside the pattern recognition additional
filter criteria have to be applied on the whole data set. (Fig. 3)
Within the EC-funded project, which was completed in 2000, we could develop and validate
this testing method. But the results showed that the AE testing has changed from a method,which was only counting hits, events or threshold crossings to a sophisticated method, where thedata processing replaced more and more the experience and “feeling” of the test operator [7].
From now on, the flat-bottomed storage tanks in some refineries, tank farms and chemicalplants will be tested regularly for corrosion and leaks. By testing several hundreds of tanks thenecessary database will be enlarged permanently and testing method will be accepted by more
and more authorities [8, 9].

2nd row sensor
Bottom row sensor

Fig. 3: Sensor layout for storage tank testing (left) and results obtained with only bottom row(red column, right) and with two rows of sensors (no indication, far right). With the arrangementin two rows it is possible to prevent the test result for the tank bottom from being flawed by corrosion of the floating roof.
AE testing of dryers (steam drums) in paper mills
For a long time, the paper mills have had the repetition of problems with their dryers tested
by hydrotest and inside inspection. Problems increased more and more due to the enlarged production output. The higher rotation speed and higher temperature led also to a smaller wall
thickness and resulted in damages to bearings, un-roundness and balance of the dryers during thehydrotests. If the cylinders have to be removed for the test, these resulted in shut-down periodsof several weeks, which was unacceptable for the production. We started to develop a more sophisticated testing method than the hydrotest by the application of AE during a pneumatic loading of the drums in their normal service position [10, 11]. The AE testing was inserted as a prewarning system for the pneumatic loading and also as a powerful NDT method, with a roughlocation based on a linear location. This became necessary due to the restricted accessibility of279

the surface of the drums. During a shut-down period of 24 hours with AE testing, 6 to 8 cylinders can be tested. This brought not only big advantages for the performance of the paper industry, but a substantial gain for the safety was achieved. Defects, which were not detected during
the conventional testing method, are now detected by the AE testing. (Fig. 4) Beside cracks,
cavitations and corrosion attacks, we found also cracks in the balance weights. If these defects
remain undetected during the shut-down period, these weights could destroy several cylinders
during the subsequent service periods with further incalculable consequences.

Fig. 4: Testing of steam drum with AE sensor installation (left); Erosion found after entering
into the steam drum due to indications obtained by AE measurement (right).
Leak detection Pig (ALARM)
In the early 1990’s, we started with the OMV to develop a leak detection pig for diameters of
100 mm and up. This development was done for the connection lines between the refinery andtank farm of OMV. See Fig. 5. A pig has always the advantage, because it carries the sensor
close to the AE source (leak) and gives the opportunity to check the pipeline in-service. After
pre-tests in the laboratory and on a test rig, test runs on real pipelines with artificial leaks were
done. The worst-case leak (drilled hole) sensitivity of the pig is 5 l/h at a minimum pressure of
300 kPa. Since 1994, the pigs (different modules for different diameters) run regularly on the
OMV pipelines [12]. At a service for the Pinghu Oil & Gas field, near Shanghai (China), on atransmission line between on-shore and an off-shore loading platform over a length of 360 km,
we detected 2 leaks and located them with an uncertainty of less than 1o/oo. This allowed the customer to repair these defects in welds.

Fig. 5: Receiving pig trap off the coast of China (left) and indications of two leaks obtained by
one pig run (right).
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Automatic testing of receptacles during filling
Together with the gas distribution company Messer Austria, we developed a brand new testand filling bench. Instead of testing the receptacles by a hydrotest, the tests will be performed
automatically during the filling operation. The test rig allows overfilling the bottles up to 1.4
times of the service pressure. The whole period of this action will be monitored with 2 sensors
each and the evaluation and stop criteria will be checked automatically from the computer according a pre-defined procedure (Fig. 6-right).. The criteria, which are established during labtests, are locked and can be changed only with our password. All rejected receptacles will be retested manually to identify the reason for the test-stop and undergoes a follow-up [13-15].

Fig. 6: AE testing on gas cylinders, 12 cylinders are tested simultaneously by pressurization withgaseous product (left), laboratory tests of fatigue cracks till burst with hydraulic pressurizationfor establishing a set of testing parameters (right top and bottom).
Detection and localization of corrosion attack on ship tankers
Based upon the application of AE testing for flat-bottomed storage tanks and horrible shipdisasters in the last several years, e.g., Erika, Prestige, Napoli, which devastated hundreds ofkilometers of the coast of Brittany, Galicia etc., we launched together with Vallen Systeme(Germany, equipment manufacturer) and four Polish companies, Gdansk University of Technology, Polish Registration society, Institute for Applied Sciences and Maritime Institute of Gdansk,
an R&D project funded by the European Commission for detection and localization of corrosion
attack on ship tankers.
The aims of the project were
• a permanent testing with an installed system, which will acquire the data during the shipping
conditions and measure on-line the status of the structure and• a spot-testing, which can be applied on ships in the harbor or on the anchor place before discharge.
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Both systems have had to overcome the problems with the rough sea environment. Based onlaboratory tests and background measurements on real ships (naval and commercial tankers), we
found out that it is possible to distinguish the AE coming from corrosion and AE produced fromshipping actions with different filter steps - frequency, localization and pattern recognition [16].
We demonstrated that we are able to detect “real corrosion sources” within ship tankers undernormal shipping conditions (Fig. 7). For these measurements, it was necessary to develop intrinsically safe, explosion-proof sensors, which were certified for zone 0 according the European
directive and approved from a shipping classification society [17].
The spot testing was solved with a new multi-element sensor (holder with 4 sensing elements; see Fig. 7-right), which was able to detect but also locate the direction from the incoming
AE wave. This was a further advantage of this project and the application of this sensor typeshall also be used for tank testing on-shore.
The project was finished in July 2006 [18]. Nevertheless, further investigations for ship corrosion become necessary, because the current technique acquires only those corrosion signals,
which are in contact with liquid. A very important part for the integrity of a vessel is the deck,
which at the moment cannot be checked.

Fig. 7: Oil tanker towed to the mooring buoy at an oil field off the coast of Poland (left), cargotanks have been tested with a multi-element sensor (right) which was lowered down throughdeck opening and then immersed into the crude oil.
Standardization
Over the decades, the application of AE testing as an NDT method changed completely. At
the beginning, it was an exotic method, which was applied only for very specific problems, andalso the confidence on the results was limited, although AE testing has often demonstrated theirability and reliability. Now based on all further developments, AE testing became more and morean accepted and regular NDT method. But such a method needs beside the technical excellence:• standardization of the method and their application and• trained and certified personnel, which has to be demonstrated against a commonly accepted standard.
Based on these demands, the test method made a big step forward, when the CEN/TC 138“Non-destructive Testing” established the Working Group 7 “Acoustic Emission” [19] for thedevelopment of basic standards for the method (terminology, control of the used equipments andgeneral principles of the different possibilities of applications). Since then, the WG7 developed
many standards, which sometimes are now under review.
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The finalized standards are:
• EN 1330 – 9 “Terminology used in AE testing”,• EN 13477 -1/-2 “AE testing-equipment characterization and control of their abilities”,• EN 14584 “AE testing of metallic pressure equipment – planar location”,• EN 15495 “AE testing of metallic pressure equipment – zone location”
and the following work items are under preparation;• WI 00138140 “Corrosion detection on flat-bottomed storage tanks”• WG7 NWI 607 “AE testing of fiber-reinforced plastics”.
As important as these standards were, it was crucial that AE testing was included in the
EN 473, which regulates the qualification and certification of the test personnel, and establish theabilities and allowances of the different 3 levels of AE testing personnel. The new EN 473:2007
or 2008 requires also a common training content all over Europe in TR 25107 “Guidelines for
NDT training syllabi”. Although training and certification do not guarantee producing excellent
AE testing personnel, it is an important basis for the further training on-the-job, which has to be
performed in the respective testing organization [20].
Beside these bundle of standards of the methodology AE testing has been introduced to different product standards in normative and informative annexes, like
• EN 13445 – 5 “Unfired pressure vessels, Testing and Inspection”,• EN 12817/18 “Inspection and re-qualification of LPG tanks up to 13 m3” and• EN 14334“LPG road tankers”.
All these standards give, beside the guidance for the application and the test performance,
criteria and rules for evaluation, real-time control and source-severity grading. Nevertheless thisis the weakest point of these standards, because the specific numbers and other qualitative
threshold are based on the experience of the test organization and/or have to be defined in the
product standards. It is obvious, that these test organization, which has a large database for manydifferent test objects will not give them just for free to other competitors. This is a problem,
which has to be solved hopefully in the years ahead.
Conclusions
The test history over the last 30 years showed, that acoustic emission was and is still a very
prospective testing method. Although it is not a classical non-destructive test method, I think weshall require from it the same rules, which are taken into account for all other NDT methods.
• AE testing shall be only applied by qualified and certified test personnel, and has to be implemented in a test organization with a quality management system in accordance with
EN/ISO 17025 and EN 473.
• AE testing shall be performed according to specific EN standards (process and/or productoriented), if available, or the method including the evaluation criteria have to be validated
according to the specific EN or other international standards.
• AE testing has to be used only by well equipped and well experienced test organizations,which have available the specific database for the respective test object or has performed
the necessary number of pre-tests on specific test samples. This is one very important requirement, because according to our experience every test object is unique in its AE behavior.
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If these requirements are fulfilled AE testing will be and become much more in the future areal-time NDT, which will not only more time saving and cheaper than other testing method, but
it will increase the safety of the structure and will be an excellent tool for the fitness for purpose
for the structures.
References
1. Tscheliesnig P., Theiretzbacher H. (1981) “Praktische Erfahrungen mit Hilfe der Schallemissionsanalyse”, 2nd ECNDT 1981, pp. 221-223.
2. Tscheliesnig P., Theiretzbacher H. (1982) “Practical experiences with Acoustic Emission onlarge storage pressure vessels”, 10th WCNDT 1982.
3. Tscheliesnig P., Theiretzbacher H. (1984) “Schallemissionsprüfung bei Druckaufbringung
mit gasförmigen im Vergleich mit flüssigen Medien”, 3rd ECNDT 1984.
4. Tscheliesnig P., Theiretzbacher H. (1985) “Leakage test by Acoustic Emission Testing(AET) on flat bottom tanks”, 11th WCNDT 1985 pp. 40-45.
5. Tscheliesnig P., Theiretzbacher H. (1989) “ New results from the detection of micro-leakagesin the petrochemical industry”, 12th WCNDT 1989, pp. 905-911.
6. Tscheliesnig P., Krenn G., Schauritsch G. (1990) “Dichtheitsprüfung mittels akustischer Korrelationstechnik in der petrochemischen Industrie”, ÖGfZP-Jahrestagung 1990, pp. 51-59.
7. TÜV Österreich, CESI Spa, Vallen System. Shell Global Solutions. DOW Deutschland(2000), /Inspection of Flat Bottomed Storage Tanks by Acoustical Methods”, Synthesis report of SMT4-CT97-2177 (EC).
8. Marino A., Tscheliesnig P. (2002) “A comparison between AE and MFL for tank floor inspection”, 8th ECNDT 2002, p. 116, CD.
9. Tscheliesnig P. (2002), “Acoustic Emission Testing (AE) of bulk liquid storage tanks – Basics, case studies and future perspectives”, 8th ECNDT 2002, pp. 119, CD.
10. Strunz K., Heck G., Tscheliesnig P. (1995) “Moderne ZFP-Verfahren zur Prüfung vonTrockenzylindern in Papiermaschinen”, ÖGfZP-Jahrestagung 1995, pp. 65-73
11. Tscheliesnig P. (1998) “New inspection method of steam drums in paper mills”, 7th ECNDT
1998, pp. 2288-2294.
12. Lackner G., Tscheliesnig P. (2004) “Field testing of flat bottomed storage tanks with acoustic
emission – a review on the gained experience“, 15th EUROCORR 2004, CD.
13. Tscheliesnig P., Krenn G., Schauritsch G., Edinger H. (1995) “An Acoustic Leak Detection
Pig”, 2nd International Pipeline Technology Conference 1995, pp. 205-210.

284

14. Tscheliesnig P., Lackner G., Hermeling W. “The automatic performance of the repetition
tests of pressure bottles with AE“ 15th WCNDT 2000, CD.
15. Lackner G., Tscheliesnig P. (2001) “Automatisierte Durchführung von Wiederholungsprüfungen an Gasflaschen mit Schallemission”, 13. DGZfP-Kolloquium Schallemission
2001, pp. 193-201.
16. Tscheliesnig P. (2004) “Detection of Corrosion Attack on ships, especially oil tankers, withacoustic emission (AE)”, 16th WCNDT 2004, CD.
17. Tscheliesnig P. (2005) “Entwicklung einer Schallemissionsprüftechnik zur Prüfung von Kor-

rosionen an Öltankern“, DGZfP Jahrestagung 2005, CD.
18. TÜV Austria et al., Final Report of the EC-funded project „Detection and Discrimination of

corrosion attack on ships (crude oil tankers) with Acoustic Emission (AE)“ project No.
EVG1-CT-2002-00067.
19. Tscheliesnig P., Fontana E. (1998) “European standardisation in CEN TC138 WG7”, EWGAE 1998, pp. 287-290.
20. Tscheliesnig P. (2004) “Training and certification on field of acoustic emission testing (AT)according the European standardisation (EN 473)”, EWGAE, pp. 235-237.

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ACOUSTIC EMISSION TESTING OF SEAM-WELDED HIGH ENERGYPIPING SYSTEMS IN FOSSIL POWER PLANTSJOHN M. RODGERSAcoustic Emission Consulting, Inc., Fair Oaks, CA 95628

Abstract
The structural integrity of seam-welded fossil high-energy piping has remained a major
safety and operations-maintenance issue for US utility companies. Several failures of seamwelded superheat and hot reheat piping segments have occurred since 1992, two of them catastrophic. Advanced methods of inspecting piping welds with ultrasonic testing (UT), such as
time-of-flight diffraction and focused/phased arrays, are pushing back the envelope of detection
to earlier stages of creep damage, but these are still very expensive, and involve considerable logistical planning and downtime to perform. The Electric Power Research Institute (EPRI) has
sponsored development activities since 1986 to mature the utilization of a real-time online
evaluation method for seam-welded piping: Acoustic emission (AE) testing guidelines were
published by EPRI in 1995, and over 100 full-scale tests have been performed to develop a database and correlate results with other established evaluation methods. An effort was begun in2002 to standardize the testing method within ASTM (AE Subcommittee of E07.04) utilizing thedeveloped database. Tests to date have shown high sensitivity to early stage creep damage,which is evidenced by development of cavities around inclusions in the grain boundaries. Successful double-blind testing with advanced ultrasonic inspection methods has proven both thereliability and sensitivity of the AE technique. The economics of the method are highly favorable. Only small areas of insulation need to be removed every 4.6-6 m to weld waveguides tothe piping surface. These form a linear location array along the length of piping, providing
global coverage of the piping system. Testing is performed online with normal peak loading andload cycling. No outage schedule is required to perform the AE examination. Results will bepresented showing that the AE method has become a reliable and economical field evaluation
tool for seam-welded high energy piping.
Keywords: Seam-welded piping, fossil power plants, online monitoring, high temperature creep
Introduction
Ever since the catastrophic failures of seam-welded, hot reheat (HRH) piping at Southern
California Edison’s Mohave plant in 1985 and Detroit Edison’s Monroe plant in 1986, utility
companies have been carefully considering the need for periodic inspections of critical piping toguard against creep-induced failures. Figure 1 illustrates the creep-damage mechanisms associated with seam-welded, high-energy piping. A number of serious defects in seamed piping were
removed after inspections in the late 1980’s, and for a number of years there were no more catastrophic failures [3]. Beginning in 1992, however, there have been six known failures of seamwelded superheat (SH) link piping supplied with Combustion-Engineering boilers, as well as two
failures in hot reheat long seamed bends. Two of these have been catastrophic: Virginia
Power’s Mt. Storm Unit 1 in June 1996, and Kansas City Power & Light’s Hawthorne Unit 5 inAugust 1998. No loss of life occurred in either of those two failures, but the cost of repairs andloss of power generation is of critical concern to utility companies in this age of growing competition. All failures of SH link piping have occurred on units with accumulated service time of
J. Acoustic Emission, 25 (2007)286© 2007 Acoustic Emission GroupFig. 1. Typical high-temperature creep damage occurring in seam-welded fossil piping systems
constructed of P11 or P22 grade steels [3]. Type IV damage in the fine-grained HAZ typicallyoccurs in subcritically annealed welds, which is more typical of thick-section SH link piping andsome HRH piping.

Fig. 2 a) Micrograph showing cavitation damage in the cusp region of an HRH double-V weld[4]. b) Micrograph showing advanced damage in the form of microcracks from a failed longseam bend [5].
125,000 to 225,000 hrs. Figure 2 shows micrographs of cavitation damage and advanced damage of microcracks from a failed long seam bend [4, 5]. These are truly microscopic defects.
Compounding the problem of inspection is the inaccuracy of supplied documentation, which
may not reflect the true alloy content and method of fabrication. The Hawthorne SH link piping287was not known to be seam-welded. The general aging of fossil plants will continue to raise concerns about the safety of operating seamed high-energy piping systems. Even seamless piping
systems have had problems, including creep-related failures of circumferential welds, and thethrough-wall creep failure of a seamless SH bend that had been improperly fabricated. Current
strategies for effectively managing the safety and life of seam-welded piping are based upon periodic inspection of the weld area for evidence of in service damage.
Equipment and AE Testing Set-up
The process of AE monitoring applied to piping systems starts with installation of AE transducers on welded waveguides (WG) along the length of the piping system. Spacing intervals for
the WG are typically 4.6-6.1 m (15-20 ft), and installation of the sensors does not require full
removal of piping insulation. There are several unique requirements for successful monitoring of
high-energy piping with AE [1, 2]:
•

•
•

•
•

The use of high-frequency sensors (300-400 kHz) and high frequency filtering (>200 kHz) to
mitigate the effects of the steam-flow background noise. This noise is predominant below
300 kHz, and would obscure detection and accurate source location if lower-frequency, or
broadband, sensors were used.
The employment of a “floating” or automatic threshold that can control the sensitivity ofdetection by keeping the voltage threshold of detection above the average background noise.
The use of active linear source location to determine the position of emitting sources on theline. The accuracy of location is dependent on the distance between sensor/waveguide positions, the pipe diameter, and the position of the emitting source. In the middle of the array
between two sensors, accuracy has been demonstrated at ±2.4 cm (±0.6”). Near the sensors
accuracy may degrade to ±60 cm (±24”). Still, this limits areas for follow-up inspection.
The use of active AE-signal-feature filtering to further refine the data and eliminate obvious
noise sources, such as flow turbulence. Rise time, duration, and average frequency have
proven most valuable.
Simultaneous recording of piping temperature and pressure are required to provide correlation between active AE sources and the likely source mechanisms.

Data Evaluation and Correlation
The primary characteristics of seam-weld creep-related sources are behavioral in nature—they respond to the pressure in the piping (hoop stress) and other mechanical sources of stress(geometry, hanger supports, etc.). During online conditions with normal peak load cycling,
creep-related sources reveal themselves by repeated behavior with each peak load cycle [2]:
•
•
•
•
•

The sources are sensitive to pressure, and may show a pronounced effect of emission ratewith pressure (Figs. 3, 4).
During load cycling, emission rates will typically peak near the start of the peak-load period.
There is periodic emission activity during steady-state pressure and temperature conditions.
The AE location profile is typically spread out over 1 m (40”) or more of piping length, andshows intermittent high-density locations of activity (Fig. 3).
The amplitude range of emission sources broadens to higher values with higher activity rates(Fig. 2).

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•Emission rates are much higher during startup conditions, even before substantial pressureloading. This demonstrates that the damaged area is responsive to stresses even when the piping is not in the creep regime (>510ºC, 950ºF).

Fig. 3. A seam-welded clamshell elbow on Illinois Power’s Baldwin #2 HRH line shows typicalcreep-related AE behavior under cyclic pressure loading (upper left). The mechanism showshigh sensitivity to load, and shows a distribution of activity along the elbow that varies with eachload cycle (lower left). Bottom right shows the amplitude density feature map of this elbow location. Distributed higher activity sources are evident, and the amplitude dynamic range is larger
in the high activity area. Defect growth at this early stage is probably related to decohesion ofinclusions that are being affected by the creep process.
The amount of emission generated by the creep mechanism, the repetitive nature with eachpeak load cycle, and the extensive dynamic range (45-90 dB amplitude) of signals, is extraordinarily different from normal ductile fracture mechanisms, such as fatigue crack growth in mild
steels. Many thousands of locatable signals are sometimes accumulated over 1 m or so of weld
length and several cycles (days) of steam line operation at peak load. The sheer numbers of thesources is inconsistent with a ductile crack growth mechanism, which produces infrequent emission of more limited dynamic range with repeated load cycles. The acknowledged mechanism of289

creep in seam welds is the development of cavities (cavitation) around nonmetallic inclusions
and carbides on the grain boundaries in the fine-grained heat affected zone (HAZ) or fusion zoneof the seam weld (Fig. 1). Isolated cavities soon give way to aligned cavitation along grainboundaries, then coalescing into scattered microcracks. Final consolidation and linking into macrocracks along the seam-weld direction occurs in the last stage of growth, which can be very
rapid depending on a host of factors (wall thickness, annealing state, inclusion densities, thermal
and localized mechanical stresses, etc).

Fig. 4. Results of AE monitoring of the Ghent #1 East HRH link piping. a) Configuration. b)
The linear source location plot shows three AE clusters detected (A, E, B).
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Fig. 4. Results of AE monitoring of the Ghent #1 East HRH link piping. c) A composite graph ofplant parameters and AE source location vs. time for the three clusters detected. The pressure
sensitivity of the sources is obvious, marking them as creep-related sources. d) The relative activity in events/inch-hr at different pressures for Cluster “A” in the original monitoring program.
After through-weld repair of the piping, the activity drops by >90% for this area, indicating thatthe original sources of emission have been removed.
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The early stage of this process involves the degradation of the bonding between particles andthe metal matrix. These are load-carrying interfaces, and their eventual failure (decohesion) is
the most plausible explanation for the amount and dynamic range of emission detected in thecreep process. From the viewpoint of classifying AE behavior, this bears similarity to the experience of monitoring an organic-based composite material that has incurred extensive matrix
damage. This also explains the emission that has been noted during the thermal excursion on
startups, even without pressure in the system, when the piping is clearly not operating in thecreep range. Damaged particle-to-matrix interfaces are prone to disbonding under high strain
conditions, and startups are known to produce an even higher axial strain than at full load operation. Indeed, the results of the extensive EPRI field testing program to date has yielded detectionof cavitation damaged seam welds that have not developed to the stage of micro- or macrocracking.
A separate test program conducted in collaboration with a UK utility demonstrated that controlled creep-crack growth in small specimens produced increasing emission with increased
crack growth rate. The emission rate was orders of magnitude higher for the increment of crack
growth than would have been expected at lower temperatures and growth under fatigue conditions. The decohesion mechanism remains active throughout the creep regime, regardless ofwhether induced by directed stress at the tip of an active crack or in a volume of weld without
visible cracking. This leads to high probability of detection of the creep-related failure processfrom very early stages, well before the damage represents a significant threat of structural failure.
Correlation of AE findings on seam-welded lines with other NDE methods and metallograpy
were an important part of the EPRI studies and field tests from 1991 to 2001. Double blind testing was performed on Pacific Gas & Electric’s Potrero #3 line in 1994, American ElectricPower’s Gavin #1 line in 1996, and Sierra Pacific Power’s Valmy #2 line over 1997-1999. In
these tests good correlation was established between conventional automated and manual multiangle UT methods and AE cluster locations in seam and girth welds. But metallography was
not used extensively in these tests to confirm the nature of the indications. Later testing would
provide more extensive correlation between AE and advanced UT methods (TOFD, Phased Array, Focused Array), and more sensitive metallographic analysis (cryo-cracking with SEM examination). These included programs on Kentucky Utilities Ghent #1 HRH line and Brown #3
SH link piping (1997-98), Central Power & Light’s Joslin #1 HRH line (1997), Illinois Power’s
Baldwin #1 HRH (1998), Salt River Project’s Navajo #2 HRH line (1998), Southwestern PublicService Co’s Harrington #2 HRH line (1999), and Portland General Electric’s Boardman HRH
line (2001).
By the late 1990’s it was becoming better understood that creep damage in seam welds did
not initiate as distinctive crack-like flaws, but rather as an accumulation of microstructural damage evidenced by “cavitation” development around inclusions and carbides along the grainboundaries in the heat affected zone or fusion zone of the seam weld (depending on whether the
structure was normalized and tempered or subcritically annealed after welding). Several highprofile failures of seam-welded piping after missed or misinterpreted UT findings (Sabine #2
HRH bend in 1992, Mt. Storm #1 SH link piping in 1996, Gaston #4 HRH bend in 2001) led to agreater sense of urgency in the fossil utility industry to find earlier stage damage more reliably inseam welds. The AE studies mentioned provided proof of early stage detection of creep damage
at the cavitation stage, often well before UT methods could reliably indicate a developing problem. Only the advanced metallographic method involving the use of cryo-cracking and SEM

292

examination at 2000-5000X was able to confirm this damage that AE was detecting at an earlystage.
The standardization of the EPRI AE methodology for seam-welded piping began in earnest
in January 2002 at the ASTM E07.04 acoustic emission subcommittee meeting. EPRI gave approval to the use of its documents and database as a necessary background for the developmentof the standard. The proposed standard WK 658 “Standard Test Method for Acoustic Emission
Examination of Seam-Welded High Energy Piping” is nearing final balloting, and is expected tobe approved in 2008. It is one of the most comprehensive and specific ever undertaken by an
ASTM committee on an AE application.
Conclusions
Acoustic emission has proven its worth in online testing programs. Approximately 30% oflines tested have shown no significant findings of creep damage, and most others have shown
only minor activity at suspect locations. The majority of seam-weld findings has been in elbows
and bends, followed by hanger locations on horizontal line segments. These are known to behigher stressed areas, and offer further validation of the AE methodology. The correlation withfollow-on nondestructive inspection has been very good, but the lesser sensitivity of UT inspection methods will generally not confirm early stage creep damage at the isolated cavitation stage.
The economics of inspection and relative certainty of detection at an early stage of creep damage
should be increasingly attractive to companies attempting to manage their piping systems in a
climate of reduced capital and operations-maintenance spending.
References
1. B. Morgan, C. Foster, Acoustic Emission Monitoring of High-Energy Piping, Volume 1:
Acoustic Emission Monitoring Guidelines for Hot Reheat Piping, Electric Power ResearchInstitute, TR-105265-V1. 1995.
2. J. Rodgers, R. Tilley, Standardization of Acoustic Emission Testing of Fossil Power Plant
Seam-Welded High Energy Piping, ASME Pressure Vessels & Piping Conference, San Diego, July 25-29, 2004, PVP-Vol. 471, pp. 113-131.
3. J. Foulds, R. Viswanathan, J. Landrum, “Guidelines for the Evaluation of Seam-Welded HighEnergy Piping, Electric Power Research Institute, TR-104631, 1996.
4. R. Munson, J. Rodgers, R. Tilley, The Utilization of Advanced Metallographic Techniques to
Verify In-service Damage in Long-seam Welded, High Energy Piping, EPRI Fossil Plant Inspection Conference, Atlanta, 1999.
5. J. Foulds, R. Carnahan, Examination of Sabine 2 Hot Reheat Pipe Seam Weld Cracking,Electric Power Research Institute, TR-107141, 1997.

293

ACOUSTIC EMISSION AND X-RAY TOMOGRAPHY IMAGING OFSHEAR FRACTURE FORMATION IN CONCRETE
TATYANA KATSAGA and R. PAUL YOUNG
Lassonde Institute and Department of Civil Engineering, University of Toronto, Canada

Abstract
This paper describes how different aspects of fracture formation in concrete can be investigated using a combination of acoustic emission (AE) techniques and high resolution computed
tomography (CT). AE and elastic wave velocities were measured using the Hyperion GigaRAM
AE Recorder, a device developed for the ultra-detailed investigation of brittle fracture and failureof rock and rock-like materials. AE analysis includes studying complex spatial and temporal
fracture development during the slow quasi-static fracture process. Predominant microcrackmechanisms were analyzed at different stages of fracture formation. CT images were used to investigate the influence of concrete microstructure on fracture topography. Combined AE and CT
images revealed different aspects of fracture development, thus expanding our understanding of
AE events and their mechanisms. These images show how coarse aggregates influence fracture
nucleation and development, as well as event sequences and mechanisms during aggregate fracturing.
Keywords: X-ray computed tomography (CT), concrete, aggregates, shear fracture.
Introduction
Fractures in concrete are directly influenced by the complexity of the concrete’s microstructure, from nucleation and propagation to surface roughness formation. To study this influence,researchers used different techniques and approaches. One approach is to measure and analyze
the fractal dimension of concrete fracture surfaces, depending on the kind of aggregate (Brandt etal., 1993) or the maximum aggregate size (Issa et al., 2003). A correlation has found between
fractal dimension and fracture toughness. Another approach involves non-destructive 3D studies
using AE techniques (Mihashi et al., 1991; Landis et al., 1995; Chen et al., 2004). These studies
show how concrete microstructure influences the fracture process zone, fracture energy, andfracture toughness. In addition, advanced AE techniques can be used to define AE source
mechanisms (Zang et al., 1998; Ohtsu et al., 1998; Grosse et al., 2003).
The detailed, nondestructive investigation of cracks inside materials has become possible
with the development of X-ray Computed Tomography (CT) techniques. For example, Landis etal. (2000) calculated fracture energy using the actual surface area of internal cracks using CT
data collected during loading. Chen et al. (2004) compared cracking paths, obtained by X-ray
inspection, in concrete with different maximum aggregate sizes; Chen’s study discovered a correlation between the width of the crack zone and the increasing of the maximum aggregate size.
Young et al. (2007) combined CT and AE imagery to relate physical features within the sample
to AE locations and phases of micro-scale damage.
In order to gain improved insight into the 3D micro-mechanics of failure in concrete, thisstudy combined AE techniques with high-resolution X-ray CT techniques. The recorded AE
event source locations were related to high resolution X-ray CT images obtained from core
J. Acoustic Emission, 25 (2007)

294© 2007 Acoustic Emission Groupsamples extracted from the beam after testing. Furthermore, various aspects of failure were studied by analyzing the AE mechanisms associated with the different aspects of fracture propagation through the concrete components.
Experimental Procedure and Data Processing
This experiment has been a part of an extensive experimental program conducted at the University of Toronto (Sherwood et al., 2007; Katsaga et al., 2008). Using the Baldwin test frame, alarge, lightly reinforced, normal-strength concrete beam with large coarse aggregates (maximum
size = 55 mm) was loaded to shear failure in a three-point bending test. The specimen, designed
to be shear-critical, was 9000-mm long, 1510-mm tall, 300-mm wide, and was supported on rollers spaced at 8100 mm apart.
AE setup
One half of the specimen was instrumented with AE sensors to record the acoustic emissions
induced from microcracking during loading. The concentrated array of 24 sensors, 16 receivers(V103, Panametrics), and 8 pulsers (R15, PAC) was designed to investigate diagonal shear fracture development before failure and was installed on the surface of the beam using special holders screwed into the concrete (Fig. 1b). The monitoring parameters were selected based on the
results of pencil-lead break or artificial shot (ultrasonic pulse) tests: 60 dB pre-amplification and
10 MHz sampling frequency.
Signals from the sensors were recorded using a sixteen-channel recording system called theGiga Recorder (Fig. 1a). Typically, AE systems experience downtime when they record discrete
AE events because the transfer of events from their random access memory (RAM) results inrecording blackouts. The Giga Recorder has the ability to acquire an ultrasonic waveform continuously at high sampling rates (up to 20 MHz) and at 14-bit resolution on a circular 40 GB
RAM buffer; this buffer is then locked and downloaded to disk following the critical stage of anexperiment (i.e. failure of a sample). Our experiment featured a sampling rate of 10 MHz, whichtranslates into a 2-minute continuous waveform (Fig. 1c). In addition to the continuous waveform, the Giga Recorder also records discrete triggered AE signals (Fig. 1d) during the entiretyof the experiment at a rate of 15 events per second. More detailed discussion of the Giga Recorder can be found in Thompson et al. (2006).
AE data processing
InSite (ASC, 2008) software was employed to analyze AE events, continuous waveforms andvelocity data. Before AE data processing, P-wave velocities were defined, assuming straight raypaths between pulsers and receivers. Wave velocities transmitted through the concrete beam before loading did not show any preferred direction in velocity variations; therefore, a homogeneous-isotropic velocity structure was assumed to exist throughout the concrete volume. A combination of Simplex and Geiger methods was used for locating AE events, a combination that provides faster solutions with improved error analysis.
One of the advantages of recording continuous waveforms, is the possibility of extracting
discrete events by applying new settings. The program SeisAcq (ESG) was used to process thecontinuous records and to “re-harvest” triggered data from them. An AE event was triggered if
signals on five channels exceeded the 80-mV threshold. By contrast, initial experimental settings
used seven channels and a 100-mV threshold. None of the discrete events extracted from these295

Fig. 1 a) Schematic diagram of acquisition system and processing software; b) Beam with arrayof sensors after testing (front view); c) continuous waveform; d) triggered event on 16 channels;e) AE source locations.
continuous waveforms were lost because the transfer of events from RAM involved no downtime. The detailed logic diagram of our equipment and software use is presented on Fig. 1.
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The relative comparison of the size of AE events was done based on their instrument magnitude, which was calculated based on equation (1). An instrument magnitude is calculated for the
event by averaging over the instruments used in the source location,
M = log10 (r  WRMS )(1)
where r is the source to receiver distance and WRMS is the peak signal amplitude on the RMS
waveform (ASC, 2008).

Fig. 2 a) 300-mm cores extracted from the beam: the jagged black line identifies fracturing thathas occurred in the cores; b) CT images for two cores (section view); c) reconstructed image
across the core length perpendicular the fracture.
To analyze the microcrack mechanisms and their evolution during shear fracture formation,
P-wave first-motion polarities were defined and analyzed using a first-motion polarity method(Zang et al., 1998),
297

pol =( )1 ksign A i
k i =1(2)where A is the first-motion amplitude and k is the number of sensors. In this study, positive polarity represents compressional pulses initiated by tensile fractures. Events are considered as tensile if pol is between 0.25 and 1, shear if pol is between -0.25 and 0.25, and crushing (implosion)
if pol is between -0.25 and -1.
Computed tomography imaging
After the failure of the beam, four core samples containing the fracture that preceded the final
failure were extracted from the beam (Fig. 2). The location of the coring was defined by studying
AE event locations. Two samples were sent to the University of Texas for high-resolution X-ray
Computed Tomography (Ketcham et al., 2001). The samples were imaged separately using ahigh energy scanning system designed for scanning large samples. Horizontal CT images weretaken at 1 mm intervals across the sample diameter, with a resolution of 1024 x 1024 pixels (6.6
pixels per 1 mm) (Fig. 2b). These images were reconstructed to provide a view across the length
perpendicular to the fracture plane (Fig. 2c).
Through the consequential CT sections, the 3D microstructure of the material can be nondestructively reconstructed. Different densities of concrete matrix and aggregates as well as
strongly marked interface between them cause variation in X-ray attenuation, giving a clear picture of the microstructure. The density of fractures and air voids contrasts sharply with the rest of
material, thus making them easily distinguishable on CT images. In this work, CT images wereused for two purposes: to analyze the influence of the microstructure on fracture propagation andto reconstruct the temporal and spatial scenario of fracture formation using combined CT and AE
data.
Combination of AE and CT data
The combination of CT and AE data is what the custom AutoCAD built-in dialog-based Visual Basic Application (VBA) was designed to do (Fig. 3). This application allows us to study the
CT scan images in precise relation to the AE locations, using the advanced viewing and graphical tools of AutoCAD. VBA application loads data from an event parameter text file created by
InSite and processes it in AutoCAD. The main features of this application are event filtering anddrawing properties. Events can be differentiated by size, shape and color based on calculated instrument magnitude and time of occurrence.
CT images were imported in AutoCAD and placed in separate layers at their exact positions(Fig. 4a). Then fractures were traced and differentiated by color: black represented fractures that
propagated through concrete matrix, blue through matrix-aggregate interface zones, and red
through an aggregate (Fig. 4b). All of these traced cracks allowed us to reconstruct the complicated 3D topography of the fracture surface inside the material (Fig. 4c). As the next step, AE
signals were imported (Fig. 4d) and filtered by ±7mm slices from the plane of the images andwere superimposed onto these images (Fig. 4e).
Image analysis
After the cracks on the CT images were traced, they were separated into three different
groups: matrix cracks, aggregate cracks, and interfacial cracks. The cracks were then converted
into bitmap images and processed, using an automated image-processing program (Launeau etal., 1996) to investigate the influence of the concrete’s microstructure on fracture propagation.
This program is based on the intercept method, which involves scanning a drawing with a set of298

Fig. 3. Schema of VB Application for combining CT images and AE events in AutoCAD.
parallel lines at specified angular intervals and counting the number of intercepts with the cracklines. The results of the intercept counts were used to build a rose of directions for each group of
cracks and to analyze statistical data.
299

Fig. 4 Combination of AE and CT data in AutoCAD: a) loading images; b) crack tracing; c) 3D
fracture surface topography; d) loading AE events; e) combined CT and AE image.
Results and Discussion
Crack topography
Images of three groups of cracks are presented in Fig. 5a. The results of intercept counting
showed that, for this slow fracture propagation, interface cracks take 45.4% of the overall crack
length, matrix cracks –43.6% and aggregate cracks –11 %. Direction Rose diagrams extracted
from the analysis showed that aggregate cracks and matrix cracks have the same main crack direction (15º from 0) and almost the same maximum intercept directions (3.019 and 2.944). However, the situation is different for the interface crack: the direction of the main crack is almost thesame (14º), but has a number of secondary intercept directions (Fig. 5b). These secondary300

Fig. 5. Microstructural observations: a) crack path divided into matrix cracks, aggregate cracks
and interfacial cracks; b) direction rose diagrams for each group.
directions show that interface cracks are responsible for the fracture surface’s high degree ofroughness.
Studying CT images revealed that large coarse aggregates have the largest impact on a fracture path. Figure 6a illustrates that large aggregates change the fracture direction locally and thefracture travels along one side of the aggregate. In this case, the fracture surface’s roughness depends on the roughness of the aggregate. It was found that if a long aggregate crosses a fracture,
the aggregate is more likely to be fractured (Fig. 6b). This phenomenon could be explained by
the fact that in this case the fracture spends more energy traveling around the aggregate thanfracturing it. Figure 6c shows the general cases of the fracture traveling around small and medium aggregates along aggregate-matrix interfaces. Aggregates of this size range thus control thefracture roughness in general. Therefore, by controlling the size and the shape of coarse
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Fig. 6. Microstructural observations: a) large aggregates change fracture path; b) cleavage of thelong aggregates; c) roughness formation through smaller aggregates.
aggregates, it is possible to create the rougher fracture surfaces necessary to improve aggregateinterlock, an effect that plays an extremely important role in the transferring of shear stresses(Sherwood et al., 2007).
Acoustic Emissions
AE source locations, divided into time periods A to E, are presented from the top point ofview in Fig. 7. Events for each time period are represented by different colors. Symbol sizes are
scaled according to their event magnitude. Periods A, B, and 36 seconds of period C show triggered AE events, while the rest of periods C, D and E show AE events extracted from continuous
waveforms. AE locations exhibited non-uniform event distribution along the fracture surface
width. During period A, five isolated events occurred in the space ahead of the fracture front.
During period B, several events nucleated near the front surface of the beam. Period C showed
AE event development in two directions: forward and sideways, across the width of the beam ina narrow band leaving uncracked concrete between the band and the primary fracture front. During period D, the sideways fracture connected with the primary fracture front and propagated a
few centimeters forward. Overall, 121 events were located. At the end of period D, microcracking was observed throughout the region of eventual fracture surface. Period E showed 120 eventsoccurring, due to the shearing of the cracked surfaces at the fracture.
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Fig. 7 AE source locations for different periods of fracture propagation and an example of a CT
image that represent a section.
AE mechanisms, obtained using first-motion polarities, were investigated for each of theshown periods. The events during Period A, which signify fracture nucleation, are dominantly
tensile (80 %). The number of tensile events decreases gradually with the fracture development(from 80 % to 25 %). The number of shear events stays almost the same for periods B, C and D(32-33 %) and increases dramatically during period E (66 %). It was also found that the shear
events released large amount of energy and produced high magnitude events compared to tensile
events. The implosion events were similar in magnitude to shear events. However, they were notpresented in the first two periods and were rare during the final periods of the fracture formation(8-9 %), except for the period D (19 %). A close analysis of the implosion events is presented in
the next section.
AE and Crack Topography
AE and CT techniques both carry significant amounts of information about different aspectsof fracture in material. CT techniques show the connection between crack topography and material microstructure, whereas AE techniques reveal its spatial and temporal characteristics. Combining both of these techniques creates a unique opportunity to investigate the specifics of fracture formation and propagation. This investigation explained a number of important observations, including event sequences and the nature of the event mechanisms. Figure 8 shows a 70mm section and corresponding AE events filtered by ±7 mm from the plane of this section. Thissection was chosen because, at this level, the first event nucleation occurred during period B(Fig. 7). A combined AE and CT image (Fig. 8) shows that the first events during nucleation
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Fig. 8 CT image at 70-mm section (see its poison on Fig. 7) and corresponding AE events andtheir mechanisms: - tensile events;- shear events.
were initiated by fracturing at the edge of an aggregate. First motion polarity analysis showedthat these events were actually a single tensile and shear event. At this level, the rest of theevents during this period were tensile events that occurred at the interface of aggregates. During
period C, a number of events the majority of which were also tensile events at the interface oflarge and small aggregates occurred along the fracture path. One large shear event appeared next
to the tensile events from stage B. During period D, the character of events changed: most werelarge magnitude shear events which occurred at new positions and next to events from the previous period. Events from stage E were only shear events distributed along the fracture path. We
can, therefore, assume that stage E shows the shearing of fracture surfaces that were formed before stage E.
The combination of CT images and AE events revealed why the spatial development of thefracture during period C was not uniform (Fig. 7). Large particles of coarse aggregate were
found on both sides of the AE band. Because these large aggregates strengthened material locally, the fracture was forced to propagate only between aggregates.
In the section “Crack topography”, it was shown that 11% of the fracture path was throughaggregates. It was also illustrated that some of these aggregates were elongated. However, analyzing CT images, we found a number of equiaxed aggregates fractured. Studying AE events related to these fractured aggregates showed that most of them were fractured during period D,
which can be explained by the accumulation of higher stresses during this period. Figure 9a
demonstrates a sequence of events and their mechanisms during the fracturing of an aggregate. A
smaller tensile event (marked as 1) initiated the fracturing and three large magnitude shear events
concluded the process. Only one normal-shaped aggregate was found fractured during Stage C(Fig. 9b). This aggregate was located at the level 195 mm (Fig. 7). As discussed above, during
Stage C events were propagating non-uniformly in a narrow band between large aggregates. The
presence of these aggregates caused a high local stress redistribution that forced the fracture topropagate through the aggregate located in middle. The sequence of events during the fracturingof this aggregate is similar to the previous case: a small tensile event (Fig. 9b, 1) is followed by alarge shear event (Fig. 9b, 3). However, in this case, a large implosion event (Fig. 9b, 4) occurred304

Fig. 9 CT images and corresponding AE events, their sequence and their mechanisms for different situations: - tensile events;- shear events;- implosion events.
between these two events. The analysis of further fractured aggregates and related AE events revealed more implosion events involved in aggregate fracturing (Figs. 9d, 9e, 9f). The fact thatthe most aggregate fracturing occurred during period D explains the large number of implosion
events at this period, compared to the other periods. Some implosion events during period D
were found in the concrete matrix. For example, in Fig. 9g, three large implosion events (Fig. 9g,
5-7) are shown in the space marked by the tensile events that occurred earlier (Fig. 9g, 1-3).
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Conclusions
The combination of AE and CT imaging provides us with a unique opportunity to study AE
events in relation to the microstructure of material.
High-resolution CT images in our study resulted in a detailed picture of the concrete microstructure, including the aggregates, cracks, and air voids. The influence of the concrete microstructure on the topography of the fracture surface was analyzed using these images. It was foundthat large aggregates can change the direction of a fracture path locally and that the fractureroughness is controlled by interface cracks. It is, therefore, possible to optimize aggregate interlock by controlling the size and shape of coarse aggregates.
The AE event locations revealed a complex, non-uniform spatial and temporal fracture development in the concrete with the large aggregates at the slow quasi-static stage of fracture
propagation. The early stages of fracture formation were associated mainly with tensile events,
the number of which decreased with fracture development. Finally, the number of shear events
became dominant (66%) due to the shearing of the cracked surfaces. Therefore, studying the predominant mechanisms during monitoring or inspection can be helpful in defining the stage offracture development.
As expected, the cracks on the CT images showed a close correlation with AE events. Analyzing the temporal and spatial evolution of AE events in relation to the CT images allows us toreconstruct the failure sequence precisely. Coarse aggregates are shown to play an important rolein fracture nucleation and development: aggregate fracturing is found at the place of fracture nucleation and the non-uniform development of events is explained by the influence of large piecesof aggregates. A close analysis of combined AE and CT images showed that the events at the
new location were mainly tensile events and subsequent events at the nearest position were shear
events. A similar sequence of events is found when the fracture propagates through aggregates.
Additionally, a number of implosion events were involved in the process of aggregate fracturing.
It was revealed that aggregates were fractured mostly during the final period of fracture surface
formation, which explained the large portion of implosion events compared to the other periods.
The overall results of the work show that the combination of AE and CT imaging provides anextremely effective tool for the investigation of the process of fracture formation in materials
with complicated microstructure.
Acknowledgments
We are grateful to M.P. Collins and E.G. Sherwood for providing an experimental opportunity to apply AE techniques during their experimental program, in which the effectiveness ofshear design methods was evaluated for large, lightly-reinforced concrete beams. We also would
like to thank D. Collins, P. Benson, B. Thompson and F. Nasseri for helpful discussions and suggestions. This work was made possible through an Ontario Graduate Scholarship to Katsaga andKeck Chair research funds to Young.
References
Applied Seismology Consultants. http://www.seismology.org/insite.aspx. Cited Jan 14, 2008.

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B. Chen and J. Liu, Constr. Build. Mater., 18:8, 2004, 585-590.
C.U. Grosse, H.W. Reinhardt, F. Finck, J. Mater. Civil Eng., 15:3, 2003, 274-279.
M.A. Issa, M.A. Issa, Md. S. Islam, A. Chudnovsky, Eng. Fract. Mech., 70, 2003, 125-137.
T. Katsaga, R.P. Young, Int. J. Fract., 148 (1), 2007, 29-45. See also Int. J. Fract. DOI
10.1007/s10704-008-9174-x, 2008.
R.A. Ketcham and W.D. Carlson, Computers & Geosciences, 27, 2001, 381-400.
E.N. Landis and S.P. Shah, Advn. Cem. Bas. Mat., 2:3, 1995, 105-118.
E.N. Landis and E.N. Nagy, Engineering Fracture Mechanics, 65, 2000, 223-234.
P. Launeau, and P.-Y.F. Robin, Tectonophysics, 267, 1996, 91–119.
H. Mihashi, N. Nomura, S. Niiseki, Cement Concrete Res., 21, 1991, 737-744.
M. Ohtsu, T. Okamoto, S. Yuyama, ACI Struct. J., 95:2, 1998, 87-95.
E.G. Sherwood, E.C. Bentz, M.P. Collins, ACI Struct. J., 104:2, 2007, 180-190.
B.D. Thompson, R.P. Young, D.A. Lockner, Pure Appl. Geophys., 163:5-6, 2006, 995-1019.
R.P. Young and B.D. Thompson, Proc. 11th International Congress on Rock Mechanics, Lisbon,
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A. Zang, F.C. Wagner, S. Stanchits, G. Dresen, R. Andresen, M.A. Haidekker, Geophis. J. Int.,
135, 1998, 1113-1130.

307

GLOBAL MONITORING OF CONCRETE BRIDGE USING ACOUSTICEMISSION
T. SHIOTANI, D. G. AGGELIS1 and O. MAKISHIMA2
Department of Urban Management, Graduate School of Engineering, Kyoto University, C1-2236, Kyoto-Daigaku-Katsura, Nishikyo, Kyoto 615-8540, Japan; 1Research Institute of Technology, Tobishima Corporation, 5472 Kimagase, Noda, Chiba, 270-0222, Japan;
2
Civil Engineering Headquarters, Tobishima Corporation, Sanbancho, Chiyoda,
Tokyo, 102-8332, Japan

Abstract
Global monitoring of civil structures is a demanding challenge for engineers. Acoustic emission (AE) is one of the techniques that have the potential to inspect large volumes with transducers placed in strategic locations of the structure. In this paper, the AE technique is used to characterize the structural condition of a concrete bridge. The evaluation of AE activity leads to information about any specific part of the structure that requires attention. Consequently, more detailed examination can be conducted once the target area is selected. In this study, surface wave
investigation was subsequently performed to detail the condition of the target area.
Keywords: Concrete structures, damage assessment, global monitoring, surface wave velocity.
Introduction
The deterioration of civil infrastructure worldwide calls for effective methods for damage
evaluation. One of them, AE monitoring technique, uses signals generated within the structure,
which are due to crack growth under stress, as well as secondary emissions due to friction of
crack interfaces. This unique monitoring technique parameterizes the fracture/failure process,and distinguishes it from other nondestructive tests. This is the only one capable of real-time
mapping of fracture processes. In addition to real-time source identification from the acquired
AE signals, the energy level or “magnitude” of the detected signals can be evaluated, and provides immediate evidence of the degree of damage. A particular engineering advantage of the
AE technique is its efficiency for global monitoring since a large and complex structure can bemonitored with a limited number of sensors. Consequently, the most crucial part of the structure
can be targeted with a more detailed AE monitoring for quantification of AE indices [1-4], or
using other suitable techniques [5]. Results obtained from the AE testing depend highly on many
external parameters such as the applied load and loading rate, the properties of the material andthe type of structure. These factors restrict the development of comprehensively applicable tools.
Additionally, due to the complex composition of most civil structures, AE waveforms depend on
their propagation path from the source to the sensors [6]. However, in any case, valuable information can be extracted concerning which part of the structure has sustained the most severe deterioration.
In the specific case presented herein, a 45-m bridge span is under examination. Preliminary
investigations of surface-crack observation and physical tests of excavated cores did not reveal
any extensive damage. The cores can characterize only the area where they were extracted andthe surface observation cannot reveal internal damage. Therefore, further monitoring was
J. Acoustic Emission, 25 (2007)

308© 2007 Acoustic Emission Groupconducted with AE technique. The AE testing was performed by moving a heavy vehicle overthe bridge. Based on the AE activity the part of structure more likely to exhibit higher degree ofdamage than the other areas in the longitudinal direction was selected for the detailed investigation. A similar application on a much smaller scale was reported in [2]. A surface ultrasonic examination, which is indicative of the quality [7, 8], followed in order to investigate the pulse velocity of concrete at the area of interest.
Experimental Procedure
For the AE monitoring, 28 sensors were attached in all to the bottom surface of the bridge using a wax. They were placed on the longitudinal axis of the bridge with a separation of 1.4 m asshown in Fig. 1. Specifically, the sensitive AE sensors to concrete structures, R6 of PAC, wereused. These sensors exhibit nominally the maximum sensitivity at 60 kHz and are widely used
for concrete monitoring. The detected AE signals were pre-amplified by 40 dB and acquired by
two synchronized data acquisition systems, namely a 16-channel DiSP and a 12-channel Mistrasof PAC. Strain gauges were also placed in three locations on the top surface of the bridge, as
shown in Fig. 1.

Fig. 1. Representation of the crane passing over the bridge and sensor location.
Acoustic Emission Activity
Damage indices
The interpretation of the detected AE activity is not always easy. This is the reason why different indices have been suggested and applied for the purpose of damage quantification. As
stated earlier, when a material or structure is stressed, AE is produced. Additionally, the behavior
during unloading is also crucial. In case the material is intact (or the applied load is low), the AE
activity during unloading is of low intensity. For damaged material, the emission continues evenduring unloading. The number of AE hits during unloading divided by the number of hits during
the whole cycle is defined as the Calm ratio and values near zero indicate intact material condition [2, 4, 9].
Another index comes from the analysis of the amplitude distribution of AE events, or socalled the improved b-value analysis [1, 4]. Although a large-scale fracture in general corresponds to large AE peak amplitude, the use of the amplitude solely can be misleading. This is309

because the accumulated damage increases the materials’ attenuation rate due to scattering at thecracks. Therefore, even a high amplitude signal will be severely attenuated before being recordedby the sensors. In this respect, the amplitudes are studied through their cumulative distributionthat uniquely changes as the damage is accumulated. Specifically, the gradient of the distribution
is calculated. With the evolution of damage, this slope decreases, meaning simply that the ratioof the large energy AE events to that of the small relatively increases in the total population of
AE events. It has been confirmed that at the moments of extensive cracking, the Ib-value exhibits
severe drops [10-14].
AE monitoring results
The load for the AE monitoring was applied by a 20-ton crane vehicle, which passed threetimes over the bridge with a constant speed of approximately 0.5 m/s, as seen in Fig. 1. As thecrane moved over the bridge, the strain and stress fields changed. The compressive strain measured on the top surface of the bridge at the mid-span can be seen in Fig. 2. The maximum strain
was recorded at 88 s, when the vehicle was in the middle of the span, suggesting the highest tensile stress at the bottom layer of the structure. In the same figure, the cumulative number of AE
hits recorded by all the sensors is depicted for one passage. It can be seen that the rate of AE hits
was more intensive before the crane reached the center of the bridge at 88 s. Up to that moment,
more than 70% of the total number of hits was recorded, implying that more active sources werelocated in the first half of the bridge.
4500

-16
StrainNumber of Hits

3500

Hits recordedby all the
sensors

3000

-14
-12
-10

2500
-8
2000

Hits recordedat the center(mult. by 5)

1500
1000

-6

Strain (_)

4000

-4
-2

500
0

0
0

50

100
Time (s)150

Fig. 2. Time history of strain at the center and cumulative AE hits during crane passage.
As stated, in order to calculate the Calm ratio, the AE activity should be correlated with ameasured mechanical parameter. In this case, this was conducted using the measured strain at the
middle point and the AE activity of the center part, recorded by the two sensors placed closest to
the center (13 and 14 of Fig. 1). The activity of these sensors and AE hits of all sensors combined are plotted in Fig. 2. For clarity, the middle sensor hits are multiplied by a factor of 5. The
hits of the middle sensors started at 74.5 s as the crane was approaching, and the last hit was recorded 10 s after the crane had passed over the bridge center, showing the intensive AE activity
even during unloading. The number of hits for three different trips of the crane and the resulting
Calm ratios are presented in Table 1. The Calm ratios concerning the center part of the bridge
range from 0.3 to 0.45 for any individual passage of the crane, indicating serious damage according to past studies [2, 11, 12, 16, 17].
310

After location of the events, interesting conclusions can be drawn about the attenuation of thestructure. In Fig. 3(a), the amplitude of the hits of all events is depicted vs. the distance from thesource. The average first hit stands at amplitude of 54 dB. In Fig. 3(b), the linear fits to the amplitude of each individual event are plotted. Attenuation was calculated from the slope of eachline. Averaging of the slopes of all the events results in -7.02 dB/m, which is representative forthe attenuation of the whole structure. It is seen that hits of the weakest events still propagate 1.5
m before being reduced below the threshold level (40 dB). Therefore, they are recorded by atleast 2 sensors. This shows that the separation of the sensors in this case allows source location.
In general for the average amplitude of 54 dB at the source, 2 m for the sensor separation would
provide a reasonable detection in this bridge. In any case, it is a crucial parameter that should beseriously taken into consideration since, in monitoring of most large structures, compromisesmust be made between the available number of sensors, time restrictions for measurement preparation and the desirable degree of detail of examination. Attenuation is a key parameter to makean adequate decision.
Concerning the Calm ratio, mentioned above, taking into account the measured attenuationand the fact that the Calm ratio is based on the results of two neighboring sensors (with separation of 1.5 m), the events should originate within a span of 4 to 5 m in the middle of the bridge.
Thus, conclusions about material degradation based in this Calm ratio concern this zone.

Fig. 3. (a) Hit amplitude vs. distance from event source, (b) individual attenuation slope for different events.
Table 1. Number of AE events for different stages of loading and Calm ratios.
Trip
1
3
5

Loading
175
409
295

Unloading
146
179
208

Calm ratio
0.455
0.304
0.414

Ib-value analysis
In Fig. 4, the Ib-values based on the AE events located at different zones of the bridge areplotted for the three different passages of the crane. The span was divided into 12 zones of 3 m
311

each, in order to allow the number of events for the Ib-value calculation. Specifically, the total
number of events to be considered for the calculation of the Ib-value should be above 50 [1, 1014]. The number of events for the same zones is also shown in the chart. It is evident that someareas of the structure exhibited larger number of events than others as seen in Fig. 4 and the Ibvalue is calculated for the zones only exhibiting more than 50 events. Focusing on the Ib-values,
it is seen that they vary between 0.05 and 0.13. According to established correlations, values
above 0.2 imply the intact condition, between 0.1 and 0.2 suggests the moderate damage and it is
becoming more intense as the Ib-value decreases below 0.1 [1, 2, 12]. This shows that a largepart of the structure is deteriorated. While the limited AE activity of the rest does not allow theevaluation of this parameter, there were not many active sources in that area.

Fig. 4. Ib-values for three different trips of the crane and indicative AE events for the first trip.
Velocity measurements
In order to make a more detailed examination of the active area of AE activity, wave velocity
measurements were made. Velocity has been established as an indicator of concrete quality formany decades and it is accepted that velocity above 4000 m/s indicates high quality, while below
3000 m/s suggests poor quality [7, 17].
For the velocity measurement, nine AE sensors were used in an arrangement of three parallel
arrays of three. The separation distance was 1.5 m, resulting in an examined area of 3 m by 3 m,
see Fig. 5. The excitation was conducted by pencil-lead break near the location of each transducer. Therefore, each time, one sensor was used as trigger for the acquisition and eight as receivers. In this way, a number of intersected paths were examined, and the results can be considered more representative of the area and more reliable than single measurement between two
points. The velocity was measured by the time of the first detectable disturbance of each waveform, which corresponds to the onset of longitudinal waves.
312

The transit times of the individual paths, along with the sensor positions were supplied to asuitable tomography program [18]. This way the visualization of the velocity structure was obtained and the information of which parts of the surface area exhibit lower velocity than others
was obtained as shown in Fig. 5. From this figure, considerable discrepancies of the wave velocity were found within the area of 9 m2. Specifically, a zone approximately in the center of theselected area exhibited velocities close 2500 m/s, while other areas had velocity higher than 4000
m/s.

Fig. 5. Velocity structure of the bottom surface of the concrete bridge. The positions of the transducers are indicated by circle and the examined wave paths are drawn by dashed lines.
It is noted that no visual defect was observed on the surface. Therefore, the low velocity zone
is attributed to a subsurface defect. The depth cannot be easily determined though. Concerning
Rayleigh waves, it is accepted that the penetration depth is approximately similar to the wavelength [19]. In this case, however, the first arrival used to measure velocity corresponds to thelongitudinal wave, which is in any case faster than Rayleigh or shear waves. Therefore, it is notstraightforward how deep is the surface layer characterized by this velocity. Concerning
Rayleigh propagation, typical velocities for the bottom part of Fig. 5 were around 2500 m/s, corresponding to the longitudinal velocity certainly higher than 4000 m/s. Although this implies
good quality, the determination of Rayleigh velocity was not always possible due to severe attenuation and distortion of the waveform, especially for paths at the top of Fig. 5. This was because there was no characteristic point to use as reference for Rayleigh wave measurement [20].
This again shows that the continuous path was disrupted by a discontinuity. Using the Rayleighvelocity of 2500 m/s and the major frequency component of 150 kHz the wavelength is calculated to approximately 17 mm. Therefore, since the Rayleigh component was not visible at someareas, this should be due to a weak material zone (discontinuity) that extends very close to313

the surface (even closer than 2 cm), because if it was deeper the Rayleigh would not be influenced. It is mentioned that the frequency of 150 kHz is measured by waveforms recorded 1.5 m
away from the excitation. Close to the excitation the frequencies are higher, while for propagation of 3 m the major frequency component is just below 50 kHz. This complicates the quantification of the penetration depth, since the frequency content is not constant but is downshiftedwith propagation due to concrete damping. The characterization depth using surface wave examination (including Rayleigh and longitudinal components) needs further study, which is currently undertaken.
As to the AE events observed, concrete cracks, delaminations of different layers (e.g. asphalt
on concrete) or friction between the tendon ducts and matrix concrete are possible origins. Follow-up investigations focusing on this weak area will clarify the source, while this sequential
investigation, which started with AE activity and followed by detailed measurements of ultrasonic velocity is useful in characterizing the quality of large-scale concrete structures, leading tocontribute to the rehabilitation program of aging infrastructures.
Conclusions
In this paper, the suitability of acoustic emission to monitor large concrete structures is presented. The AE technique was initially used to select the most deteriorated area based on thenumber of AE events and the values of quantification indices like the Calm ratio and Ib-value.
The subsequently conducted ultrasonic examination exhibited very low velocities confirmingthat the area indicated by AE activity was actually deteriorated. This shows the potential of AE
testing as a global monitoring for examination of large volumes using a limited number of sensors. Even if AE indices or parameters cannot be directly correlated with the degree of damage,they suggest which part of the structure needs further and detailed investigations. Subsequently,
wave velocity measurements were conducted allowing a more general evaluation through thereported correlations between velocity and concrete quality. It is suggested that the combinationof such stress wave techniques as AE and surface wave examination can assess the degree ofdamage of large civil structures that so far has been difficult to attain.
References
1)

T. Shiotani, K. Fujii, T. Aoki, K. Amou, Evaluation of progressive failure using AE
sources and improved b-value on slope model tests, Prog. Acoust. Emiss VII, (1994),
JSNDI, Tokyo, pp. 529-534.

2)

M. Ohtsu, M. Uchida, T. Okamoto, S. Yuyama, Damage assessment of reinforced concretebeams qualified by acoustic emission. ACI Structural Journal, 99(4), (2002), 411-417.

3)

C. Grosse, H. Reinhardt, T. Dahm. Localization and classification of fracture types in concrete with quantitative acoustic emission measurement techniques. NDT&E Int, 30(4),(1997), 223–230.

4)

T. Shiotani, Evaluation of long-term stability for rock slope by means of acoustic emission
technique, NDT&E Int, 39(3), (2006), 217-228.

5)

V. M. Malhotra, N. J. Carino (Eds.), CRC Handbook on Nondestructive Testing of Concrete, (1991), CRC Press, Florida.

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6)

B. Schechinger, T. Vogel, Acoustic emission for monitoring a reinforced concrete beam
subject to four-point-bending, Construction and Building Materials, 21(3), (2007), 483-490.

7)

T. R. Naik, V. M. Malhotra, The ultrasonic pulse velocity method, in: Malhotra, V. M.,
Carino N. J., (Eds.), CRC Handbook on Nondestructive Testing of Concrete, (1991), CRC
Press, Florida.

8)

T. Gudra, B. Stawinski, Non-destructive characterization of concrete using surface waves,
NDT&E Int, 33, (2000), 1-6.

9)

S. Colombo, M. C. Forde, I. G. Main, M. Shigeishi, Predicting the ultimate bending capacity of concrete beams from the “relaxation ratio” analysis of AE signals, Construction and
Building Materials, 19, (2005), 746-754.

10) T. Shiotani, M. Ohtsu, K. Ikeda, Detection and evaluation of AE waves due to rock deformation, Construction and Building Materials, 15(5-6), (2001), 235-246.
11) T. Shiotani, Y. Nakanishi, X. Luo, H. Haya, T. Inaba, Damage evaluation for railway structures by means of acoustic emission, Key Engineering Materials, 270-273, (2004), 16221630.
12) T. Shiotani, Y. Nakanishi, X. Luo, H. Haya, Damage assessment in deteriorated railway
sub-structures using AE technique, Journal of Acoustic Emission, 22, (2004), 39-48.
13) J. H. Kurz, F. Finck, C. U. Grosse, H. W. Reinhardt, Stress drop and stress redistribution in
concrete quantified over time by the b-value analysis, Structural Health Monitoring, 5,(2006), 69-81.
14) S. Colombo, I. G. Main, M. C. Forde, Assessing Damage of Reinforced Concrete Beam
Using ‘‘b-value’’ Analysis of Acoustic Emission Signals, Journal of Materials in Civil Engineering-ASCE, 15(3), (2003), 280-286.
15) T. Shiotani, D. G. Aggelis, Damage quantification of aging concrete structures by means of
NDT, Proc. of Structural Faults and Repair-2006, June 13-15, Edinburgh, (in CD).
16) T. Shiotani, X. Luo, H. Haya, Damage diagnosis of railway concrete structures by means of
one-dimensional AE sources, Journal of Acoustic Emission, 24, (2006), 205-214.
17) M. F. Kaplan, The effects of age and water/cement ratio upon the relation between ultrasonic pulse velocity and compressive strength. Mag. Concr. Res., 11(32), (1959) 85– 92.
18) Y. Kobayashi, H. Shiojiri, T. Shiotani, Damage identification using seismic travel time tomography on the basis of evolutional wave velocity distribution model, Proc. Structural
Faults and Repair-2006, June 13-15, Edinburgh. (in CD).
19) D. G. Aggelis, T. Shiotani, “Repair evaluation of concrete cracks using surface andthrough-transmission wave measurements”, Cement and Concrete Composites, 29, (2007),
700-711.
20) L. Qixian, J.H. Bungey, “Using compression wave ultrasonic transducers to measure thevelocity of surface waves and hence the dynamic modulus of elasticity of concrete”. Construction and Building Materials, 10(4), (1996), 237-242.

315

DEMAND ON FLEXURAL TENSION STEEL REINFORCEMENTANCHORAGE ZONES IN FULL-SCALE BRIDGE BENT CAPSQUANTIFIED BY MEANS OF ACOUSTIC EMISSION
THOMAS SCHUMACHER, CHRISTOPHER HIGGINS,+STEVEN GLASER* and CHRISTIAN GROSSE
School of Civil and Construction Eng., Oregon State University, Portland, OR, USA;*
Dept. of Civil and Environmental Engineering, University of California, Berkeley, CA, USA;+
Non-Destructive Testing and Monitoring Techniques, Univ. of Stuttgart, Stuttgart, Germany

Abstract
This paper documents the development of an AE technique for monitoring and quantifying
the demand on anchorage zones of the flexural-tension steel reinforcement in full-scale bridge
bent caps. Bent caps are deep transverse bridge beams that support the longitudinal girders andtransfer loads into the columns. The horizontal flexural-tension reinforcement at the bottom of
the bent cap is anchored into the columns and acts as a crucial structural element. Pull-out or anchorage failure could lead to system collapse since this member is non-redundant. A new approach to monitor the column anchorage zone and to quantify the maximum demand of rebar
pull-outs by means of quantitative AE is presented and applied to test data on full-size bent column anchorage sub-assemblages.
Keywords: Reinforced concrete, bridge bent caps, column anchorage zone, structural healthmonitoring
Introduction
Large numbers of conventionally reinforced concrete deck-girder (RCDG) bridges were constructed during the federal highway system expansion of the 1950s. During this time period, research developments on anchorage of reinforcing bars resulted in rapid changes to design specifications and practice. Specifically, the geometric standardization of deformed reinforcing bars in
ASTM A305-50 resulted in higher allowable stresses with reduced detailing requirements. Failures later in the decade revealed that the contemporary design practice was inadequate. By theearly 1960s, design specifications were amended and at the same time, pre-stressed concrete began to supersede conventionally reinforced concrete for bridge construction.
One concern of the design found in existing bent caps is the detailing of the anchorage zones
in bent caps. The flexural-tension reinforcement at the bottom of a bent cap is anchored into thebent column. For many bridges, these rebars are straight (not hooked) and terminated so thatminimum concrete cover (cc) is provided at the tail. This is normally around cc = 38 mm. The
typical anchorage length provided in a 610-mm-square column is only around ld = 572 mm. The
development length (ld) required by the current AASHTO-LRFD (2004) specification for a bar
with a diameter of db = 36 mm (#11) is:
ld = 1.25  A b  f y

f c ' , but not less than 0.4  db  f y(1)

which results in ld = 1490 mm assuming Grade 60 (fy = 414 MPa) rebar steel and concrete compressive strength of fc’ = 28 MPa. This required development length is more than 2.5 times
J. Acoustic Emission, 25 (2007)316© 2007 Acoustic Emission Groupgreater than the available anchorage length (although Grade 40 (fy = 276 MPa) rebars were generally used in mid-20th century bridge construction).
Experimental Setup and Load Protocol
To better understand the behavior and structural mechanics during a rebar pull-out, reduced
bent column sections were designed, constructed, and loaded until failure [1]. Figure 1 illustrates
a test specimen with applied force P and the support reactions P/2. The force was applied withhydraulic cylinders supplied by a manually activated pump. Load cells measured the applied
force.
PP

l = 1830 mm

b = 610 mm

P/2

ld

h = 610 mm

P/2

y

y

x

z

Fig. 1 Elevation and side view of a test specimen for rebar pull-out tests.
The overall dimensions of the tested bent column sub-assemblages were l x b x h = 1830 x
610 x 610 mm. Each specimen had four Grade 60 steel reinforcing bars with diameter db = 36mm in the corners as well as one in the center for the actual pull-out. The embedment length forthe pull-out rebars were chosen as ld = 533 mm and ld = 305 mm for specimen 1A and 1C, respectively. Ties were Grade 60 db = 13 mm (#4), spaced at s = 203 mm.
Both specimens were loaded using load steps (LS) with increasing amplitude followed byunloading until failure was reached. A Vallen AMSY-5 system with eight channels was used tomonitor AE activity. For specimen 1A, eight broadband DECI SE1000-H sensors were used andfor specimen 1C eight resonant Vallen SE150-M sensors. According to the calibration sheets, thevariation of frequency response was 4 dB and 20 dB for the broadband and resonant sensors, respectively (for a frequency range between 40 and 250 kHz). For non-AE data acquisition, thesoftware DASYLab 8 was utilized. The deformation of the pull-out rebar at the top and bottom
ends was monitored during the experiment as well as support movements.
Figure 2 shows the applied force on the pull-out rebar and localized AE events for specimen
1A. Note that a filter (LUCY) was set to exclude inaccurate localization results (see next section
for description). The experiment ended after reaching a force of Pmax = 492 kN when the rebar
started to enter strain hardening. Most of the AE events were produced in load step five (indicated by arrow) when the first visible surface crack formed on the specimen.
In Fig. 3, the applied force on the pull-out rebar and recorded AE events for specimen 1C areshown. The experiment was discontinued after reaching a force of Pmax = 382 kN when the rebar
pulled out of the concrete. In load steps five and six, the majority of AE events were generated317

(indicated by arrows). Again, this corresponded well with the occurrence of major cracks on
the specimen surface.

Fig. 2 Applied force vs. localized AE events for specimen 1A.

Fig. 3 Applied force vs. localized AE events for specimen 1C.
3-D AE Event Localization
In a first step, 3-D AE event localization was performed. The arrival-time picking was improved by introducing a floating threshold dependent on the recorded background noise. A factor,
sometimes called crest factor, is multiplied by the background noise voltage to determine thethreshold. This crest factor was set to 8.0 and 13.0 for the broadband and the resonant sensors, respectively. The threshold updating interval was set to 1.0 second. Pencil-lead breaks were performed on the surface of one of the specimens to quantify localization errors and variability in results. It was found that including five to seven signals in the computation leads to the most accurate
localization results. For this study, only the first five arrival times within an AE event were included to maximize the number of localized AE events. This turned out to be helpful in later stagesof the experiment where developed cracks distorted stress wave paths and increased localization
errors. A comparison with 26 real AE events recorded from one load step was performed using PolarAE, a program developed at the University of Stuttgart, Germany. The average spatial difference of the results over all events was found to be xyz = 16 mm between the two programs. It was
concluded that the developed picking and localization options available with the commercial product used for this study was sufficiently accurate for this experiment and the specimen size. A filter
setting to eliminate inaccurate localization results was set to LUCY  51 mm. LUCY stands for318

location uncertainty and is the root-mean-square of the difference between calculated and observed
distances between source and sensor that is calculable when at least five arrival times are available.
It describes how well a calculated source position explains the observed arrival time differences.
Unfortunately, LUCY does not contain any directional information. A better representation of theuncertainty would be the principal standard deviations of the numerical least-squares solution.

Fig. 4 AE event localization results for specimen 1A.
319

Fig. 5 AE event localization results for specimen 1C.
The first author of this paper has implemented a localization algorithm in MatLab based on
Geiger’s method that plots 3-D error ellipsoids with principal standard deviations as axes foreach localization result. In future analyses, this algorithm will be used to enhance interpretationof localization results.
320

Figures 4 and 5 illustrate all localized AE events during the experiment. The green circles ()represent AE events that occurred during the load steps before the major failure (macro-) crack
formed, the yellow triangles () the AE events between that observation and the failure load
step, and the red squares () the AE events during the final load step when the experiment was
terminated due to rebar strain hardening and pull-out for specimen 1A and 1C, respectively. The
circles represent spatial clusters of AE events that occurred within a sphere with a diameter of 51
mm, which indicates localized activity in a certain area. The numbered yellow cylinders illustrate
the AE sensors mounted on the surface of the specimen.
It can be observed that there were fewer localized AE events for specimen 1A than 1C. The
reason was that one of the sensors for specimen 1A did not work properly and the utilized
broadband sensors are less sensitive than the resonant ones.
Indicated with a dashed line in Figs. 4 and 5 are the spatial filter settings used for the analysis
of AE event cloud front, to be described subsequently. Only AE events that were identified inside the cube were taken for further analysis. This volume was chosen as a cube with a side
length equal to the embedment length of the pull-out rebar, i.e. ld = 533 mm and ld = 305 mm forspecimen 1A and 1C, respectively and centered about the rebar.
AE Event Cloud Front Analysis
Many experiments on monitoring of AE activity during rebar pull-out experiments can befound in the literature [2-5] and both qualitative as well as quantitative AE evaluation methods
have been applied to the test data. This study intends to use AE event localization as a quantitative means to describe the demand on the pull-out rebar during a load test. The central idea isthat there should be an evolution of the location of the captured AE events while going to higher
load levels.
Bond stresses are assumed as uniform and their integration leads to a linearly increasing rebar stress over the embedment length. Based on this idealized bond stress distribution, it was expected that AE events are created when a certain stress threshold is crossed along the embedment
length (ld). As higher forces are applied to the pull-out rebar, the higher the stress gradient becomes, and AE events should therefore progress along the embedment length away from the freesurface of the specimen. The AE event cloud front was identified as the location of the upper
quartile (75%) of all localized AE events within one complete load step. A normal distribution of
AE events was assumed for statistical evaluation. The depth of progression for this cloud front
was then expressed as a percentage of the embedment length and given the variable name q.
Figures 6 and 7 illustrate the progression of the AE event cloud front for all load steps of thetwo specimens. The y-coordinate corresponds with the longitudinal orientation of the pull-out
rebar. Statistical values such as median and mean with 95 % confidence bands for localized AE
events are given as well as the AE event cloud front (thick dashed line). Some of the first load
steps were not included in the analysis because too few AE events occurred.
Conclusions and Future WorkA clear trend of progression of the AE event cloud front from one load step to the next can befound for specimen 1A, which supports the proposed hypothesis. For specimen 1C, the trend is lessclear. This could be due to the fact that only few localized AE events were created. Generally, after321

Fig. 6 AE event cloud analysis for load steps 3 through 7 of specimen 1A.

Fig. 7 AE event cloud analysis for load steps 2 through 9 of specimen 1C.
322

big cracks form, localization of AE events becomes more difficult due to distortion of the media.
Stress waves travel longer and more convoluted paths, which lead to bigger errors in the computed
source location. The maximum value for the progression of the AE cloud front was found to be q =75% for specimen 1A where the rebar did not pull-out (experiment discontinued after rebar enteredstrain hardening) and q = 96% for specimen 1C where the rebar pulled out of the concrete. It appears that this maximum value could be taken as an indication of the maximum rebar demand.
In a next step, the behavior of other rebar configurations (two and four rebars) with applied
normal forces (similar to a real bent cap) will be studied. The final goal will be to evaluate AE
data collected from full-scale bent cap experiments and use AE event localization to quantify anchorage demands at overall member failure.
References
1. C. C. Koester, M.S. Thesis, Oregon State University, 2007.

2. G. L. Balazs et al., Magazine of Concrete Research, 177, Dec. 1996, 311-320.
3. A. S. Kobayashi et al., Experimental Mechanics, 9, Sept. 1980, 301-308.
4. St. Koeppel, Dissertation ETH Zurich, Technical Report Nr. 272, Feb. 2002.
5. S. Weihe et al., Characterization of damage and failure during the pull-out of a reinforcement
bar by acoustic emission and numerical simulation, 1st International Conference on Damage
and Failure of Interfaces, Vienna 1997.

323

DAMAGE EVALUATION OF POST-TENSIONED CONCRETE
VIADUCT BY AE DURING PROOF LOADINGEDOARDO PROVERBIO, GIUSEPPE CAMPANELLA and VINCENZO VENTURI*
Dept. Industrial Chemistry and Materials Engineering, University of Messina, 98166
Messina, Italy. *Sidercem srl, 95045 Misterbianco (CT), Italy.

Abstract
Recent collapses of civil structures (reinforced concrete, prestressed concrete or mixed steel
structures, i.e. buildings, bridges, dams, and so on) have demonstrated once again the need forreliable tools for an early monitoring of damage progression. Damages due to deterioration
processes, overload, bad design, poor material quality, can grow subcritically until final collapse.
Acoustic emission has been successful used for more than 20 years in industry for monitoring
metal equipments (pipelines, pressure vessels, gas tanks, etc.) and the technology is quite mature.
The application of the AE technique in the civil engineering requires however the overcoming ofseveral problems related to structural complexity, material inhomogeneity and the highattenuation factor for high-frequency acoustic waves in concrete, and environmental noise. A
great effort yet has to be done on data handling and data interpretation.
We have used AE technique for damage evaluation of two severely deteriorated viaducts
during proof loading. The viaducts, made at the end of the 1950’s along the national road 114 inSicily (Italy), consist of several spans. Each span was built in a post-tensioned concrete girderconfiguration. The load test consisted basically of 5 loading steps and 5 unloading steps. For
each load test step, it has been possible to evaluate the displacements in the middle, in the borderand in the center of the span. It was observed that, during loading phase, the mean AE hits rate,
corresponding to a minimum in the mean amplitude, reached a maximum at an intermediate load,
while at higher loads a reduced number of AE hits characterized by high mean energy wasrecorded. AE behavior was asymmetric with respect to the loading sequence (relaxation
dominant), indicating the advanced degradation status of the viaducts.
Keywords: Concrete structures, bridge, proof loading, corrosion
Introduction
Segmental cast-in-place post-tensioned structures were extensively used in Italy during thereconstruction processes soon after the Second World War, as innovative design solutions
adopted for a rapid and economical building of bridges. After 50 years, most of those structures
have formally concluded their design service life, but social and economic reasons frequently
force their owners toward rehabilitative solutions instead of undertaking demolition andrebuilding. The rehabilitation project and the evaluation of the residual load-carrying capacity
involves the solution of unusual problems arising during the degradation assessment step linked
to the peculiar geometry and to the structural weakness of such constructions. For such
structures, the main concern is the status of prestressing or post-tensioning cables.
The evaluation of corrosion of the post-tensioning cables (usually grouted in ducts placed inbox girder walls) required the use of new, but not yet well established, techniques such as thoseJ. Acoustic Emission, 25 (2007)

324© 2007 Acoustic Emission Groupbased on magnetic induction, while indirect techniques such as impact echo could locate
grouting defects, but not give any information about cable conditions.
Since the difficulties in accessing to cable anchorage and the impossibilities to re-tensioning
cables themselves the evaluation of mechanical characteristics and performance of the viaducts
could be estimated on the basis of a dynamic behavior analysis, whose main limit is however thedefinition of the right theoretical model. AE technique seems to be very promising in this field
since it is not invasive, allows a volume evaluation and at the same time has the possibility tolocate discrete defects. AE was however introduced very recently in the field of healthassessment of reinforced concrete structures notwithstanding some difficulties to be overcome in
the field of data handling and analysis. Relationship between AE signal parameters and failure
processes that produce these signals have in fact to be properly defined for example by means of
the development of pattern recognition techniques. With the same aim, several health indices aswell as “Load ratio”, “Calm ratio”, “Felicity ratio” or “Historical index” have been defined [1-2].
Experimental
The Agrò and Fiumedinisi viaducts, on the national road number 114 on the eastern coast of
the Sicily Island were designed in 1954 by Riccardo Morandi and built during 1955-56. They
are 13- and 8-spans viaducts, respectively, with the span length of ~22 m. The viaducts aresegmental cast-in-place post-tensioned prestressed concrete box girder type, characterized by afive-cell longitudinal trapezoidal void section box girder. Four cast-in-place diaphragms were
provided at each end and along each span of the bridge. An 11-cm-thick top concrete slab wasmonolithically cast-in-place. The post-tensioning ducts of 40-mm diameter were drilled in-place
and the post-tensioning internal tendons consisted of 5-mm wires bundle. Following detailed
visual and instrumental inspection, which showed a critical degradation status due to seaside
vicinity and to the “advanced” age [3], it was decided to evaluate damage effects on structural
behavior by means of proof-loading test and a simultaneous AE monitoring.
The set-up of the AE system involved a long series of calibration tests on concrete samples atrest, carrying out the scratching and breaking of pencil-leads of different hardness anddimensions on the surface of samples; these tests have been carried out in different environments
to test the effectiveness of filter system and to calibrate the “trigger threshold” suitable to extract
the significant components of AE signals.

Fig. 1. Scheme of the static loading condition during the fourth loading step.
325

The proof loading test consisted basically of 5 load steps:
I° load test step: the load was produced by a 30-ton (0.3 MN) truck set in the mid-span;
II° load test step: the load is produced by a 30-ton truck set in the middle and by a 20-ton(0.2 MN) truck set on the parallel lane; Total = 0.5 MN
III° load test step: the load is produced by two 30-ton trucks set on the same lane; 0.6 MN
IV° load test step: the load is produced by two 30-ton trucks set on the same lane and a 20
ton-truck set on the parallel lane; 0.8 MN
V° load test step: the load is produced by two 30 tons (0.3 MN) trucks set on the same lane
and a 20-ton (0.2 MN) truck plus 30-ton (0.3 MN) truck set on the parallel lane; 1.1 MN.

Fig. 2. Displacement of the measuring point (Fig. 1) on the viaduct span during proof loading.
A scheme of the 4th load test step with the indication of displacement measurement points isreported in Fig. 1. For each load test step, it has been possible to evaluate the displacements inthe middle of the span in the border and in the center (Fig. 2). AE sensors adopted were VS30-V
flat response type (23-80 kHz bandwidth, 140 pF capacitance), a total of ten sensors were usedand positioned on the lateral side of different segments of box girders as shown in Fig. 3.
Results
The AE application has proven effective to detect vibration phenomena produced by trucks
crossing on the structure, and the intense AE activity recorded was connected to the residual
stress in the structure induced by the post-tensioning system, while the events recorded during
the first phase seems to be connected with the phenomena of internal rearrangement of preexisting defects.
326

Fig. 3. AE sensors positioning on box girders.

Fig. 4A. Mean numbers of hits for all channels vs. time for the second load step (load: dashedline; load scale on the right side in MN).
Considering the huge amount of data recorded, only a limited amount of available results arereported here and related to span No. 12 of the Agrò viaduct. Hits, energy and amplitude of AE
events have been reported as mean values of the total amount of events recorded during a
specific loading step (i.e., within a specific time interval).
An example is reported in Fig. 4 and refers to the second load step. One of the main aspectthat must be highlighted is that the great amount of hits have been recorded during transientphases (loading and unloading), while, at the maximum load, the structures remained almost
silent (Fig. 4A). On the contrary, the energy of the AE events reached the maximum value at the327

Fig. 4B. Mean energy for all channels vs. time for the second load step (1 eu=10-14V2s; load:
dashed line; load scale on the right side in MN).

Fig. 5. Mean hits, amplitude and energy for channel 7 during loading steps matched witheffective displacement measured at points 3 and 6 (Displacement scale on right side).
intermediate loading (0.3 MN) as reported in Fig. 4B. It is important to keep in mind that byconsidering the specific loading procedure, which considered the presence of asymmetric loads,
the maximum displacements of all the measuring points of the side beams do not correspond tothe maximum load applied (Fig. 2). In Fig. 5, relationship between AE parameters for sensors 7
and displacements measured at the nearest measurement points (3 and 6) are reported.
328

As a further evaluation of the degradation status of the viaduct, we carried out a relaxation
ratio analysis [4]. Relaxation ratio is defined as the ratio of the average energy during unloading
phase to the average energy during loading phase. Considering that AE activity during theunloading process is generally an indication of structural instability [5], a relaxation ratio greaterthan one (relaxation dominant) implies a defective structure.
In Fig. 6, results obtained on the second box girder are reported. A strong peak in therelaxation ratio during the third loading condition is evident for channels 3, 8 and 9 (on the samebox girder side), while at higher load condition (0.8 MN) the peak was recorded on channel 4 (onthe opposite side of the box girder).

Fig. 6. Relaxation ratio for channels 3-4-5 and 8-9 on box girder No. 2.
Conclusions
We used AE technique for damage evaluation of two severely deteriorated viaducts during
proof loading. The viaducts, made at the end of the 1950s along the national road 114 in Sicily(Italy), consist of several spans. Each span was built in a post-tensioned concrete girderconfiguration. It was observed that during loading phase, the maximum mean AE hits rate,
corresponding to a minimum in the mean amplitude, was reached at an intermediate load, whileat higher load a reduced number of AE hits characterized by high mean energy was recorded.
The application of the relaxation ratio analysis indicated the structural instability (relaxation
dominant) of the two viaducts.
Acknowledgment: The present research was carried out with the financial support of the
Ministry of University and Scientific Research in the frame of the research project n°12278“New technologies for the evaluation of deterioration and the control of prestressed reinforced
concrete structures”.

329

References
1. L. Golaski, P. Gebski and K. Ono, J. Acoustic Emission, 20, 2002, 83-98.
2. Recommended Practice for In-Situ Monitoring of Concrete Structures by Acoustic
Emission, NDIS 2421, Japanese Society for Non-Destructive Inspection, 2000.
3. E. Proverbio, G. Laganà, V. Venturi, Integrated Lifetime Engineering of Buildings and Civil
Infrastructures Proc. 2nd Int. Symp. ILCDES 2003, pp.239-244
4. S. Colombo, M.C. Forde, I.G. Main, J. Halliday and M. Shigeishi, Materials and Structures,
38 (11), 2005, 851-856.
5. M. Ohtsu, M. Uchida, T. Okamato and S. Yuyama, ACI Structural Journal, 99 (4), 2002,
411-417.

330

EARLY FAULT DETECTION AT GEAR UNITSBY ACOUSTIC EMISSION AND WAVELET ANALYSISCHRISTIAN SCHEER, WILFRIED REIMCHE and FRIEDRICH-WILHELM BACH
Institute of Materials Science, Department of Non-Destructive Testing,
Leibniz Universität Hannover, An der Universität 2, 30823 Garbsen, Germany

Abstract
To secure the availability of machines and facilities, the reaction to beginning damage must
be as fast as possible. Thus, early detection of damage initiation is crucial to the quick planningand execution of reconditioning measures. This helps to minimize downtime and increase availability. Modern high performance transmissions in fabrication and energy industries more andmore have to satisfy high requirements concerning their nominal load, running properties andoperational stability. Helical cut gears with small modules are usually used to meet these demands. To assure an undisturbed operation, to avoid unplanned downtimes and consequential
damages of high performance transmissions a condition monitoring and fault diagnosis is useful.
Vibration analysis is a good tool for detecting faults and unacceptable operating conditions ofmachines in an early stage, due to its large dynamic range when using spectral and correlation
analysis. Detecting a defect in a machine by vibration analysis is however not possible before thedegree of damage is already affecting its vibration characteristics. Using classical vibration
analysis, especially the detection of cracks and their propagation in rotating shafts and gear
wheels, is possible only with relatively short forecasting times before failure. Early detection of
cracks in shafts and gear wheels is possible by acquisition and analysis of acoustic emissions(AE). Unlike accelerometers that capture the physical behavior of the component, like low- or
high-frequency vibrations up to 200 kHz with a linear frequency response, an AE sensor is very
sensitive in its resonances at higher frequencies in order to detect ultrasonic impulses caused bychanges within the material’s structure like crack formation and crack propagation [1, 2].
For the detection of gear defects in transmissions by AE analysis, the signal path should be as
short as possible. This minimizes interferences, like the influences of ball bearings and othermachine parts and emphasizes on useful information about faults. At a gear wheel test bench,
acoustic emission sensors were placed on the gearbox casing and additionally on the ends of therotating shafts in order to detect defects at an early stage as well as enabling their location andthe determination of the affected component. Because of the superimposed emissions of the rolling element bearings and other components it is not sufficient to regard only the number andintensity of the emissions. Therefore an additional evaluation of the short-time excitations infrequency domain is necessary using wavelet analysis. Compared with an FFT, wavelet analysis
offers a higher resolution in time domain especially for high frequent events. The crack initiationand crack propagation at the root of a tooth shows early changes in the wavelet plot, which areperiodic with rotational speed. Propagating pitting shows a different behavior. Both different
faults show a characteristic development during the lifetime of a gear wheel.
Keywords: Wavelet analysis, condition monitoring, gear faults, rotating machinery, rotating
sensors

J. Acoustic Emission, 25 (2007)331© 2007 Acoustic Emission GroupIntroduction
Due to increasing competition, companies are forced to introduce innovative production
technologies rather than only relying on the evolution of established processes. The striving for
economic production methods with short processing times as well as the more stringent requirements for properties of today’s high performance components brings near-net-shape forming
methods into the centre of attention. The goal of the Collaborative Research Centre 489 (SFB489) is to develop new technological and logistical innovative as well as economic process
chains, based on precision forging technology for the mass production of high performance components. One main target is to realize a considerable reduction of the entire process chain, which
is founded on the integration of manufacturing steps as well as the substitution of metal cutting
sequences by the employment of the precision forging technique. Thus, it is necessary to convert
smooth metal cutting sequences for the precision forging process together with an integrated heat
treatment [3].
Dynamic load test of components in the process chain “Precision Forging”
The high demands on technological properties of the precision forged components like gear
wheels require tests at realistic operating conditions. Hence one central point of the project isfixed by the diagnosis of components where the precision forged gear wheels are tested in thegear test bench to analyze the running behavior and the development of damages. In addition to
the running and sound behavior the rate of wear and the development of damages have to be acquired and described by adapted measurements of acceleration and improved analysis techniques.
Another point is the out-of-phase determination of quality features dependent on the loadingstate, like the tooth flank strength and the tooth root strength of precision forged gears, which isessential for the optimization of the component’s properties and the manufacturing steps in theprocessing line. Other factors of influence given by the manufacturing process are the surface
finish, deviation of tolerances, the reshaped material itself, and the hardening process. The precision forged components are compared with conventional machined components to characterize
the influences of the heat treatment and the hard finishing on the qualities and the properties.
This enables the other sectional projects to optimize the component and the individual process
steps [3].
Wavelet-analysis
Wavelet analysis provides a method of decomposing a recorded signal into a family of component parts. It is therefore a suitable new analysis technique to determine fault patterns. For
general signal analysis, the objective is usually to extract frequency data from the signal andlearn how its frequency composition changes with time. Besides the short-time-Fourier transform(SFFT), the wavelet transform is a method for the visualization of the time-frequency-intensity
behavior. But in this case the wavelet transformation allows a higher resolution in time domain
than the SFFT. Using the wavelet transform a so-called mother-wavelet has to be chosen, whichis continuous in time and frequency and has an average value of zero. The wavelet transform
scales this mother-wavelet for each section of the signal to allow the best fit in frequency andamplitude. The two scaling factors plotted in time domain are the frequency and amplitude coefficients of the wavelet plot. The wavelet transform has the advantage of showing low-frequency
signal components as well as high frequency components with a high resolution in time domain,even if they only arise for a short time [4, 5].

332

Acoustic Emission Analysis at Rotating Machinery
In addition to the instrumentation with accelerometers one of the gearboxes was equippedwith AE sensors at different measurement positions. At first gear wheels were tested as can beseen in the right part of Fig. 1. For these tests two AE sensors were placed on the two bearing
housings, fixed bearing and loose bearing. One was placed on the lubrication conduit and anotherone right in the oil beam of the oil conduit in order to obtain AE signals, which may cause a
feedback via the oil beam. Last but not least a rotating AE sensor was placed at the end of the
shaft with the test gear. The sensor itself is positioned at the rotating end of the shaft, containing
a sender, which is facing a receiver and sending the AE information via induction [6].
Further tests were performed using pinion shafts as displayed in the left part of Fig. 1. Another arrangement of sensors was used to enable location of AE events. At the corners of the
gearbox four sensors are placed in addition to two rotating ones at the ends of the shafts.
For data acquisition a multi-channel AE measurement system was used using statisticalmethods to investigate AE properties like amplitude, energy, hits and counts. Additionally, thesensor signals were periodically obtained by a high frequent transient measurement system to gettransient data of several revolutions of the test shaft [7-9].

Fig. 1: Arrangement of AE-sensors at gearbox.
During the first test measurements with different speeds and different loads the influences ofthese parameters on the signal to noise ratio were identified and the thresholds for the AE amplitudes were set. Different loads ranging from 200 to 450 Nm showed no significant influences.
For the following description of the fault initiation and propagation only the information of therotating acoustic emission sensor at the end of the test shaft is used. The other sensors showed nosignificant information about the condition of the test wheel because of their complex signal path333

and superimposed AE from other components of the test bench. Figure 1 shows the arrangementof the different sensors and the transmission path from the teeth to the sensor. The mounting ofan AE sensor directly on the gearwheel was not possible because of the shape and size of thegear wheel and the dimensions of the gearbox. Additionally, the connection of signal cables to asensor directly on the gear wheel via the rotating shaft is quite difficult.
After the mounting of a test wheel and start-up of the test bench during the first hours of operation a quite high amount of AE signals can be detected. Seizing of the keys and contact areasof the test gear and the shaft caused these AE signals. After a few hours, this run-in effect is overand the normal operation condition with only a small amount of events is reached. Additional
disturbances may occur if there is too much slack between shaft and gear wheel. Therefore, seizing occurs between shaft and gear wheel, which causes a high amount of AE signals. Anotherevent causing AE signals was the fracture of a key between shaft and gear wheel. The propagating fault caused AE signals over several hours. In this case the detection of faults at the gear
wheel is possible only with a close fit between shaft and gear wheel. Additionally the test bench
has to be run-in and has to be working at operating temperature.

Fig. 2: Run-in behavior of gear wheel.
The start-up of the test bench with gear wheels and too much slack between shaft and gear
wheel is shown in Fig. 2. The relation of the hit rate to the other properties of AE signals shows
information about the source of the AE signals. A high hit rate with concurrent high amplitudes
or energy assumes the source of AE signals to be close to the sensor and therefore in the area of
the joint between shaft and gear wheel by the keys. Because of the long distance between sensor
and teeth mesh AE signals from the teeth reach the sensor at the end of the shaft with low amplitudes only.

334

The fixed bearing at the beginning caused the high amount of AE signals with low amplitudes. Figure 3 shows AE signals at start-up, which occur with ball-spin frequency (BSF) at the
fixed bearing. Intense heating of the shaft after start-up causes decrease in bearing gap. When theoperating temperature is reached, the bearing gap is correct and there is a decrease in AE signals.
Additionally there are increased relative motions between surfaces during start-up and run-in.
After about three hours the run in effect is finished and there is normal operation with low hit
rates only.

Fig. 3: Influence of fixed bearing at start-up.
Detection of Gear Faults by AE Analysis
The progression of the properties of an AE signal for the undisturbed reference condition
after run-in and the crack initiation and propagation until the failure of the gear wheel and shut
down of the test bench is displayed in Fig. 4. For the description of the AE signals, the number ofhits and the energy of the signals are shown, which are usually used for failure detection.
In the upper part of the figure, the number of hits during the reference condition is very low,
whereas in the lower diagram the crack initiation can be detected by an increasing hit rate causedby the opening and closing of the crack during one revolution of the test wheel. With an increaseof load cycles the fracture of one half of the tooth occurs. Since the other half of the tooth is still
carrying the load the test bench could be operated further. In this case, the energy of the AEsignals shows no significant changes regarding the fracture of a gear wheel tooth.
Another example of the detection of gear faults is presented in Fig. 5. Here, the beginningand propagation of pitting at the teeth flanks is shown. Compared to the reference condition,
there is an increase in the hit rate, which is not as great as in the case of a crack. Also the hit rate
may decrease again, when pitting has developed. There is a more fluctuating behavior of the hit
rate. The propagation of pitting in the lower diagram of Fig. 5 shows this swelling progression to
be more significant.
335

Fig. 4: AE data of reference and crack.

Fig. 5: AE data of reference and pitting.
More significant differences between crack development and pitting show transient signals of
the shaft sensor, logged over several revolutions of the shaft. These were recorded using an additional high-speed data-acquisition card with 1-MHz acquisition rate through 250 ms. The amplitude of an AE sensor is influenced chiefly by its main resonance ranges because of its non-linear
behavior. To evaluate signal components out of these ranges a transformation into frequency
domain is necessary. Especially the wavelet transform is a good means for this kind of high frequency analysis with short-time events.

336

The time-frequency components of a crack development compared to the reference condition
are displayed in Fig. 6. The wavelet transform is used here with the Morlet-wavelet as themother-wavelet. The reference condition as well as the crack at a single tooth shows high amplitudes at about 100 kHz (resonance of the sensor, resp. the measurement chain). These lowfrequency parts of the signals are not detected by the AE system because it uses a 120 kHz highpass filter. The crack is clearly visible by short-time pulses in the frequency range of 200250 kHz. They appear at ca. 0.04 s intervals, which equals the rotational speed of 25 Hz. Six
revolutions are displayed in the diagram and during each revolution there is a short-time excitation at 200-250 kHz when the damaged tooth comes into mesh.

Fig. 6: Wavelets of reference and crack.
The behavior of pitting can be seen in Fig. 7 in contrast to the crack development in Fig. 6.
The dominant signal component at 100 kHz is visible in this plot, too. There is a much more
broad-banded signal component visible over the whole frequency range up to 400 kHz, whichlasts much longer than the excitation of the crack development. It is excited more than once per
revolution because of the pitting development at several teeth simultaneously. When these teeth
are in mesh the pitting propagates and AE signals are generated.
Location of Fault-specific Events
Because of the very high acquisition rates of up to several MHz, the location of AE signals is
possible using run-time analysis of several sensor data. The sound waves propagate in concentric
circles, resp. spheres, from the point of origin and can be detected by AE sensors. Propagating
through solid matter the waves are damped. The maximum range of the sound waves depends
upon the material properties, the geometric and surrounding conditions of the object and thenumber of surface contacts between different objects. At metal parts these high frequency soundwaves may be detected beyond a distance of several meters. The determination of the speed ofsound, which is necessary for the location, is quite difficult at complex three-dimensional structures, because it varies dependent on different media and many contact surfaces. Performing this337

location of AE events inside a gearbox an averaged speed of sound is used. Further analysis of
the signal paths, the materials and contact surfaces will improve this method.

Fig. 7: Wavelets of reference and pitting.

Fig. 8: Location of teeth mesh inside the gearbox.
Four sensors at the corners of the gearbox near the shafts are used to locate AE-events inside
the gearbox (Fig. 1, Fig. 8). This enables a location on a sectional plane of the gearbox. Becauseof the very good results of the shaft sensor an additional rotating sensor was placed at the end of
the second shaft to realize a linear location between these two sensors.
338

Additionally, the gear test bench has been modified for a pinion shaft as another test object.
The advantage to the gear wheel is the difference between signal path for the sensor and transmission path of the load. The power transmission is not in the area of the signal path from teeth
mesh to the rotating sensor at the end of the shaft (Fig. 1). This causes much less AE events byseizing in the fit and at the keys. Therefore, the run-in effect could be minimized and additionallya higher rotational speed of 45 Hz could be used.
The locations of the AE events inside the gearbox are presented in Fig. 8 and Fig. 9. The linear location between sensor 1 and sensor 2 is visualized in the upper part of the figures. In thelower part of the figures, the sectional plane of the gearbox is visible showing the location of AE
events inside the gearbox. Figure 8 shows the reference condition with only few events locatedbetween the sensors 1 and 2 and in the area of the teeth mesh in the sectional plane located by thesensors 3 to 6.
The propagation of faults is shown in Fig. 9. Regarding the linear location a deviation of thehit maximum towards sensor 1 is clearly visible. The distances from the maximum to sensor 1
and sensor 2 identify the AE events as coming from the area of the teeth mesh. The location by
the sensors at the gearbox shows a similar result. Using the sensors 3 to 6, there is an accumulation of AE events in the area of the teeth-mesh and at the roller bearing on the pinion shaft. The
AE signals from the teeth mesh are generated by the teeth mesh itself as well as by the development of a crack, shown in green. The AE signals in the bearing area are caused by seizures in theclose fits and a wrong fit of the bearing inner ring, shown in red (cp. Fig. 1). Additionally, there
are several location results, which are not feasible, shown in light blue.

Fig. 9: Location of faults inside the gearbox.
Future work consists of filtering the AE data according to their location to improve the distinction between different gear faults, run-in effects, seizures and faults at other components likerolling element bearings.
339

Summary
Condition-based maintenance is a contribution to ensure the reliability of operation, highavailability and efficiency of industrial machines and plants. This necessitates the acquisition of
information about the condition of single components.
1. The condition monitoring of the gear test bench at the Institute of Materials Science (IW) is
achieved by vibration measurements to describe the operating condition and detect the development of faults by analysis in both the time and frequency domains. Slow developing faults like
pitting can be detected quite early by this method. The detection of cracks leading to tooth fractures is possible with a very short forecast time only.
2. The analysis of AE signals allows an early detection of cracks in the tooth root because of thecrack propagation before the teeth mesh is disturbed and the vibration behavior changes.
3. The location of acoustic emission events enables the allocation to specific components inside
the gearbox to improve the distinction between gear faults, run in effects, seizures, and rollingelement bearing faults.
Acknowledgements
We express our sincere thanks to the German Research Foundation for the promotion of thiswork within the Collaborative Research Centre 489 “Process Chain for the Production of Precision-forged High Performance Components”.
References
1. Toutountzakis, T., Mba, D.: Observations of Acoustic Emission Activity during Gear DefectDiagnosis, NDT&E International, 2003, 26, 471-477.
2. Tan, C. K., Irving, P., Mba, D.: Prognostic Potential of the Acoustic Emission Technique for
Spur Gears, Essential Technologies for Successful Prognostics, 59th Meeting of the Society for
Machinery failure Prevention Technology, Virginia Beach, US, April 2005.
3. Bach, Fr.-W., Kerber, K.: Sonderforschungsbereich 489 „Prozesskette zur Herstellung präzisionsgeschmiedeter Hochleistungsbauteile“, www.sfb489.uni-hannover.de, 2006.
4. Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J.-M.: Wavelet Toolbox User’s Guide,
Mathworks, 2000.
5. AGU-Vallen Wavelet, wavelet transform tool, Vallen-Systeme, Icking, 2005.
6. Sporer, A.: Hohe Sensibilität entwickeln, Acoustic-Emission-Systeme für die Schleifprozessüberwachung, Walter Dittel GmbH, Werkstatt und Betrieb 03/2006.
7. Vallen, H.: Die Schallemissionsprüfung, Castell-Verlag, 2003.
8. Vahaviolos, S.J.: Acoustic Emission: Standards and Technology Update, ASTM STP-1353,
1999.
9. AMSY-5 Users Manual, Vallen-Systeme GmbH, 2004.
340

APPLICATION OF ACOUSTIC EMISSION IN MONITORING OF
FAILURE IN SLIDE BEARINGSIRENEUSZ BARAN, MAREK NOWAK and WOJCIECH DARSKI*
Cracow University of Technology, Institute of Production Engineering M-6, Kraków, Poland;*Gdask University of Technology, Ocean Eng. and Ship Technology, Gdask, Poland

Abstract
The article describes the laboratory tests, which make the first stage of the study concerning
the use of the AE method to determine the technical state of the slide bearings in engines with
self-ignition. The aim of the present tests was to compare the recorded signals in relation to thetechnical state of the material of the bearing bush and to check the possibility of using the AE
method in determining the transition moment from the fluid friction into the semi-dry friction in
the bearing and signaling the first micro-defects of the material of the bearing bush. The
experiment has not solved the problem, but they are of a development character and will be
continued in the nearest future.
Keywords: Frequency analysis, slide bearing, friction factor, bearing bush
Introduction
Ship combustion engines with self-ignition have fundamental influence on the profitability ofships and their safety at sea. Therefore, it is important to ensure the failure-free operation ofthese engines. In ship engines, the maintenance of the sliding abilities of the main bearings andconnecting-rod bearings is of special importance because the failures of these bearings constitutealmost 30% of the total number of failures in all parts of engines. Since the costs of failures ofcombustion engines caused by the failures of slide bearings are high, intensive theoretical andexperimental tests of the slide bearings properties have been undertaken [1, 2].
The currently used methods of monitoring of the technical state of slide bearings indicate thestate of failure only when a considerable degradation of the slide bearing material appears. In
many cases, this is unsatisfactory in avoiding severe defects or failures. Testing by means of the
AE method occurs in real time, and this method can be used as a system of the early warning to
avoid damage in structural parts of combustion engines [3, 4].
The presented tests were carried out on the fully computer-controlled laboratory stand (PG-2
1 type) for the examination of slide bearings, as shown in Fig. 1. This stand enables one tosimulate the real performance conditions of a bearing and to record all the essential parameters,such as: moment of friction, rotational speed, temperature of the bearing and of the lubricatingoil. Technical data of PG-2 1 laboratory stand:• Power of engine:
11 kW,• Range of rotation of shaft:
1-4000 RPM,
• Maximum transversal load:
150 kN,• Diameter of shaft:
76.18 or 80.25 mm,• Length of bush:36.75 or 40.00 mm.

J. Acoustic Emission, 25 (2007)

341© 2007 Acoustic Emission GroupThe tested bearing bush of MB10-type (Fig 2) is used in C-330-type diesel engine. This
engine is generally used for drive auxiliary assembly on ships.

Fig. 1. The laboratory stand PG-2 1 for examination of slide bearing.
Technical data of MB10-type bush:• Catalogue No.:
1006022010
and 1006021010,• Length:40.00 mm,
• Outside diameter: 86.00 mm,• Thickness of wall: 2.905 mm,• Thickness of steel part:2.600 mm,
• Diameter of seating of frame:
86 mm.
Tests were made under different conditions ofslide bearings:• Constant transversal load andconstant rotation:
1 or 2 kN, 1700 RPM,
• Constant transversal load and cyclic
variable rotation:
1 or 2 kN, 1-1700 RPM,
• Step changing transversal load andcyclic variable rotation:
1, 2, 4 and 8 kN, 1-1700 RPM.

Fig. 2. The bearing bush MB10-type after
several cycles.

The trials by the AE method were carried out with the use of the Vallen AMSY5 system withdifferent types of sensors (VS150RIC-Vallen, WD-PAC and others) in order to cover the widest
band of frequency measurements. At the same time, the AE system enabled us to record the test
parameters (moment of friction, rotational speed, loading force, temperature) in order to correlatethem later with the recorded AE.

342

Measurements of the acoustic background
The first stage of the tests concerned both the measurement of the acoustic background andthe noise generated by the operation of the installations on the testing stand. That is why the firsttests were carried out on a new bearing in different loading variants and rotational speeds withthe recording of signals in a widest band of frequency. The frequency analysis of the recorded
signals allowed us to determine the main band of noise and the selection of frequency high-passand low-pass filters for subsequent measurements in order to minimize noise coming from theoperation of the testing stand.
An example of the recorded bands of frequency on two wide-band PAC-WD sensors withconstant transversal load and constant rotation is presented in Fig. 3. Two examples are given for
narrow-band filtered signals (a) and wide-band signals with strong low-frequency activity (b).

Fig. 3. The arrangement of the frequency bands of the signals recorded by 2 wide-band sensors.
(a) narrow band, 100-300 kHz segment of 95-850 kHz filter output; (b) Wide band, 20-850 kHz.
Measurements of bearings in different technical state
The first stage of the tests using frequency filters was the measurement of a new bearing withthe accepted conditions (loading and rotational speed) similar to the preliminary tests. We usedthe constant rotational speed of the shaft of 1700 RPM and two values of the bearing loadings, 1
and 2 kN. The AE parameters for these operating conditions for a new bearing established thebasis of comparison in the remaining tests.
In order to compare the AE signals recorded during the testing of the new bearing with thesignals coming from defected bearing, the measurements were taken of three sliding bearings
made of the same material (simulated different bearing capability):•••

The new bearing bush without defect – full bearing capability,
The bearing bush with an opening, simulating a local defect in the material of the bush,which caused disturbances in the flow of lubricating oil – a small but apparent loss ofbearing capability,
The bearing bush with longitudinal and circumferential scratches simulating considerable
wear of the bush material – large loss of bearing capability.

343

High activity of AE was recorded but clearly different for each kind of tested bearing bushes.
General AE parameters such as: Hits, Counts, Duration, Energy and RMS showed differences
between bushes. However, Amplitude of signals contained similar ranges for all the tested
bearing bushes. To evaluate different activities of AE, uses of different threshold levels madecomparison difficult with these parameters. At this time, the best parameters for comparison are
RMS and frequency distribution of signals. Recorded RMS for the new bearing and the bearing
simulating the local loss in the bearing bush material are presented in Figs. 4, 5 and 6. There aredata of RMS from different channels with different frequency ranges. Two types each of lowand high-frequency data (with or without *) and medium frequency data are shown. Note RMS
for High Frequency (red line) is off-scale because overloading occurred on this channel.

Fig. 4. RMS for different ranges of frequency filters – new bush with no defect.

Fig. 5. RMS for different ranges of frequency filters – bush with an opening defect.
344

Fig. 6. RMS for different ranges of frequency filters – bush with scratches.
The observed data shows general increase of AE RMS levels (except Low* and High*channels between Figs. 4 and 5). Medium and High channels respond to bush damage statessensitively. The relative changes in the 5-channel data provide clear indication of the bushdamage states.
The initial dimensions of the bearing and the bearing’s fit are of vital importance on theparameters of the recorded AE. For the bearings of the same dimensional group, different levelof the AE activity was recorded. It is especially visible in the first stage of the bearing operation,
when the initial wear of bearing bush surfaces to the shaft neck takes place. Therefore, it is vitalto enlarge the data library by taking a larger number of measurements for each of the acceptedvariant of the bearing states.
Considering the serious differences in the intensity of emission for the particular kinds of thestate of bearings during the measurements, changes were being made in the arrangements of theacquisition parameters in order to record the measuring data, which could enable the subsequent
analysis with the use of the waveform [5].
The measurement of the bearing with changeable parameters
During the testing of the new bearing, a measurement was also taken under the conditions ofvariable rotational speed and step changing of transverse load, which were to lead to a quicker
wear of the bearing bush material. After the cyclic changes in rotational speed, the AE wasrecorded during the operation of the bearing under the established conditions. The recorded AE
was to detect the appearance of the first damage in the material of the bearing. The sum of
distribution of hits in frequency bands is presented in Fig. 7. The visible change in the activity ofhits indicated a change in the operation of the bearing. The reason of the sudden increase in the
AE activity was the wear of the surface layer of the bearing. These changes were invisible in theconventional bearing parameters (moment of friction, temperature, ...) recorded on the testing
stand PG-2 1. It was indicated that significantly changing inside slide bearing is detectable by
AE. The example of one of the half bush after these tests is shown on Fig. 2, where surface wear345

can be seen. It should be noted that this research requires a larger number of the trial tests inorder to record AE in different states of wear of the bearing material.

Fig. 7. The arrangement of the frequency bands of the signals during long-time tests withvariable rotation and step changing of transverse load.
The measurement under the conditions of intermittent fluid friction
One of the essential aspects of the tests was the measurement of AE during the disappearanceof the fluid friction and transition into the semi-dry friction; i.e., at the moment of the firstcontact of the pin with the bearing bush. To this end, the test methodology was modified andcaused to decrease the friction factor till the start of the contact of the metal pin with the bearing
bush. The AE recorded at this time signaled the decrease of the friction factor due to thedisappearance of the activity of the AE signals. This was followed by the increase in the friction
factor during the contact of the pin surface and the shaft, then a sudden increase in the activity of
AE was observed. Figure 8 shows the changes of the bearing parameters, the moment of friction(left) and RPM (right) in the new bearing test. The changes in the AE hit activities in various
frequency bands are also presented in Fig. 8. Here, actual RPM reached zero when the friction
was high. No AE was emitted in this period and until ~700 RPM was reached.
In this connection one should suppose that carrying out of a larger number of tests will allow
the unequivocal identification of the moment of transition from the fluid friction into the semidry friction. The presented tests were carried out on the testing stand under the laboratory
conditions. In the future, measurements of the bearings installed in combustion engines areplanned.
Conclusions
The application of the frequency filters allowed the reduction of the majority of noise for thetesting stand, but in the future it will be necessary to determine noise on real engines. The
presented examples illustrate that it is possible to apply the AE method in order to define thestate of the bearing material. The sensitivity of the AE method allows one to record the signals,
346

which indicate the transition from the fluid friction into the mixed friction. In the future, it will
be advisable to use a pattern-recognition analysis software to identify the recorded signals.

Fig. 8. The changes of the moment of friction (left) and RPM (right) parameters and the AE hit
activities in the new bearing test.
References[1] Burger W. Albers A. Scavino R. Dickerhof M.: Proc. of WTC2005 World Tribology
Congress III, Sept. 12–16, 2005, Washington DC, USA.
[2] Scavino R., Burger W., Fritz M., Albers A.: Tribologie Fachtagung 2004, Hungary Academyof Sciences, Kozma: Veszprem.
[3] Al-Ghambi, Zhechkov D., Mba D.: 26th Europen Conference on Acoustic Emission Testing,
Berlin, 2004,[4] Yoshioka T., Fujiwara T.: Wear, 81 (1), 1994, 183-186.
[5] Goaski L., Gbski P., Baran I., Ono K.: J. of Acoustic Emission, 17 (3-4), 1999, S37.

347

MAPPING OF WHEEL FLANGE RUBBING ON RAIL USING AE:
LABORATORY TEST
N. A. THAKKAR, R. L. REUBEN and J. A. STEEL
Mechanical Engineering, School of Engineering and Physical Sciences, Heriot-Watt University,
Riccarton, Edinburgh, Scotland EH14 4AS UK
Abstract
Abnormal contact stresses between wheels and rails can give rise to excessive rail wear and,in more extreme cases, to rolling contact fatigue (RCF) and gauge corner cracking (GCC). The
independent investigation into the Hatfield train disaster in the UK, whose primary cause was arail defect, highlighted the need for improved awareness of the condition of railway rails. This
work is aimed, not specifically at detecting rail cracks, but at monitoring the intensity of contact
between wheel and rail using acoustic emission with a view to making an in-service update ofestimated remanent life.
A test-rig has been built in order to simulate rail-wheel contact, providing for variations innormal contact force and rolling speed, and allowing the simulation of a range of defects in a
way that would not be possible at full scale. In this paper, wheel flange rubbing on the rail (aprecursor to GCC) has been simulated and monitored using acoustic emission (AE). A simple
analytical model for AE arising from normal wheel contact has been used to locate parts of the
track where a wheel flange is rubbing on the rail. The method works by comparing AE peaks
above normal with natural defects with and without the additional defect of flange rubbing, calculating a cross-correlation function, whose peakedness is an indication of how well correlated
the defects are with the AE peaks. In all cases, cross-correlations were better when rubbing waspresent indicating that flange rubbing can be detected even in the face of natural defects.
Keywords: Rail/wheel interaction, structural integrity monitoring.
Introduction
Fracture of railway rails can have serious consequences [1], and some shallow angle defects
cannot easily be identified even by the most advanced NDT defect detection techniques [2]. Contact stresses are responsible for rail defects and Miller [3] has identified the main factors affecting contact stresses as external load, traction force, and rail-wheel contact area. Contact area andhence the stress between rail and wheel had been measured as a function of load using ultrasonic
NDT although this has not yet been used to establish the exposure to wear and surface fatigue
under a range of conditions, such as load, speed and surface lubrication [4, 5]. More detailed mechanics analyses based on contact stresses (e.g., Ringsberg and Josefson [6]) have established theconditions for RCF initiation on rail heads.
Acoustic emission monitoring has been applied to a number of contact problems, such as
bearings and defects, and has been found to be sensitive to bearing defects [7] and gear defects[8] in reciprocating and rotating machineries. Contacts involving curved surfaces lead to particular issues with durability and, given that contact between a ball bearing and race and between
rails and wheel have some similarities [9], it is reasonable to suppose that AE monitoring can beused to examine the rail-wheel interaction and perhaps also be sensitive to surface and subJ. Acoustic Emission, 25 (2007)

348© 2007 Acoustic Emission Groupsurface condition. It has already been claimed that fatigue initiation and propagation in rolling
contact fatigue (RCF) can be detected using AE, Guo et al. [10]. Equally, it has been found thatultrasound is not useful for identifying gauge corner cracking in rails and even the eddy current
technique is not able to find all individual rail surface defects [2].
The principle of this work is that AE can be used to determine whether the contact between arail and wheel is “normal” or abnormal, abnormalities being defined as rough areas of the rail
surface, out-of-round wheels, poor flange conformity or misshapen rails. Being able to do thiswould allow a rail-mounted sensor to be used to count the contact cycles in terms of intensity as
well as number in a way that could not be derived from traffic figures alone, thus supplementing
rail maintenance management systems. In earlier work, Thakkar et al. [11] devised an analyticalmodel for the particular experimental set-up used, which describes the AE level from normal
rolling using some simple AE propagation coefficients derived from pencil-lead break tests on
the track. This model was used to identify areas on the track where natural surface defects cause
additional AE in a similar way to defect identification in bearings and gear teeth. In this paper, aparallel set of experiments is reported, using the same track surface condition, but with an eccentricity of the wheel trajectory in relation to the track, so that flange rubbing takes place over acontrolled length of the track.
Experimental Procedure
The test rig (Fig. 1b, c) consisted of a circular track round which a single wheel was driven
using a motor at the centre of the track and a rigid arm, to which the wheel was attached. To obtain a rubbing effect of the wheel flange on the track, the motor shaft was displaced by about 2.4
mm (Fig. 1a) from the centre of the circular track. The variation of contact around the circumference was therefore mostly “normal” with an arc, within which the flange comes into contact withthe rail, reaches a maximum contact load and then comes out of contact with the rail.
PAC Micro-80D AE sensors and PAC type 1220A preamplifiers (dual gain 40dB-60dB)
were mounted on one side of the joint in the circular track (Fig. 1b) and connected to an NI 6115
data acquisition board used to collect raw AE data.

Fig. 1: (a) Motor shaft displacement, (b) sensor positions on rail, (c) wheel flange rubbing on
rail.
The sensors were mounted on the web of the rail at 0.1 m and 1 m from the joint, the sensor
nearest the joint being used as the trigger sensor and the other to collect the data for processing.
The raw-data measuring sensor leaves 4.4-m distance on the other side of track, for wheel totravel before wheel arrives once again on top of the joint. The reason why raw-data measurement sensor was placed 1-m away from joint is to get adequate raw signal on both sides ofplaced sensor for the even minimum wheel speed (1 ms-1). Four different wheel speeds (1 ms-1,
349

1.5 ms-1, 3 ms-1 and 3.5 ms-1) were used and four different contact loads (4 kg, 5 kg, 6 kg and 6.5kg) were applied and, corresponding to applied speed, wheel travelling distance increased (2-m,
3-m, 6-m and 7-m, respectively) as the data-storage time was constant, 2 sec. The mounting
points were cleaned with sand paper and vacuum grease was used as a couplant with the sensors
being held against the surface using magnetic clamps. Data was acquired at 2.5 MHz, for 5
Msamples corresponding to 2-sec recording time, in which the wheel moves, for a speed of 3.5
ms-1, 7 m including the pre-trigger distance (0.14 m), completing more than a full revolution of
the test rig (5.4-m circumference) for the condition discussed. Five records were taken for every
wheel speed and load giving a total 80 records, under 16 different conditions. The track wasmade from 50 mm×6.25 mm profiled mild-steel bar, cold bent into the circular shape and joinedby a simulated fish plate with a 3-mm gap. The wheel was of cylindrical shape of diameter 90
mm with a flange on the inner edge and was mounted on two rolling-element bearings (Fig. 1c).
A dial gauge was mounted on the supporting arm (Fig. 2a) to measure the position of the rail
relative to the motor shaft after displacement (Fig. 1a) and measurements of the radial distance
between the rail and the arm tip taken at regular intervals around the track circumference. The
actual fitted (through measured points) and nominal circles are shown to scale in Fig. 2b, thenominal circle being the one, which assumes the design radius of 850 mm and a centre at (0,0).
Figure 2c shows the same data with the radial coordinate amplified by subtracting 840 mm fromeach radial value, and shows the flange contact zone, which corresponds to distances of 1.4 to
2.4-m from the joint.

Fig. 2. (a) Dial gauge on supporting arm, and measured points and fitted and nominal circles, (b)at full scale and (c) with amplified radial scale.
Results and analysis
A simple attenuation model has been developed [11], which treats a wheel as continuously
moving source that generates AE energy, E0, throughout its travelling. AE energy increases withincreasing speeds, on the simulated track for the constant raw-data measurement time (2 sec).
The total energy, Et, is recorded at fixed sensor 1-m away from the joint. The circumferential position x1 is the position of the wheel between joint to sensor when wheel approaches towards thesensor while circumferential position x2 is the position of the wheel between sensor to joint whenwheel travels away from the sensor. For both positions, we considered five different paths (direct
wave travelling, reflected wave travelling once it hits the joint from both sides and transmitted
wave travelling from both sides of joint) that change with respect to continuously changing
wheel positions so that Et can be calculated by equation 1 and 2 to discriminate the changes inthe signal due to wheel position with respect to raw data measurement sensor.
350

Et = E0 e! kx1 + RE0 e ( 2 1! k (c ! x )+ c2 )+ RE0 e! k ( c1 !( c2 ! x1 )+ ( c1 !c2 )) + TE0 e! k ( c1 ! x1 ) + TE0 e! k ( c1 + x1 )Et = E0e ! kx2 + RE0e ! k ( x2 +c2 +c2 ) + RE0e ! k ( c1!( c2 + x2 )+( c1!c2 )) + TE0e ! k ( c1! x2 ) + TE0e ! k ( c1+ x2 )

(1)
(2)

This model is fitted to RMS voltages of measured raw signals for explained condition andtime steps used for both are 2.9 ms. During the described time step, waves are travelling 8.99-m(1.66 times the track circumference) as the wave velocity was found to be 3100 ms-1 during calibration test by using pencil-lead break tests with four sensors in single array and similar threearrays used by keeping source sensor at constant distance and moving rest of the sensors 0.26-m
away from source. The attenuation coefficient, k = -0.57 dB/m, was found using the same calibration set up while calibrating reflection coefficient (R = 48%) and transmission coefficient (T =25%) from and through the joint, respectively.

Fig. 3: Measured rms AE at a wheel speed of 3.5 m/s. –––– rms AE;

analytical model.

Only one unknown in Eqs. 1 and 2 is E0, as c1 and c2 are the track circumference and distance
between joint and sensor, respectively. These are known and can be varied at not only every
measurements but also for Eqs. 1 and 2, to produce a best-fit level of the model. It takes the average value of RMS signal until the wheel arrives on top of the sensor from joint for Eq. 1 andagain takes average value of RMS but this time for Eq. 2 and distance of wheel from top of thesensor to wheel arrival on joint for the explained condition. Once the wheel arrives on top of thesensor again the cycle starts from the beginning.
351

Comparing the circumferential variation in AE with the best-fit model allows the identification of areas where there is an indication of abnormal contact. Figure 3 shows five independentmeasurements of the rms averaged AE (averaging time 0.01 s) along with the fitted normal contact curves for one example of a wheel velocity of 3.5 ms-1 and a preload of 4 kg. As can be seen,
the signal is significantly and consistently above the model fit in the flange-rubbing zone, highlighted by black fill, between D1 and D2.
The dashed lines marked with “J” are the positions where the wheel passes over the joint andhere spikes are evident due to wheel impact on the joint. The location of the joint in the AE record is an important part of the curve-fitting process as it allows the discontinuity between Eq. 1
and Eq. 2 to be identified. At the example wheel speed of 3.5 ms-1, the wheel arrives twice at thesensor during each record.
In the earlier work [11], the natural defects on the surface of the rail were measured and Fig.
4 shows their intensity, measured as the total length of defect per 0.1-m length of track alongside
the rms AE above background noise again averaged per 0.1-m length of track for each of the fiverecords at the example condition of 3.5-ms-1 wheel speed and 4-kg load. Figure 5 shows the corresponding autocorrelation function for each of the five records with their corresponding kurtosis
values, ranging from 1.9 to 2.07. Figure 6 shows the defect intensity distribution with flange

rubbing added, along with the corresponding AE signal above normal rolling. Figure 7
shows the cross-correlations for the flange-rubbing case along with the corresponding
values of kurtosis (1.66 to 1.88) for comparison with Fig. 5, again for the example condition. As can be seen, the kurtosis is improved over the case without flange rubbing.

Fig. 4: Signal above normal and defect intensity without flange rubbing.

Fig. 5: Cross correlation between defect intensity and AE above normal without flange rubbing.
352

Fig. 6: Signal above normal and defect intensity with flange rubbing.

Fig. 7: Cross correlation between defect intensity and AE above normal with flange rubbing.
Applying the same approach to the remaining speeds at a 4-kg load yields similar results, andFig. 8 shows the averaged value of kurtosis for the five records at each condition. It is clear thatthe average kurtosis is always smaller with flange rubbing, indicating a more peaked crosscorrelation function, although the difference is more marked at the lowest speed, where the kurtosis is generally higher, indicating generally poorer cross-correlation. It is suspected that thepoor correlation for low speeds is partly due to the fact that a complete revolution is not acquiredin the low-speed records and, more importantly, to the fact that the sensitivity to natural defects
is low at low speeds; this is also the case for low loads. Whereas the effect of speed and load on
sensitivity to natural defects is a matter for future investigation, it appears at present that the sensitivity to flange rubbing is not affected as much by the experimental conditions.
Conclusions
Using a simple model of AE for normal rolling and correlating the AE signal above themodel value with a measure of defect intensity per unit length of track, the following conclusions
can be drawn.
1. The signal-above-normal-rolling can be correlated with the intensity of natural defects andflange rubbing can be treated as defect of unit intensity.
2. The correlation is improved when flange rubbing is present and the correlations persist forflange rubbing under conditions where it is poor for natural defects.
3. The effect of experimental conditions (speed and load) on defect sensitivity is matter for
future investigation.
353

Fig. 8: Kurtosis with and without flange rubbing for all wheel speeds at 4-kg load. (Conditions 14; 1 ms-1, 1.5 ms-1, 3 ms-1 and 3.5 ms-1, respectively)
References
1. Office of Rail Regulation (ORR). “Train derailment at Hatfield: A final report by the independent investigation board”, (UK) Health and Safety Executive, July 2006.
2. Bruzelius, K., Mba, D., “An intial investigation on the potential applicability of acoustic
emission on rail track fault detection”, NDT & E International, 37, 2004, 507-516.
3. Miller, K.J., “Structural integrity – whose responsibility?”, Proceedings IMechE, Part L,
217(1), 2003, 1-22.
4. Pau, M., Aymerich, F. and Ginesu, F. “Ultrasonic measurements of nominal contact area andcontact pressure in a wheel-rail system”, Proceedings of IMechE, Part F, 214, 2000, 231-243.
5. Pau, M. “Ultrasonic waves for effective assessment of wheel-rail contact anomalies”, Proceedings of IMechE, 219, 2005, 79-90.
6. Ringsberg, J.W. and Josefson, B.L. “Finite element analysis of rolling contact fatigue crack
initiation in rails”, Proceedings of IMechE, Part F, 215, 2001, 243-259.
7. Morhain, A. and Mba, D. “Bearing defect diagnosis and acoustic emission”, Proceedings
IMechE, Part J, 217(4), 2003, 257-272.
8. Toutountzakis, T. and Mba, D. “Observation of acoustic emission activity during gear defectdiagnosis”, NDT & E International, 36(7), 2003, 471-477.
9. Kalker, J.J. “Survey of wheel-rail rolling contact theory”, Vehicle System Dynamics, 8(4),1979, 317-358.
10. Guo, Y.B. and Schwach, D.W. “An experimental investigation of white layer on rolling contact fatigue using acoustic emission technique”, International J. of Fatigue, 27(9), 2005,
1051-1061.
11. Thakkar N.A., Reuben, R.L., Steel, J.A. “Rail-wheel interaction and defect location using
acoustic emission: a laboratory study”, Mechanical Systems and Signal Processing, submitted, 2007.
354

DAMAGE ASSESSMENT OF GEARBOX OPERATING IN HIGH NOISYENVIRONMENT USING WAVEFORM STREAMING APPROACH
DIDEM OZEVIN, JASON DONG, VALERY GODINEZ and MARK CARLOS
Physical Acoustics Corporation, 195 Clarksville Road, Princeton Junction, NJ 08550

Abstract
Threshold and timing parameters are two fundamental factors that control arrival times andwaveform signatures collected in an acoustic emission test. While this approach works successfully in many applications, recording the complete, continuous waveforms emitted from an AE
test independently of threshold value is more appropriate for some specific applications. Applications such as those with high background noise or complex structures with numerous boundaries and interfaces are better served by continuously streaming with duration in the order of hundreds-of-ms instead of the traditional hundreds-of-s-long AE waveforms. State-of-the-art AE
data-acquisition hardware enables this “AE Waveform Streaming” approach at high sampling
rates throughout a traditional AE test. These waveforms can subsequently be analyzed in realtime to extract traditional AE features in the time and frequency domain.
In this paper, the application of waveform streaming will be shown on detecting corrosion
pitting on gear teeth under a high background noise environment. The tests were performed using an FZG machine to generate pitting corrosion on gear teeth under fatigue. In these tests, arecently designed “integrated” AE-vibration sensor was used for collecting high- and lowfrequency data while monitoring the condition of the gears. Recording the complete, continuous
waveforms provided flexibility during post-processed data analysis, which can be implemented
in real time during consecutive tests. The paper also presents the impact of the waveform streaming approach in terms of understanding threshold effects on waveform signatures, in the ability
to set and analyze different digital filters, and in the extraction of low frequency vibration information.
Keyword: Waveform streaming, threshold independent, gearbox, frequency analysis, real time
analysis
Introduction
Threshold-based data acquisition has been used and operated successfully in many acoustic
emission (AE) applications. Threshold is a key factor for AE technology as the technology ismostly based on arrival times for source location and time- and frequency-domain feature extraction for pattern recognition [1, 2]. However, the threshold-based approach creates a detection reliability problem for high noise, high hit-rate applications, such as monitoring of highly dynamic
systems or process monitoring. Additionally, threshold and timing settings may perturb the direct
AE wave signature, which is substantial for a quantitative analysis and source discrimination [3].
While this approach works successfully in many applications as presented in Table 1, recording
the complete, continuous waveforms emitted from an AE test independently of threshold value ismore appropriate for some specific applications. In this paper, we will present the use of waveform streaming in addition to threshold-based data acquisition. The advantages of streaming
waveform approach are discussed using the gearbox-testing application.
J. Acoustic Emission, 25 (2007)355© 2007 Acoustic Emission GroupTable 1. The comparison of threshold based and waveform streaming approaches.
Threshold BasedApproachCapabilities
Very well applied forflaw detection, locationand many other applications
Not much memory
needed, real time feature
extraction and fast
analysis

Waveform Streaming
Approach

Limitations

Capabilities

In high noisy environments and high hit-rate
applications, the systemsaturates and loses data

Limitations

Complete waveforms
with no distortion due to
DAQ setting

Memory and fastcomputer need forvery long duration
waveforms

Extracting vibration
information for rotatingmachineryTime of arrival extraction can be cumbersome

Approach
The high sampling rate required and the high hit-rate of AE signals are two constraints forthe continuous recording of waveforms. State-of-the-art data acquisition hardware allows recording waveforms continuously with high sampling rates throughout an AE test. The data acquisition system can record long-duration waveforms with high sampling rate continuously or at predefined time intervals. The waveform shown in Fig. 1 has 2-sec. duration sampled with 500-kHz
rate at 5-sec. interval. The waveform presents several transient signals during the 2-sec. period asshown in the zoomed view. This transient signal is an indication of bearing damage. These transient signals have typical AE signal characteristics; i.e., short duration, rise time and decay time.
The waveform streaming approach allows the identification of the transient signal characteristics
without being affected by threshold and timing parameters.

Fig. 1. An example of long-duration waveform (2 sec.) sampled with 500-kHz rate.
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The waveform streaming can be triggered using four different modes:
 Hit-based trigger: When the data acquisition system detects a hit exceeding threshold, the







system records a long duration waveform (longer than the hit duration defined by timing parameters).
Time-based trigger: The system streams waveforms at each time interval. The time-based
trigger is an efficient way to determine the condition of machinery that requires periodic
measure of a machine’s ability to perform efficiently.
Manual trigger: Waveform streaming is activated manually during acquisition.
External trigger: If there is a certain parametric input such as pressure, the waveform streaming is activated as a function of parametric input level.
Continuous mode: Waveforms are streamed throughout an entire AE test.

Recording complete, continuous waveforms provides flexibility during post-processed data
analysis, which can be implemented in real time in consecutive tests, for example:
 Understanding the effect of threshold and timing settings on waveform feature extraction thatcan be used to optimize the settings of threshold-based acquisition for consecutive tests.
 Extracting time-domain features using post-process digital filters and different threshold setting.
 Optimizing frequency segments for spectral feature configuration.
 Extracting low-frequency vibration information if AE waveform length is sufficiently long tocapture multiple revolutions.
Additionally, although the arrival-time information of transient signals is not registered inreal time, single or multiple AE hits in a streamed waveform can be separated with their actual
arrival times using the NOESISTM software. The NOESISTM software has an option to extract
features and arrival times of multiple transient signals in a streamed waveform, based on threshold or length. Furthermore, streamed waveforms of multiple channels can be synchronized sothat the relative arrival times of transient signals of different channels can be extracted or waveforms of different channels can be cross-correlated in order to find arrival-time differences. The
waveform streaming can be acquired using PCI-2 (2-channel) or PCI-8 (4-channel AE, 4-channel
vibration) board developed by Physical Acoustics Corporation [PAC]. It is important to note thatthese boards have the capability to record threshold-based data and waveform streaming concurrently. A successful application of waveform streaming together with threshold-based approach
was demonstrated on the condition monitoring of a rolling-element bearing [4]. In this paper, theapplication of the approach on the detection of pitting corrosion growing on gear teeth due to fatigue is demonstrated.
Gearbox Application
As an example of the flexibility offered by the waveform-streaming approach for practical
AE monitoring, a series of tests were performed at the Ohio State University Gearbox Laboratory using an FZG machine to generate micro-pitting or spalling in spur gears at elevated temperatures. The temperature at steady state reached 90°C. The rotation speed of the input gear was
1440 RPM with 220 N·m torque. The input gear had 26 teeth; the output gear had 17 teeth. The
first data set included a gear with no damage at the beginning and end of 1-hr duration testing.
The second data set included a gear with damage at the beginning of the testing and the damage
grew over the 1-hr test duration. The third data set included a gear with no damage at the beginning of the testing and the damage (pitting) formed and grew over a 6-hr test. In these tests, AE
357

and vibration data was collected continuously. The first and second tests were used to form aclassifier for background noise and pitting emission; the third test was used to determine if theclassifier could detect the pitting formation. Table 2 is a summary of the tests and the damage
states of the specimens at the beginning and end of the tests.
Table 2. The initial and end conditions of the test specimens.
Test No

Duration (hr)Initial StateEnd State

1

1

Undamaged2

1

Damaged3

6

Undamaged

No visual damage
Damage grew considerably
Small pitting initiated

The gear pitting formation under fatigue was monitored using two integrated AE-vibration[AE/VIB] sensors recently developed by PAC [5]. The sensors were mounted on the front andside panel of the FZG machine (shown in Fig. 2b). AE/VIB 1 was mounted below the lubricant
level of the machine and AE/VIB 2 was mounted at the same level as the lubricant. A PCI-8
board was used to record the data from two AE/VIB sensors. The board has 16-bit A/D conversion capability and 1-MS/s waveform-streaming rate. The board was installed inside a PAC μSamos chassis, and this DAQ system was controlled by a laptop as shown in Fig. 2c.

Fig. 2. Photographs of (a) input and output gears, (b) AE/VIB sensors, (c) DAQ system.
AE waveforms having 100-ms duration were collected every second. The sampling rate for
AE waveforms was 1 MHz. The band-pass analog filter setting was 20 kHz to 400 kHz. The AE
sensor gain was 40 dB.
358

Extracting features using different sets of digital filters and spectral divisions
Test 1 included a gear with no damage at the beginning and end of 1-hr test. Test 2 included
a gear with a minor damage at the beginning of the testing and the damage grew over the 1-hrtest. The same gear but opposite teeth was used at Test 1 and Test 2. Fig. 3 shows the energy
trends of two tests recorded by the AE/VIB 1. The fluctuation was because of water pump operation to cool the lubricant inside the gearbox. While the same gear was used during the tests, thetime-domain signal level changed because the gearbox was opened and the opposite side of thegear teeth, which had a minor damage tooth, was mounted. Variation of time-domain feature due
to mechanical feature indicates the difficulty of comparing one test to another if the mechanicalstructure has any slight modification. Spectral features gain significance as descriptors to differentiate damage emissions and background noise emissions.

Fig. 3. Time-domain feature, energy (e.c.), trends for two tests recorded for ~3600 sec. The fluctuations were generated due to the operation of water pump inside the gearbox.
Figures 4a and 4b show examples of a raw, streamed waveform and its frequency spectrum.
The frequency spectrum of the raw waveform, Fig. 4b, indicates the dominant frequency components to be due to mechanical noise at 22 kHz and 33 kHz. If frequency centroid or peak frequency were calculated directly using a raw waveform, the mechanical noise frequencies would
mask the result. When a waveform is streamed from an AE sensor, it is expected that lowfrequency mechanical noise and high-frequency damage growth emission would be summed.
Extracting high-frequency, relatively low-amplitude (as compared to mechanical noise) damagegrowth emissions, requires filtering background noise frequencies. Figure 4c shows the same
waveform with 80-kHz high-pass filter using 10th-order Butterworth filter. Figure 4d shows thefrequency spectrum of the segmented section between two green lines as shown in Fig. 4c. The
peak frequency of complete waveform is dominated by low frequencies (<50 kHz). Digital filtering and segmented frequency spectrum provide information about the content and occurrencetime of high-frequency transient signals. As shown in Fig. 4d, the segmented section of AE
waveform has the frequency peak at 240 kHz together with 120 kHz and 300 kHz frequency
components.
359

The software allows segmenting (green lines) the time-domain waveform for frequency spectrum calculation. Spectral features, such as peak frequency, partial powers (partial powers indicate how the energy distribution at defined frequency segments changes as compared to the fulldefined spectrum band) can be extracted using digital filters.

Fig. 4. A streamed waveform of Test 2 (a) raw waveform and (b) its frequency spectrum (0-500kHz), (c) filtered waveform having 80 kHz high-pass and (d) its segmented (between green lines
in c) frequency spectrum (0-500 kHz).
Applying pattern recognition to determine pitting corrosion emissions during Test 3
In this application, AE features were extracted from streamed waveforms using 35-dB
threshold, complete waveform length and 80-kHz high-pass, 10th-order Butterworth filter. In theinitial set, the features extracted from Test 1 and Test 2 waveforms were amplitude, absolute energy, frequency centroid, peak frequency and partial powers. Partial powers indicate how the energy distribution at defined frequency segments changes as compared to the entire spectrum
band. Energy values at individual frequency segments are normalized to the entire spectrum
band. In this application, the entire spectrum band was chosen as 80 to 400 kHz segmented into
three parts, 80-100 kHz, 100-200 kHz and 200-400 kHz. In pre-processing, AE features werenormalized using unit variance. Principal component analysis was performed. The max-min distance method with Euclidian distance and 0.8 distance threshold were the parameters of the unsupervised pattern recognition method. Different sets of AE features were used to optimize theclustering result using R and Tou criteria [6]. Peak frequency, PP1 and PP2 resulted in the most
optimal set of AE features. As mentioned above, time-based features are sensitive to mechanicalvariation from one test to another while there is no damage growth. The unsupervised algorithm
identified three clusters having one cluster further apart than two clusters. In our interpretation,
we called the farthest cluster as “damage AE” (detected mostly during Test 2, in which pitting
corrosion occurred), and the other two clusters as two distinct background noise sources.

360

The classifier was used to identify if pitting developed on the gear teeth used in Test 3. In
Test 3, no tooth had pitting at the beginning of the test; however at the end of the fatigue test,
some teeth show pitting as shown in Fig. 5. Therefore, the classifier would have been expected tofind some data points corresponding to the “damage AE” cluster. The data points were preprocessed and classified. Figure 6 shows the result of the last 40 minutes of the data.

Fig. 5. The end state of the gear used in Test 3.

Fig. 6. The classification result of Test 3 using the classifier formed above. a) PCA analysis foraxes 0 and 1; b) PCA analysis for axes 0 and 2; c) Classified hits vs. time for Damage (red),
Noise 1 and 2 (blue and green); PCA analysis for axes 1 and 2.

361

As discussed above, principal component analysis (PCA) was conducted to minimize thenumber of AE features used for the classifier formation. Three PCA axes were selected to represent the data sets. Figure 6a shows the scatter plot of PCA 0 and PCA 1 axes after the classifier
was applied to the usage set (Test 3 data). The 2-D distributions of PCA 0 versus PCA 1 (Fig.
6a) and PCA 1 versus PCA 2 (Fig. 6d) indicate distinctly the separation of “damage AE” classfrom background noise classes. The 2-D distribution of PCA 0 versus PCA 2 has significant
overlap of “damage AE” and “noise 2” classes. However, the use of three principal component
axes provides multidimensional analysis to discriminate class centers and classifies unknown hitclass accurately. Figure 6c shows the AE accumulation of three classes. Two background noiseclasses were consistently detected during the testing. However, “damage AE” activities were detected at random time intervals as expected from a corrosion-pitting source, which grows
throughout a fatigue loading test.

Fig. 7. Extracting gear-mesh frequency using a long duration AE waveform, (a) AE waveform,(b) Average Signal Level (ASL) trend, (c) ASL spectral energy.
Extracting gear-mesh frequency
Vibration monitoring detects coupling failure by comparing two different states (undamaged
versus damaged) of structural components. The most fundamental challenge for detection of
cracking is that it is typically a local phenomenon and may not significantly influence the lowerfrequency global response of a structure that is typically measured during vibration tests [7].
However, vibration monitoring provides additional information to the findings of AE method. It
is advantageous to extract vibration information using long-duration AE waveforms.
The gear-mesh frequency is the combined effect of all gear inertia. In these tests, the input
gear rotation speed was 1440 RPM, which corresponds to 24-Hz frequency. The input gear has
26 teeth, which correspond to gear-mesh frequency of 624 Hz. Figure 7a shows a long-duration
AE waveform acquired in Test 2. The time-variant Average Signal Level (ASL) is extracted
from this streamed waveform so that high frequency noise is filtered out as shown in Fig. 7b.
362

ASL spectral energy is further calculated based on the time-variant ASL and its maximum spectral energy that just lies in the gear-mesh frequency as shown in Fig. 7c. ASL spectral energy
plot indicates that the streamed AE waveform can be effectively used to extract a gear-mesh related feature that is not available with the conventional AE signal processing technique.
Summary
Waveform streaming, recording complete, continuous waveforms, provides flexibility during
post-test data analysis, such as, understanding signal characteristics without being influenced bythreshold and timing parameters, and identifying damage emission frequency information. Additionally, governing vibration frequencies can be extracted using AE sensor if AE waveform
length is sufficiently long to capture multiple revolutions. Waveform streaming can also be used
to optimize the settings of threshold-based data acquisition.
Acknowledgements
This study was performed as a part of SBIR Phase II program sponsored by NAVAIR undercontract number N68335-04-C-0202. We would also like to thank Professor Ahmet Kahraman
and his graduate student Jeremy Bluestein for their help during testing in the Ohio State University Gearbox Laboratory.
References
1. V.F. Godinez, F. Shu, R. Finlayson, B. O’Donnell, Proceedings of SPIE, Health Monitoringand Smart Nondestructive Evaluation of Structural and Biological Systems III, 2004, Vol. 5394,
pp. 428-437.
2. V.F. Godinez, F. Shu, R. Finlayson, B. O’Donnell, Anastasopoulos, A., and Tsimogiannis, A.,
Canadian Institute for NDE (CINDE) Journal, 25, 2004, 8-16.
3. M. Ohtsu and K. Ono, J. of Acoustic Emission, 5, 1986, 124-132.
4. S. Prieto, MSc Thesis, School of Engineering, Cranfield University, 2006.
5. V.F. Godínez-Azcuaga, D. Ozevin, A. Anastasopoulos, A. Tsimogiannis, Aging Aircraft Conference, 2007, in publication.
6. Anastosopoulos, A., Philippidis, T.P., “Clustering Methodology for the Evaluation of Acoustic
Emisión from Composites,” J. of Acoustic Emission, 13(1-2), 1995, 11-22.
7. C.R. Farrar, S.W. Doebling and D.A. Nix, Philosophical Transactions of the Royal Society A:
Mathematical, Physical and Engineering Sciences, 359, 2001, 131-149.

363

CLUSTERING ANALYSIS OF AE IN ROCK
N. IVERSON, C.-S. KAO and J. F. LABUZ
Department of Civil Engineering, University of Minnesota, Minneapolis, MN 55455

Abstract
Clustering analysis, through a fractal dimension, offers quantitative information with regard
to the spatial distribution of AE events, which can be used to identify the characteristic of the
failure process in quasi-brittle materials. For example, an inspection of AE locations from a mechanical test on rock can be interpreted using the fractal dimension: three represents random
events occurring in a volume while a fractal dimension of two signifies that the events have localized on a plane. A clustering algorithm is used to analyze laboratory tests performed on
specimens undergoing tensile or shear-type failure. Because failure of rock involves localizationof deformation in the form of microcracks, the location of AE can be used to track the failure
process, and the fractal dimension provides a basis for determining localization.
Keywords: Fractal dimension, clustering analysis, localization, strength testing
Introduction
A common feature of failure in a quasi-brittle material such as rock is the development of
microcracking, which releases energy in the form of elastic waves called acoustic emission (AE).
The AE technique can be used to monitor the evolution of damage, through the entire volume, atvarious stages of loading. The coordinates of an acoustic source can be obtained from the arrival
times of the P-wave at the receivers. The locations of AE provide a picture that forms a basis forthe justification of mechanical models of damage and failure (Shah and Labuz, 1995; Ono,2005).
Specimens of rock were tested in a closed-loop, servo-hydraulic load frame and the data from
LVDTs, strain gages, and load cells were stored on a computer and time stamped for synchronizing with AE events. Eight piezoceramic AE sensors, connected to preamplifiers with bandpass
filters from 0.1 – 1.2 MHz, were bonded to a specimen using a modified alkyl cyanoacrylate adhesive. One sensor was used as a trigger, with a threshold of 7 mV after amplification at 40 dB
gain. The AE signals were recorded using four, two-channel high-speed digitizers, at 20 Msamples per second with a 100-μs recording window, and stored for later analysis. A pre-trigger region, set at 50 μs, was included to ensure capture of the first arrival. Various algorithms foranalysis of AE (e.g. first arrival detection, locations, source characterization) were developed andused to interpret failure of rock.
AE Locations
The unknowns for a three-dimensional location, assuming that the P-wave velocity is known,
are the three AE source coordinates (x, y, z) and the time of source inception (to). However, thequadratic nature of the distance equation requires a minimum of five receivers to remove any
ambiguity in optimizing the source location. The optimization is achieved by minimizing theresidual  in a least-squares sense using the Levenberg-Marquardt technique:J. Acoustic Emission, 25 (2007)

364© 2007 Acoustic Emission Group

2

2
2
2
(
)
(
)
(
)
x
x
y
y
z
z

+

+


i
i
i
(1)
2 =
+ to  ti 


c

i =1



where xi, yi, and zi are the coordinates of the ith sensor; c is the P-wave velocity; and ti is the arrival time at the ith sensor (e.g. Salamon and Wiebol, 1974).
# _ of _ Sensors

To find the relative arrival time, the waveform was checked to see when the signal passed adesignated threshold value, which was determined from the mean amplitude and standard deviation of the noise during the pre-trigger period. The threshold was set at three times the standard
deviation from the mean. When the threshold value was exceeded, the point was stored as thetrigger point. The data were scanned preceding the trigger point until the point in which thechannel’s mean value was obtained; this point was then used for the potential arrival of theevent. The next 120 points were checked to see if the trigger threshold was crossed three moretimes during that interval. If the threshold had indeed been exceeded three subsequent times, thepoint was then stored as the arrival time. About 65% of the total events were located within 5
mm error. The error for each event was calculated as the average difference between the distances from the calculated location and the estimated arrival times.
Clustering Analysis
One way of describing a geometric figure is by its fractal dimension, which can be thought ofas the number of variables required to describe a shape. For example, a line has a fractal dimension of one, a plane has a fractal dimension of two, and a volume has a fractal dimension of three(Mandelbrot, 1983). The use of fractal analysis to monitor the evolution of the failure process isreasonable because a fractal dimension of three represents random events occurring in a volume,
and a fractal dimension of two signifies that the events have localized on a failure plane (Hirata
et al., 1987; Smalley et al., 1987).
The process used to obtain the fractal dimension is the correlation integral, which compares
the proximity of events to one another:2(2)
C (r ) =N R (R < r)
N ( N  1)
where NR(R < r) = the amount of pairs (pi, pj) with a distance smaller than r (e.g. Knox, 1963;
Williams, 1983). The correlation integral can then be used to calculate the fractal dimension(FD):
C (r ) = r FD(3)
By varying the value for r, a plot can be made to represent the changing value of the correlation
integral versus the distance r. The fractal dimension is the slope of the function on a log-log
plot, as shown in Fig. 1.
To demonstrate the clustering analysis, three sets of 100 data points, which can be thought ofas AE locations, were placed randomly (i) in a volume (Fig. 1a), (ii) on the surface of a sphere(Fig. 1b), and (iii) along a line (Fig. 1c). The clustering analysis was performed, and the corresponding fractal dimensions were 3.0, 2.0, and 1.0, respectively. In laboratory tests associatedwith failure of rock, a common feature is a plane of fracture or shear band, so the fractal dimension offers the possibility to identify localization of deformation.
365

(a)

(b)

(c)

Fig. 1. Points randomly distributed (a) in a volume, (b) on a surface of a sphere, (c) along a line.

366Simulated Failure Plane
One hundred events were spread randomly across a 50-mm plane (vertical range = 0) and thefractal dimension was calculated to be 2.0. This was then done again with a different verticalvariance to simulate AE location error, the tortuousity of a crack, or the thickness of a shear
band. This variance was created by assigning a random vertical component to each point, andcalculating the fractal dimension. The vertical variance was increased in 5-mm increments untilthe vertical variance was equal to that of the width (50 mm) and a cube was formed. The results
are shown in Fig. 2.
With the points in a plane, the fractal dimension was 2.0 (Fig. 2a). Figures 2b and 2c represent a vertical range of 5 and 20 mm, respectively, and the fractal dimensions were 2.15 and2.70. Thus, for a location error of about 5 mm, a fractal dimension of 2.2 signifies that failure
has localized along a planar feature. As the vertical range increased to the height of the specimen, 50 mm, the locations were spread throughout the volume and the fractal dimension was 3.0(Fig. 2d). From 25 – 45 mm vertical range, the fractal dimension of 2.9 does not change appreciably (Fig. 3).

Fig. 2. Points randomly distributed across a plane.
(a) vertical range = 0. (b) vertical range = ±2.5 mm.
367

Fig. 2. Points randomly distributed across a plane.
(c) vertical range = ±10 mm. (d) vertical range = ±25 mm.

Fractal dimension

3.0

2.5

2.0

1.5

1.0
0

10

20

30

40

50

Vertical variance (mm)Fig. 3. Results of FD change with respect to variance of points on a plane.
368

It has been documented that the initiation of tensile failure is associated with a damage zoneof concentrated microcracks, and an intrinsic zone of finite width is indentified (Labuz andBiolzi, 1998); a distinct fracture does not form until further deformation into the post-peak regime. For shear failure in compression, a shear band is formed by a region of inelastic processes
such as microcracking and porosity increase within a thickness of 5 – 10 grain diameters (Riedel
and Labuz, 2007). Thus, for both tensile and shear type failure, a fractal dimension greater than
2.0 may be realized upon localization because of the failure processes.
Experimental Results
Three Point Bend Test
The first series of experiments involved initiation and propagation of tensile failure throughmode I fracture. Smooth boundary (no notch) specimens of Serena sandstone were loaded in athree-point bend configuration. The beams were cut to a length of 310 mm, a width of 80 mm,
and a thickness of 32 mm; the distance between supports was 300 mm. A strain-gage based displacement transducer with a gage length of 20 mm was placed at the center position to measure
the crack mouth opening displacement (CMOD), which was used as the control signal within theclosed-loop, servo-hydraulic load frame.
(a)

-40

(c)

-40

(b)

90% - 100%

-20

0

20

40

(d)

90% - 80%

-20

-40

0

20

40

-40

100% - 90%

-20

0

20

40

0

20

40

80% - 70%

-20

Fig. 4. AE events in beam with no notch at different load ratios: (a) pre-peak 90 – 100%;(b) 100 – 90% post-peak; (c) post-peak 90 – 80%; (d) post-peak 80 – 70%.
Figure 4 shows the located events with error <5 mm at different stages of loading, as indicated by the % of peak load called the load ratio. A migration of AE locations with loading wasobserved and the AE tracked the propagation of the fracture. Because of the absence of a stressconcentrator—the boundary of the beam was smooth—only 18 events were located between the
90 – 100% load ratios (Fig. 4a). Nevertheless, a region of localization, which may be related to
an intrinsic length of the rock, was evident (Labuz and Biozli, 1998). A fractal dimension of 2.3
was calculated (Fig. 5) for mode I fracture initiation. As CMOD was increased beyond peakload, 152 events were located between the 100 – 90% load ratios (Fig. 4b). The fractal dimension remained at 2.3, even though the post-peak regime is essentially controlled by crack propa369

gation. From the load ratios of 90 – 80%, the located AE events increased to 239 (Fig. 4c), andfractal dimension decreased to 2.2 (Fig. 5). The AE locations showed greater activity near thetip. Between the 80 – 70% load ratios, the located AE events numbered 329 and the length outlined by AE increased (Fig. 4d); the fractal dimension was slightly less than 2.0.

100

3.0

90

2.5

80

2.0

70

1.5

Fractal dimension

Load ratio (%)

The clustering analysis showed that the fractal dimension started at about 2.3, certainly influenced by the gradient of axial stress due to bending, but localization was identified independently through a speckle interferometry technique. Crack propagation was responsible for the decrease in the fractal dimension in the post-peak regime.

Load ratio
Fractal dimension

60
0.01

1.0
0.02

0.03
CMOD (mm)0.04

Fig. 5. Load history near peak and fractal dimension for beam with no notch.
Biaxial Test
A plane-strain (biaxial) compression apparatus (Drescher et al. 1990; Riedell and Labuz2007) was used to examine shear banding in Berea sandstone. The apparatus allows the failure
plane to develop and propagate in an unrestricted manner by attaching a low friction linear bearing to the upper platen. Plane-strain deformation is enforced by a thick-walled steel cylindercalled the biaxial frame. A significant number of events (778) were located with < 5 mm error atvarious stages of loading (Fig. 6).
The clustering analysis was applied to the biaxial experiment by considering the AE locations in groups of 70 events (Fig. 7). From load ratios of 95 – 98% (lateral displacement of
0.085 – 0.093 mm), 219 events were located and the fractal dimensions were 2.64, 2.86, 2.59.
The value of 2.6 was associated with inelastic deformation, but it is interesting to note a slightincrease of the fractal dimension to 2.86 at a lateral displacement of 0.088 mm prior to peak load.
From 98 – 100% peak load (lateral displacement of 0.093 – 0.101 mm), the fractal dimension
dropped to 2.49 (Fig. 6b); a loss of homogeneous deformation was also recorded by the lateral
LVDTs. In the post-peak region, the fractal dimension remained at 2.49 (Fig. 6c) and decreased
to a value of 2.36 (Fig. 6d), before terminating the test. An examination of the specimen after
the experiment showed a localization of deformation in the form of a shear band (Riedell andLabuz, 2007).
370

(a)

(b)

95% _ 98%(c)

98% _ 100%(d)

100% _ 95%

95% _ 93%

Fig. 6. AE events in a biaxial specimen at different load ratios: (a) pre-peak 95 – 98%;(b) pre-peak 98 – 100%; (c) post-peak 100 – 95%; (d) post-peak 95 – 93%.

Fig. 7. Load history and fractal dimension for the biaxial test.
Conclusions
This research was focused on the development of numerical methods to analyze AE data.
Specifically, a location algorithm for estimating the source hypocenter was used to map the failure process of rock under tensile and shear type loading. A clustering analysis based on fractal371

theory provided a quantitative method for describing the transition from random microcracking
to localized failure.
The distribution of events within an increment of load has a fractal structure that can be characterized by a fractal dimension, a reduction of which indicates clustering of hypocenters. A parametric study was used to provide a visual interpretation of a fractal dimension between three(random events) and two (localization of events on a plane), while a series of simulated failure
planes with different vertical variance was used to test the sensitivity of the fractal dimension.
The algorithms developed were used to interpret AE data from various experiments on rock. The
results for the beams with a smooth boundary (no notch) showed that near peak load the fractal
dimension was about 2.3, and approached 2.0 in the post-peak phase of loading as crack propagation occurred. Confined, plane strain compression tests involving shear-type failure showedthat the fractal dimension was approximately 2.5 at peak load, and as the test progressed andshear banding developed, the fractal dimension decreased to around 2.4.
Acknowledgements
Partial support was provided by NSF Grant Number CMS-0070062 and the GAANN program of the Department of Education.
References
1. A. Drescher, I. Vardoulakis, and C. Han, Geotech. Testing J. ASTM 13, 1990, 226-234.
2. T. Hirata, T. Satoh, and K. Ito, Geophysical J. of Research, Astronomical Society, 90, 1987,
369-374.
3. G. Knox, British J. of Preventive and Social Medicine, 17, 1963, 17-24.
4. J.F. Labuz and L. Biolzi, Int. J. Solids Struct., 35, 1998, 4191-4204.
5. B.B. Mandelbrot, The fractal geometry of nature, W.H. Freeman, 1983, 468 p.
6. K. Ono, J. Strain Anal. Engng Design, 40(1), 2005, 1-15.
7. J.J. Riedel and J.F. Labuz, Int. J. Num. Anal. Meth. Geomech. 31, 2007, 1281-1299.
8. M.D.G Salamon and G.A. Wiebol, Rock Mech., 6, 1974, 141-166.
9. K.R. Shah and J.F. Labuz, J. Geophys. Res., 100 (B8)2/3, 1995, 15527-15539.
10. R.F. Smalley Jr., J.-L. Chatelain, D.L. Turcotte, and R. Prevot, Bulletin of the SeismologicalSociety of America, 77(4), 1987, 1368-1381.
11. G.W. Williams, Time-space clustering of disease, in Statistical Methods for Cancer Studies,
R.G. Cornell, ed., New York, 1984. Marcel Dekker, Inc.

372

IMPACT IMAGING METHOD TO MAP DAMAGE IN CONCRETE
BRIDGE DECK SLABSSTEPHEN D. BUTT, VIDYADHAR LIMAYE1 and JOHN P. NEWHOOK2
Faculty of Engineering, Memorial University, St. John’s, Newfoundland and Labrador, Canada;
1
MacDonnell Consulting Group Limited, Halifax, Nova Scotia, Canada;2
Dept. of Civil & Resource Engineering, Dalhousie University, Halifax, Nova Scotia, Canada

Abstract
An acoustic impact imaging method was developed to map damage in concrete bridge deckslabs and was evaluated by conducting a survey on a damaged full-scale bridge deck model. The
imaging system consisted of a mechanical hammer source, two acoustic sensors and a digital
waveform recorder. Imaging surveys were conducted along transverse lines that resulted inacoustic measurements on a 30-x-30-cm grid covering the entire upper surface of the slab.
Acoustic measurements were processed to determine relative attenuation and then displayed using spatial imaging software. The resulting images showed increased levels of attenuation aroundpunch-out failure zones and coinciding with many of the cracks mapped on the upper deck surface. Overall, the imaging technology showed good potential as a means of rapidly and nondestructively mapping damage in concrete bridge deck slabs.
Keywords: Impact imaging method, damage survey, concrete bridge deck inspection, structural
health monitoring, acoustic transmission technique, acoustic attenuation.
Introduction
A simple and robust acoustic impact method was developed to identify damage in concrete
bridge deck slabs by measuring the relative signal attenuation between two acoustic sensors [1].
This technique was applied to monitor progressive damage during fatigue testing of a full-sizedmodel of a bridge deck slab at the Heavy Structures Laboratory at Dalhousie University, Halifax,
Canada. Testing ended with punch-through failure of the deck and details of the pre-failure
acoustic monitoring are presented in related publications [1-3]. This paper is focused on the related Impact Imaging Method (IIM), which was developed for post-failure mapping of damage inthe bridge deck model. The IIM is an improvement over many existing acoustic impact methods,
which have been used for several decades to map damage in engineering structures [e.g., 4],since it incorporates image generation to enhance damage resolution. The objective of this work
was to evaluate the potential of the IIM to provide reliable mapping of bridge deck slab damage
to aid inspection of bridges in-service.
The bridge deck slab model measured 12.2 m x 3.0 m on plan and was divided internally into
four 3.05 m long panels along its length, each with varying internal reinforcement. Panel A wasthe control panel with no internal reinforcement while Panel B was similar to Panel A, but with a
75-mm high crack trainer midway along its length. Panel C had a bottom mesh of GFRP crack
control reinforcement, and Panel D had a bottom mesh of steel crack control reinforcement. Eachpanel was tested under step-wise increasing fatigue loading to simulate wheel loading in-service
until punch-through failure of the panel by the formation of a punch shear cone beneath the loading pad. Throughout testing, internal and external measurements of load, displacement and strain
were made, and detailed mapping of visible cracks were made at regular intervals (Fig. 1). After
J. Acoustic Emission, 25 (2007)373© 2007 Acoustic Emission GroupFig. 1. Crack patterns mapped on the top and bottom surfaces of Panel A after punch-throughfailure. The punched-out conical slab is shaded in gray.
all 4 deck slab panels had failed, the IIM was used to map damage over the full area of the deckusing the methodology described in the next section. Further details regarding the deck slab
model fabrication, configuration, instrumentation and testing procedures are provided in related
publications [5, 6].
Acoustic Survey Method
Acoustic System and Survey Procedure
The acoustic system consisted of a mechanical acoustic source, two acoustic sensors, a PCbased acoustic signal digitizing and recording system, and custom data processing software [1].
For the damage survey, the acoustic source was a ball-peen hammer that was struck onto thinsteel plates, measuring approximately 7.5 x 7.5 cm, that were mounted on the side of the deck
model at mid-height using quick-setting epoxy. The acoustic sensors were Physical Acoustics
Corp. R6I acoustic transducers with a frequency range of 25 to 110 kHz, and were mounted on
the upper deck utilizing vacuum grease as an acoustic couplant.
Survey Procedure
The acoustic survey was conducted over the top surface of the deck on survey lines oriented
along the transverse direction, i.e., along the short dimension of the deck slab. The first survey
line was situated 30 cm from the transverse edge of Panel D and adjacent lines were spaced at 30
cm along the longitudinal direction. Details of the survey procedures along each transverse line
are given in Fig. 2. Along each survey line:a) One acoustic sensor (reference sensor) was fixed at a position 30 cm from the edge of thepanel.
b) The second sensor (active sensor) was positioned at a position 30 cm further along the survey
line from the reference sensor.
c) The hammer source was struck on the steel plate, resulting in the recording of the same
waveform as it propagated past the reference and active sensors. Multiple hammer strikes
were made and later averaged during data processing.
d) The active sensor was moved to a new position 30 cm further along the survey line and Stepc) was repeated.
374e) Step d) was repeated until the active sensor was positioned in its final location at the longitudinal centerline of the deck slab.
Steps a) through e) were then repeated for the next transverse survey line. When the survey wascompleted along one side of the deck, the survey was repeated on the other side of the deck withthe final active sensor positions also ending at the longitudinal centerline of the deck. At thecompletion of this procedure a reference or active sensor had been positioned at every point of a30-x-30 cm grid pattern covering the entire upper surface of the deck.
Acoustic Data Processing
The acoustic waveform magnitude measured by each sensor for the hammer strikes wascomputed by summing the positive area under the recorded full waveform to get the integrated
amplitude IAmp. The normalized magnitude M of each recorded waveform was then determinedby:IAmp a(1)
M =IAmp rwhere the subscripts a and r denote the active and reference sensors, respectively. The dataset of
M values was then analyzed to evaluate the standard fall-off of acoustic signal magnitude Fs during propagation from one sensor position to the next in undamaged regions of the deck:M(2)
Fs = n +1
Mnwhere the Mn and Mn+1 are the normalized waveform magnitudes at two sensor positions spaced
30 cm apart along the same transverse survey line with little or no damage between them. Figure
3 shows the standard fall-off values resulting from this analysis. Note the anomalous fall-off values from 0.6 m to 1.2 m are what are attributed to the focusing of the acoustic waveform when
propagating from the thickened haunches to the thinner deck slab. The relative attenuation
Attenrel of the waveform when propagating through the deck slab was then computed as:Fs  ( M n +1 M n )(3)
Fs
where the Mn and Mn+1 are defined above and Fs given as Eq. (2). Thus, the relative attenuation
represents the decimal percentage that the actual signal fall-off is less than the standard fall-off
i.e. when there is no damage along the wave travel path.
Attenrel =Fig. 2. Schematic of acoutsic survey procedure along a single transverse survey line.
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Fig. 3. Values of standard fall-off used to detremine relative attenuation. The distance shown iswith respect to the edge of the deck model.
Image Generation
Image generation was based on the presentation of the relative attenuation values as a 2-D
plan image of the deck slab. The relative attenuation values were assigned 2-D coordinates,
which were 15 cm from the active sensor position along the transverse survey line in the direction of the reference sensor. Global variogram analysis was conducted to generate a suitablesemi-variogram model for the relative attenuation data and standard point estimation methods
using ordinary kriging [7] were used to estimate the relative attenuation values on a 15-x-15 cm
grid covering the full plan of the deck model. Industry-standard geostatistical modeling software
was then used to generate the contoured relative attenuation images.
Figure 4 presents a plan composite photograph of the deck model showing the cracks mappedon the upper surface of the slab and the upper trace of the punch-through failure zone for eachpanel. For comparison, the relative attenuation image is given in Fig. 5. Note the good correlation between damaged and cracked regions of the deck and the regions of highest attenuation.
Discussion
As stated above, there is good correlation between the regions of highest acoustic attenuation
shown in Fig. 5 and many of the cracked and damaged regions visible on the upper deck surface
shown in Fig. 4. For example, each of the four punch-through failure zones is surrounded by aregion of greater than 16% relative attenuation, which increases to up to 40% relative attenuation
immediately adjacent to the failure zone. As well, most of the longitudinal cracks and othercracks oriented at less than 45° to the transverse survey direction are imaged as zones of greaterthan 16% relative attenuation. The cracks that are not imaged well include the discontinuous
low-aperture longitudinal crack running from Panel A to Panel D on the upper side of Fig. 4 andmost of the transverse oriented cracks. This makes sense since the transverse cracks are in a direction parallel to the survey direction and thus are not generally crossed by the acoustic wave
raypath between the sensors during the survey.

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Panel A

Panel B

Panel C

Panel D

Fig. 4. Composite photograph looking down onto the upper surface of the deck model after
punch-through failure of all four panels. The regular pattern of sensor positions can be partiallyseen as the regular grid pattern of dark spots. The legend is to the right.

Panel A

Panel B

Panel C

Panel D

Fig. 5. Acoustic attenuation image of the bridge deck model. The image is oriented the same as
Fig. 4 and the legend is to the right.
A final observation from these imaging results is that there are no regions with high levels of
relative attenuation that do not have corresponding damage. In other words, the acoustic imaging
technique does not generate false positives indicating damage where there is none. From a structural health point of view, this feature of the technique is as significant as its ability to correctly
identify locations where damage is present.
Conclusions and Recommendations
Overall, the Impact Imaging Method has demonstrated good potential to image damage due
to cracks in concrete bridge deck slabs. Cracks are imaged best when they are oriented perpendicular to the direction of the acoustic survey and worst when they are oriented parallel. Equallyimportant, the imaging technique does not falsely identify damage when there is none. Both ofthese factors highlight the robustness of the imaging technique and its potential to contribute to
the non-destructive evaluation and structural health monitoring of concrete bridge deck slabs.
Based on these promising results, several recommendations can be made for further research anddevelopment with respect to this technology. From an operational point of view, recommendations for future work include:• Acoustic surveys should be run in multiple directions to ensure that all cracks are imaged
and, at a minimum, each area should be covered by two perpendicular survey orientations.
• Investigate the use of rolling acoustic sensors and sources to enable surveys to be done
more rapidly than the method described in the paper.
• Investigate the impact of asphalt layers on imaging the underlying concrete slab.

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Acknowledgements
This investigation was funded by Intelligent Sensing of Innovative Structures (ISIS) Canada
and the National Science and Engineering Research Council (NSERC).
References
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Evaluating Fatigue Damage in Concrete Bridge Deck Slabs, ACI Structural Journal. 101, 1,
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2. S.D. Butt and V. Limaye. Damage Detection in Concrete Bridge Deck Slabs using Acoustic
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3. S.D. Butt, V. Limaye and D. Connors. Imaging damage in concrete bridge deck slabs using
acoustic attenuation, in Proc. 31st CSCE Conference, ed. N.B. Moncton, June, 2003, 14061414.
4. R. Schroeer, Acoustic Impact Technique, Non-destruct Test, 3(3), June, 1970, 194-6.
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