JOURNAL OF ACOUSTIC EMISSION
Editor: Kanji Ono
Associate Editors: A. G. Beattie, T. F. Drouillard, F. C. Beall and M. Ohtsu
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 and summaries 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 Literature section will collect the titles and abstractsof papers published elsewhere and those presented at meetingsand conferences. Reports and programs of conferences andsymposia will also be presented.
E. 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.

AE and will advise and assist the editors on the publication
policies and other aspects. The Board presently includes thefollowing members:
S.H. Carpenter
D.A. Dornfeld
P. Fleischmann
L. Golaski
M.A. Hamstad
H.R. Hardy, Jr.
R. HillP. JaxT. Kishi
O.Y. Kwon
J.C. Lenain
S.L. McBride
C.R.L. Murthy
H. Niitsuma
A.A. Pollock
T.M. Proctor, Jr.
I. RomanC. Scala
C. ScrubyA. Vary
E. Waschkies
M. Wevers
J.W. Whittaker
B.R.A. Wood

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(USA)
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4. Publication
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Hall, University of California, Los Angeles, California 900951595 (USA). tel. 310-825-5233. FAX 818-990-1686. e-mail:ono@ucla.edu

2. Endorsement
5. Subscription
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publication of Journal of Acoustic Emission.
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The Editor and Associate Editors will implement the editorial
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tors on any major change. The Editor, Professor Kanji Ono, has
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No advertisement will be accepted, but announcements forassist the review processes as lead reviewers. Mr. T.F. Drouillard
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Editorial Board are selected for their knowledge and experience on
ISSN 0730-0050 Copyright©2000 Acoustic Emission Group - CODEN: JACEDO- All rights reserved.

Journal of Acoustic Emission
Contents of Volume 18, 2000
Selected papers from “EWGAE 2000, 24th European Conference on
Acoustic Emission Testing”, published by CETIM, Senlis, FranceStudies of the non-linear dynamics of acoustic emission generated in rocks by Z.
Majewska and Z. Mortimer (Institute of Geophysics, University of Mining andMetallurgy - Poland)1

Acoustic emission as result of tensile and shearing processes in stable and unstablefracturing of rocks by J. Pininska (Warsaw University - Poland)

8

Relation between acoustic emission signals sequences induced by thermal loading andthe structure of sedimentary rocks by B. Zogala, and R. Dubiel (University ofSilesia - Poland)

15

Acoustic emission/Acousto-Ultrasonic data fusion for damage evaluation in concreteby A. Tsimogiannis, B. Georgali, and A. Anastassopulos (Envirocoustics S.A. Greece)

21

Concrete Crossbeam Diagnostic by acoustic emission method by Z. Weber, P.
Svadbik, M. Korenska and L. Pazdera (Technical University of Brno - Czech
Republic)

29

Waveform based analysis techniques for the reliable acoustic emission testing ofcomposite structures by M. Surgeon, C. Buelens, M. Wevers, and P. De Meester(Metalogic and Katholieke University Leuven - Belgium)

34

Optical fibres for in situ monitoring the damage development in composites and therelation with acoustic emission measurements by M. Wevers, L. Rippert, and S.
Van Huffel (Katholieke University Leuven - Belgium).

41

Lamb-wave source location of impact on anisotropic plates by H. Yamada, Y
Mizutani, H Nishino, M. Takemoto and K. Ono (Aoyama Gakuen University - Japanand University of Calif., Los Angeles [UCLA] - USA)

51

Characterisation of the damage and fracture mechanisms in Ti3 SiC2 using acoustic
emission by P. Finkel, R.K. Miller, M.A. Friesel, R.D. Finlayson, P.T. Cole, M.W.
Barsoum and T. El-Raghy (Physical Acoustics Corp. and Drexel University - USA)61

Evaluation of martensitic transformation dynamics of Cu-Al-Ni shape memory alloy
single crystals by acoustic emission method by K. Yoshida, S. Kihara, and K.
Sakamaki (University of Tokushima - Japan)

68

The identification of basic fatigue parameters on electroresonance pulsator with help of
acoustic emission technology by P. Mazal, and J. Petras (Brno University ofTechnology - Czech Republic)

75

The phase of contact damage and its description by help of acoustic emission
by J. Dvoracek, J. Pazdera, and L. Petras (Brno University of Technology - Czech
Republic)81

Acoustic emission monitoring of delayed hydride cracking in Zirconium
by A. Barron and C. Rowland (Bodycote and Pancom - United Kingdom)

87

Examination of plate valve behaviour in a small reciprocating compressor using
acoustic emission
by J.D. Gill, R.D. Douglas, Y.S. Neo, R.L. Reuben and J.A.
Steel (Heriot-Watt University - United Kingdom)96

A new method of acoustic emission source location in Pipes using cylindrical guided
waves by H. Nishino, F. Uchida, S. Takashina , M. Takemoto and K. Ono(Aoyama Gakuen University - Japan and University of Calif., LA - USA)102

Acoustic emission method for pressure vessel diagnostics at a refinery by B.S.
Kabanov, V.P. Gomera, V.L. Sokolov, and A.A. Okhotnikov (Kirishinefteorgsintez Russia)

111

Optimisation of acquisition parameters for acoustic emission measurements on smallpressure vessels by F. Rauscher (Vienna University of Technology - Austria)

118

Monitoring of weld's defects evolution submit to static and dynamic loading thanks tothe acoustic emission method by C. Hervé, R. Pensec, and A. Laksimi, (CETIM France)

125

Acoustic emission due to cyclic pressurisation of vessels with partially penetrated
longitudinal seams by M. Bayray (Institute of of pressure vessels and plantTechnology - Austria)

131

Inspection of LPG vessels with AE examination by P. Tscheliesnig, and G.
Schauritsch (TÜV Austria - Austria)

138

Inspection of pressure vessels used in refrigeration and air conditioning systems
by A. Skraber, F. Zhang, M. Cherfaoui, and L. Legin (CETIM - France)

144

The new Russian standards in the field of acoustic emission
by V.I. Ivanov, and L.E. Vlasov (Orgenergoneft - Russia)

150

Using acoustic emission to monitor metal dustingby F. Ferrer, E. Andres, J. Goudiakas, and C. Brun (Elf Atochem - France)

155

Use of acoustic emission to detect localised corrosion philosophy of industrial use,
illustrated with real examples by A. Proust, and J. C. Lenain (Euro PhysicalAcoustics - France)

161

Inspection of flat bottomed storage tanks by acoustical methods. Classification of
corrosion related signals by P. Tscheliesnig, G. Lackner, M. Gori, H. Vallen and B.
Herrmann (TÜV - Germany, CEST - Italy)

167

Screening of tank bottom corrosion with a single point AE detector: AE-Simple byP.J. Van De Loo, and D.A. Kronemeijer (Shell Global Solutions - The Netherlands)

174

Case histories from ten years of testing storage tank floors using acoustic emission
by S.N. Gautrey, P.T. Cole, and S.J. Schoorlemmer, (Physical Acoustics Limited-United Kingdom)180

Acoustic emission detection of damage in reinforced concrete conduit by H.W. Shen,
S. Iyer, M.A. Friesel, F. Mostert, R.D. Finlayson, R.K. Miller, M.F. Carlos and S.
Vahaviolos, (Physical Acoustics Corp. - USA)

189

Comparison of artificial acoustic emission sources as calibration sources for tool wear
monitoring in single-point machining by A. Prateepasen, Y.H.J. Au and B.E. Jones(Brunel University - United Kingdom)

196

Development of an equipment to monitoring and control the quality of resistancewelding (CRAFT Project) by J. Catty, (CETIM - France)

205

A study of small HSDI Diesel engine fuel injection equipment faults using acoustic
emission by J.D. Gill, R.L. Reuben, J.A. Steel, M.W. Scaife and J. Asquith(Heriot-Watt University - United Kingdom)

211

Unsupervised pattern recognition of acoustic emission from full scale testing of a windturbine blade by D. Kouroussis, A. Anastassopoulos, P. Vionis and V. Kolovos(Envirocoustics S.A. - Greece)

217

Acoustic emission proof testing of insulated aerial man lift devices by A.
Anastassopoulos, A. Tsimogianis and D. Kourousis (Envirocoustics S.A. - Greece)

224

BOXMAP - Non-invasive detection of cracks in steel box girders by J.R. Watson,
K.M. Holford, A.W. Davies and P.T. Cole, (Cardiff University, Physical Acoustics
Ltd - United Kingdom)

232

Monitoring failure mechanisms in CFRP orthopaedic implants during fatigue testingby A. Taylor, S. Gross, C. Rowland and P. Gregson (Finsbury, University ofSouthampton, Pancom - United Kingdom)239

Continuous monitoring of rock failure by a remote AE system by T. Shiotani, S.
Yuyama, M. Carlos and S. Vahaviolos (Tobishima Corp. – Japan, PAC - USA)

248

New AE signal conditioner for industrial use by H. Vallen, J. Forker and J. vonStebut, (Vallen Systeme GmbH – Germany and Ecole des Mines - France)258

New software tools for the AE-practitioner
Systeme GmbH - Germany)265

by H. Vallen and J. Vallen, (Vallen

Improved source location methods for pressure vessels by V. Godinez, S.
Vahaviolos, R.D. Finlayson, R.K. Miller, and M.F. Carlos (Physical Acoustics Corp.
- USA)

272

Neural network localization of noisy AE events in dispersive media by M. Blahacek,
Z. Prevorovsky, and J. Krofta (Institute of Thermomechanics - Czech Repubric)

279

Dynamics and damage assessment in impacted cross-ply CFRP plate utilizing thewavaform simulation of Lamb wave acoustic emission by Y. Mizutani, H. Nishino,
M. Takemoto and K. Ono (Aoyama Gakuin University – Japan and UCLA - USA)286

Acoustic emission detection during stress corrosion cracking at elevated pressure andtemperature by R. Van Nieuwenhove, and R.W. Bosch (Centre d’Etude de l’Energie
Nucliaire - Belgium)

293

Detection of pitting corrosion of aluminiurn alloys by acoustic emission technique
H. Idrissi, J. Derenne, and H. Mazille (INSA de Lyon - France)

299

by

Reliability of acoustic emission technique to assess corrosion of reinforced concreteby H. Idrissi, and A. Limam (INSA de Lyon - France)

307

AEWG43 Presentation
Theoretical Treatment of AE in Massive Solid….by M. Ohtsu

S1

Diagnosis of Concrete Structures by AE….by M. Ohtsu

S7

EWGAE 2000 (EWGAE.pdf)
SOMMAIRE -- Content of EWGAE 2000 Proceedings
AVANT-PROPOS
Mohammed CHERFAOUI, Christel RIGAULT
Présentation - EWGAE

i-v
vi
vii

Authors Index (18Auindx.pdf)
Contents of Volume 18, 2000 (18Conts.pdf)

I-1 – I-2
I-3 – I-6

e-mail Addresses of Authors (e-mail.pdf)
EWGAE 2000 Participant List (Senlis.pdf)

JOURNAL OF ACOUSTIC EMISSION, VOLUME 18
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participants and selected authors' e-mail are available in Senlis and e-mail files.
ISSN 0730-0050

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STUDIES OF THE NON–LINEAR DYNAMICS OF
ACOUSTIC EMISSION GENERATED IN ROCKS
Zofia MAJEWSKA and Zofia MORTIMER
Institute of Geophysics, University of Mining and Metallurgy, Kraków, Poland
ABSTRACTThe paper concerns the acoustic emission (AE) generated in rock specimens by fracture process of avariable origin and scale. Two different experiments have been conducted: uniaxial compression andgas sorption. Generalised correlation exponent dimension Dq of the distribution of AE energy rate
have been determined and the reconstruction of the attractors of the phase space for the AE time series
have been calculated. It was shown that Dq may also be associated with fracturing mode and thatprocess of rock fracturing during compression is of lower order than that of coal fracturing induced bygas sorption.
INTRODUCTION
AE techniques may be applied to a wide range of activities in the geotechnical area, e.g. mine safety,
rock and soil slope stability, surface subsidence, in situ stress evaluation, underground storage, nuclear
waste disposal, geothermal energy development and others too numerous to mention. In all these
activities the rock failure is of prime importance. In general, AE techniques are used as a diagnostic
tool but attempts are made to find the relationship between the AE characteristics and the basic physics
of the phenomenon under study [1]. Recent studies have produced substantial evidence, whichindicates that fractal statistics can be successfully applied to AE generated in rocks samples understress [e.g. 2-8].
This paper is concerned with an examination of fracture process in rocks in terms of non-linear
dynamics of observed AE during two very different types of tests - uniaxial compression and gas
sorption on hard coal. Rock fracture in compression is a progressive process and AE characteristics
together with the stress-strain curves in the pre- and post-failure regions can provide an insight into thefailure process. The purpose of the gas sorption tests on hard coal was to study the specific interactions
between coal matrix and gas molecules leading to coal fracturing, which is revealed as AE. Presented
in this paper are studies of non–linear dynamics of AE from microfracturing monitored during uniaxial
compression experiments and gas sorption tests comprised of:! analysis of fractal /multifractal character of AE energy distribution [Dq (q)],! analysis of time variability of fractal dimensions D2 and D∞,! analysis of phase space of AE process (calculation of embedded attractors).
EXPERIMENTAL
Uniaxial compression tests
Three rock types were used in this study; coal, dolomite and sandstone. All specimens were cylinders
42 mm in diameter and 84 mm in length and were loaded uniaxially using the Instron 8500 stiff testingmachine operated at constant strain rate 10–6 s-1 (axial or circumferential). The load and strain historyof each specimen was obtained. AE was detected by Dunegan SE1000-HI transducer with an integral
40dB preamplifier mounted on the upper platen of the Instron. AE signals were further amplified by
low-noise, high gain preamplifier and then fed to a microcomputer controlled AE analyser. AE
monitoring system was operated with the following settings: filter 10 kHz – 100 kHz, gain 90 dB.
1

Gas sorption tests
Studies have been conducted on two types of hard coal: high rank and medium rank, obtained fromtwo Polish coal basins (Lower Silesia Basin and Upper Silesia Basin). Test specimens were 22 mm indiameter and 44 mm in length. Experimental facilities used in high pressure sorption of CO2 on coal
comprised a pressure vessel and associated pressurisation and monitoring systems. The coal specimen
could be pressurised and depressurised using a system of pressure valves. AE from coal specimen wasmonitored by an externally mounted transducer on the cap of the test vessel. The other elements of AE
monitoring system were the same as before. Each specimen was first vacuum degassed (10-4 torr).
Next, carbon dioxide from a gas cylinder was admitted into the vessel at a pressure of 4 MPa. AE
measurements were carried out during fifty hours of gas sorption.
THE BASIC CALCULATIONE PROCEDURE
In this study the generalised dimension Dq was estimated by means of the statistical moment of order qof the correlation integral [Grasberger and Procaccia, 1983]:q
1 N  1C(l,q) = ∑(1)∑ Θ l − xi − x j 
n i =1  N − 1 j ≠ iρwhere N is the total number of measures xi and Θ is the Heaviside function. An object is multifractal if(

logC (,lq)∝ qD q + 1 logl)

(2)

The linearity of the log-log plot indicates the existence of statistical self-similarity of events. The slopeof log C(l, q) vs. log (l) plots gives Dq, the estimate of the generalised dimension. For q=2 thegeneralised dimension is the correlation exponent dimension, denoted by D2. The estimate D∞ clears up
information about the most intensive clustering in a heterogeneous fractal set. The difference between
estimates D2 and D∞ represents the degree of the heterogeneity of a heterogeneous fractal set. Here theD12 is taken as the estimate of D∞.
The Dq vs q relation characterises the specific conditions of the process under study. According to theρTakens theorem, from the set of observations of particular values s(n) d-dimensional vectors y(n)with
the delay co-ordinates (shifted by the time lag T) were created [e.g. Abarbanel, 1996]:
ρ(3)
y (n) = [ s(n), s(n + T ), s(n + 2T ),........, s( n + ( d − 1)T ]These vectors create d-dimensional embedded space, in which the reconstructed attractor isinvestigated. Single variable time series of the AE energy rate have been examined. The correlation
dimensions D2, for increasing embedding dimensions d, from 1 to 15, were calculated. When the
Euclidean dimension reaches a certain value d beyond which the fractal dimension D2 no longer
increases, that d value estimates the dimension of the reconstructed phase space and the dimension of
the attractor. A small number of degrees of freedom confirms the deterministic chaotic behaviour ofthe process.
RESULTS
Only selected results are presented due to the limited space. Acoustic emission and loading history of
two specimens – dolomite (loaded with constant circumferential strain rate) and coal (loaded withconstant axial strain rate) are shown in figs. 1 and 3a. There are characteristic changes of AE energy
rate during loading cycle. The AE development is different for the two samples examined. In dolomite,the anomalous rapid initial increase in AE corresponds to high stress concentration arising still at crack2

closure. During the linear elastic phase, AE energy slows down and rises significantly for the second
time near the failure strength. Changes of AE energy rate follow that of load, both in pre-failure andpost-failure regions.

Fig.1 AE energy rate distribution for dolomite specimen under uniaxial compression test.
Coal, at the beginning of loading behaves in different way from the dolomite - only very few AE
events are recorded and a marked increase in AE is evident but beyond fifty percent of the failure
strength. A sharp rise in AE occurs at eighty percent of the failure strength and is followed by a drastic
drop just before failure.

Fig.2 AE energy rate distribution and temporal changes of D2 and D2-∞
for dolomite specimen under uniaxial compression test.
3

Figures 2 and 3b reveal that temporal variations of D2 and D2-∞ indicate consecutive stages of specimen
deformation. The pre-failure and post-failure regions for dolomite specimen are described by two
different Dq vs q relations (Fig.6). The pre-failure region characterises monofractality and strong selfsimilarity of AE energy rate distribution while a weak multifractality and a weak self-similarity isobserved in post-failure region. It seems that a various focal mechanisms of AE are responsible for it.
In uniaxial compression rock failure occurs in two modes [e.g. 11, 12]:
1. local fracturing predominantly parallel to the applied load,
2. both local and macroscopic shear failures.

Fig.3 AE energy rate distribution (a) and temporal changes of D2 and D2-∞ (b)for coal specimen under uniaxial compression test.
For heterogeneous rocks, the first mode of failure is characteristic in pre-failure region whereas theconsecutive development of faults at the boundary and in the interior of the specimen take place inpost–failure region. The essential similarity of the plots Dq vs q for coal (which failed violently) anddolomite in the pre-failure region confirms the above explanation. Figure 7 shows that the embedded
phase space of AE energy rate associated with rock fracturing under uniaxial compression is around
six dimensional.
4The AE phenomenon induced in hard coal by gas sorption can be explained, to some extent, on thebasis of copolymeric model of coal structure [e.g. 13, 14]. From the model, hard coal consists of:! a surface part (macro, meso-, micro- and submicropores),! a volume part (macromolecular and molecular fractions).
Gas molecules can be deposed in coal structure on the surface of macropores (surface adsorption),
located into mesopores (capillary condensation) and into micro- and submicropores (volumetricadsorption). Furthermore, some molecules may penetrate into macromolecular and molecular fractions(absorption). The sorption capacity of coal is made up of the submicropores, molecular fraction andmacromolecular three dimensional cross- linked network.
Figures 4 and 5 show the AE energy rate and changes of D2 and D2–∞ estimates during CO2 sorption for
two types of coal tested. In general, three subprocesess within the sorption of CO2 can be distinguishedin the plots, namely: adsorption, adsorption – absorption and absorption. At first, the adsorption takeplace, then two simultaneous processes (adsorption and absorption) proceed at various rates, hereafter
absorption is carried through. The difference in kinetics of CO2 adsorption and absorption is evident.

Fig.4 AE energy rate distribution (a) and temporal changes of D2 and D2-∞ (b)during CO2 sorption on specimen CA.
Plots Dq vs q (Fig. 6) show that sorption process in specimen CA (high rank coal) is moreheterogeneous than in specimen CK (medium rank coal). Moreover, the reconstruction of theembedded phase space indicates that CO2 sorption in high rank coal is a chaotic process of lower orderthan it is in medium rank coal (Fig. 7).

5

Fig.5 AE energy rate distribution (a) and temporal changes of D2
and D2-∞ (b) during CO2 sorption on specimen CK.

Fig. 6 Generalised fractal dimensionof AE energy rate.

Fig. 7 Plots of correlation dimension D2
versus embedding dimension d.
6

The observed differences in behaviour of coal tested are interpreted as due to the direct impact of coalrank. In high rank coal, the content of a molecular fraction is low and a contribution of the aliphatic
and heterocyclic cross – linked chains of macromolecular fraction increases, what results in structure
stiffening. In contrast, the very high content of mesopores and slight amount of molecular size pores ispeculiar to medium rank coal as a result of a marked differentiation in dimensions of coal structure.
Therefore, for this coal, the contribution of the elastic absorptive part is lower than it is in high rank
coal.
CONCLUSIONS- Multifractal analysis of the AE generated in rocks subjected to uniaxial compression shows thatgeneralised fractal dimensions may also be linked with fracturing mode,- the heterogeneity of fractal structure of the distribution of AE energy rate associated with CO2
sorption on coal depends on coal rank and grows along with it, and- process of gas sorption in high rank coal is a chaotic process of lower order than it is in medium
rank coal.
REFERENCES
Hardy H.R., 1998, In Proc. of 6th Conf. AE/Ma in Geologic Structures and Materials, pp. 599-609.
Lei, X., Nishizawa O. and Kusunose K., 1993, Geophys. J. Int., vol. 115, pp. 79-84.
Main J. G., Sammonds P. R. and Meredith P. G., 1993, Geophys. J. Int., vol. 115, pp. 367-380.
Hirata T., J., 1987, Geophys. Res., vol.92, pp. 6215-6221.
Smirnov V. B., Ponomarev A. V. and Zavyalov A. D., 1995, Physics of the Earth, vol. 1, pp. 38-58,(in russion).
6. Itakura K., Sato K., Nagano K. and Kusano Y., 1994, Progress in Acoustic Emission VII, JSNDI,
pp. 255-260.
7. Majewska Z., Mortimer Z., 1988, Proc. of 14th IAES Conf., pp. 109-118.
8. Mortimer Z., Majewska Z., Lasocki S., 1999, Pol. Acad. Sc. M-22 (310), pp. 89-94.
9. Grassberges P. and Procaccia J., 1983, Physica 9 D, pp. 189-208.
10. Abarbanel H.D.I., 1996, Analysis of Observed Chaotic Data, Springer Verlag, Basel.
11. Wawersik W. R., Fairhurst C., 1970, Int. J. Rock Mech. Min. Sci., vol. 7, pp. 561-575.
12. Andreev G.E., 1995, Brittle Failure of Rock Materials, A.A.Balkema /Rotterdam/Brookfield/.
13. Hall P., Thomas K. and Marsh H., Fuel, 1992, vol. 71, pp. 1271-1275.
14. Milewska-Duda J., Fuel, vol. 72, 1993, pp. 419-425.
1.
2.
3.
4.
5.

ACKNOWLEDGEMENTS

This work was sponsored by the Polish Committee for Scientific Research under contract No
6PO4D05017.

7

ACOUSTIC EMISSION AS A RESULT OF TENSIL AND SHEARINGPROCESSES IN STABLE AND UNSTABLE FRACTURING OF ROCKS
Joanna PININSKA
University of Warsaw, Warsaw, Poland
ABSTRACT
Uniaxial tests performed on rocks indicate that intra-, inter- or transgranular cracking can assume anumber of forms, depending on the mineral composition and structure of the rock medium. Based on an
analysis of deformation characteristics and the course of acoustic emission, one can infer as to whether
the observed defects are a result of tensile or shearing processes, or both.
INTRODUCTION
Strength experiments in laboratory show that the main factors responsible for destabilisation of rock structure
under compression are the size, geometry and strength of the mineral components of rock. These and manyother properties of rock inhomogeneity influence the course of cracking (1-5). The character and course ofcracking determine the rate, at which a rock loses its stability rapidly or with a delay and refers with the great
similarity to the model of acoustic emission (AE) events.
The simulations of cracking processes in media having different structures recently presented by Napier andPeirce (6) show how much the advancement of tensile and shearing processes during cracking depends on a
structure of the rock and dynamics of its deformation. In order to refer those models to the lithogenic divisionsof rocks, they should be constructed in view of the basic characteristics of igneous, sedimentary andmetamorphic rock structure. A general genetic review shows that the majority
a

b

c

a’

b’

c’

Fig. 1. Example of characteristic genetic features of rock structures: a - periodic structure, regulargrains; b - chaotic structure with a number of pores, irregular grains; c - directional structure ofgrains; a’,b’,c’ - examples of random tesselation patterns after Malan & Peirce (7): a’ and c’- mediumof polygonal structure; b’ - medium of trigonal structure,8

of igneous and equigranular sedimentary clastic rocks with homogeneous, massive structure and well-formed
grains have the features of a periodic medium with quartz grains as the basic elements (Fig. 1a). Most of
sedimentary rocks of organic origin have a random distribution of their basic elements; the geometry of grains iscomplex - irregular with numerous pores in between (Fig. 1b). Directional, strongly anisotropic positioning ofgrains is characteristic of metamorphic rocks (Fig. 1c).
Due to the inhomogeneity of rock structure, the process of cracking has a discontinuous character and varying
dynamics. It can be accelerated or delayed depending on the local properties of the rock structure and theactual stage of stability. The hysteresis loops on the strain curve are a representation of oscillations resulting fromthe concentration and relaxation of stresses. Therefore, the process of cracking and its varying dynamics can beregistered with the use of a deformation curve or an acoustic emission plot, provided the strength tests are
conducted in a stiff testing machine with a strain rate control at the pre-failure and post-failure state (Fig. 2a and
b).
a

b

Fig. 2. Varying dynamics of cracking registered at the prefailure and postfailure state in a stiff
testing machine: a - from axial strains; b - from acoustic emission; (diabase from Niedzwiedzia
Gora).
In order to explain the mechanism of cracking and evaluate its dynamics and its acoustic emission path, it isnecessary to study the features of the structure in relation to the observations of the shape of the centres ofcracking, their initial orientation and its modification during their further development.
EXAMPLES OF MANIFESTATIONS OF CRACKING
From strength testing of various lithologic types of igneous, sedimentary and metamorphic rocks from the Holly
Cross Mts, the Sudetes and Jurrassic Uplands in Poland, the following rock structures/rock cracking relations
were found: micro-fracture inside grains - intragranular cracking (Fig. 3a); micro-fracture around grains intergranular cracking (Fig. 3b); and transgranular development of macro-fracture (Fig. 3c) with displacements
along surfaces of discontinuity (Fig. 3d).
The character of the grain contacts in polymineral rocks influences the cracking mechanism; hence the
distribution of very strong grains, quartz in particular is very significant. If the strong grains touch, stressconcentrates on the contacts and these grains are destroyed first. The nuclei of cracking are formed as a result
9

a

b

c

d

Fig. 3. Formation of initial fissures in rocks: a - intragranular cracking (sandstone with silicate
cement); b - intergranular cracking (sandstone with carbonate cement); c - development oftransgranular cracking (granite); d - development of cracking with a displacement (limestone).
a

b

c

d

Fig. 4. Nuclei of intragranular cracking: a - Zerkowice sandstone; b - Michalowice granite (visible
barrier in the way of the crack in the cover of a plagioclase grain); c - crystallising fissured grain ofquartz (Kosmin syenite); d- fissured grain of olivine in undisturbed rock groundmass (Luban basalt).
of tension, being an intragranular separation of an individual grain. This is exemplified by the quartzite sandstones
and the sandstones with siliceous cement (Fig. 4a), where the nuclei of cracking concentrate in the quartz grains
and the silicate cement forms a barrier disabling further cracking. Similar phenomena can be observed in granites
10

(Fig. 4b) and other igneous rocks (Fig. 4c and 4d).
When the grains do not touch one another but are separated with a weak clayey ferruginous silicate or
carbonate cement, the cracking takes place in the cement and is of intergranular character. This effect occurs inmost of weak sedimentary rocks, for example in the Dwikozy sandstones with carbonate cement (Fig. 5a).
Similar phenomena of intergranular cracking can be observed in carbonate rocks, especially organogenic rocks,
for example in Pinczow limestones (Fig. 5b). The failure fracture in carbonate rocks usually has a very roughsurface and displacements typical of shearing processes (Fig. 5c).
acbFig. 5. Formation of centres of intergranular cracking: a - in clastic rocks with carbonate cement(Dwikozy sandstone); b - in organodetrital limestones (Pinczow limestone); c - fracture withdisplacements (Ogrodzieniec limestone).
Cracking in metamorphic rocks is conditioned by the anisotropy of the medium. Generally, it has anintergranular character and is concordant with the lamination. Cracking is connected with shearing, for example
in the marble of Kletno (Fig. 6a). However, local intrusions of other stronger grains may form barriers modifying
the propagation of the crack. Barriers originating from the intrusion of quartz in marble are presented in Fig. 6b.
abFig. 6. Example of fracture propagation in metamorphic rocks: a – use of structural surfaces (coarse
marble of Kletno); b - modification of crack propagation on accessory quartz grains.
Different grain sizes quantitatively influence the formation of defects in the rock; the destabilisation of a finegrained rock structure can occur only when a greater number of fissures appears than is required in a coarsegrained rock. Therefore, e.g the fine-grained Zimnik granites are stronger than the medium-grained Strzegom
granites, and the fine-grained Przedborowa syenites are stronger than the coarse-grained Braszowice gabbro.
The irregular shape of grains having complex geometry induces new processes of shearing of edges, which can
11be observed, for example, in the Pinczow limestones (Fig. 5b). The cracking is of inter-, intra- and transgranular(with displacements) mixed-type character. The susceptibility to tightening of fractures increases (Fig. 7). The
nuclei of microcracking concentrate and this leads to the formation of macrofractures and the development of
unstable cracking. The process of macrofracturing can be locally modified with varying directions of anisotropy
of individual grains or through transgranular cracking, which in the course of unstable cracking destabilises the
structure of rock.

Fig. 7. Transgranular cracking in limestones from Ogrodzieniec.
RELATION OF THE MECHANISM OF CRACKING TO THE MODEL OF ACOUSTICEMISSION
The above-discussed properties of the rock structure cause the formation of nuclei of microcracking and thedevelopment of macrocracking to have a varying dynamics, related to the given strain mechanism. They arefollowed by AE-event specific distribution. In the case of intragranular cracking of periodic media, cycles
representing destruction of individual grains appear on the strain curve as repeatable results of relaxation andstress concentration, and as a continuos, regular increase and decrease of signal energy in acoustic emission on
the micro and macro scale. The processes follow the model of deformation type MII and model of postfailure
acoustic emission type H (Fig. 8a).
In rocks with slip surfaces resulting from shearing, considerable post-critical volumetric strains are observed(Pininska, 1994, 1995). Eventually, in the course of final failure, local strengthening and slowing down of theprocess can take place.
The deformation curves and acoustic emission paths related to intergranular cracking in a medium with random
composition of grains show irregular patterns of relaxation and concentrations of strain and energy emission. The
processes follow the model of deformation type MIII and model of postfailure acoustic emission type M or L
(Fig. 8b).

12

a

b

Fig. 8. Regular (a) and irregular (b) effects of cracking registered on the strain curve and acoustic
emission path and the models of deformation and acoustic emission (after Pininska (4)).
As already mentioned, due to the influence of the shearing processes in intergranular cracking, considerable
volume changes can be observed, the decrease of strength is slowed down and residual strength is significantly
maintained. Intragranular cracking results in a rapid loss of strength. This is accompanied tightening of thefractures, which locally strengthens the rock, but no significant volume changes occur and the final destruction of
the structure is usually violent.
COMPARATIVE STUDY
The presented phenomena observed in the majority of rocks subjected to uniaxial compression can beinterpreted in a view of simulation studies by Napier & Peirce (1995). They modelled the development of
cracking in brittle materials having a polygonal and trigonal structure (Fig. 1a and b). The analyses showed thatthe finer the material, the higher the density of failure fissures and the state of mobilisation of all cracks causing
final destruction of the structure at a given stage of strain is much lower in the fine-grained material than in its
coarse-grained counterpart. This confirms the strength experiences in actual rocks, where unstable cracking incoarse-grained material takes place much faster. Less fractures are necessary to originate the unstable cracking
process.
Fissures in the polygonal structure develop much harder than in the trigonal one, therefore the structure is much
more stable than the trigonal one. However, this is true only to a certain extent as, at more advanced cracking,additional tensile centres caused by the stress of the displacing grains can occur. As a result of the additional
13

factor mobilising the new nuclei of cracking, the polygonal structures undergo a rapid decomposition withoutslipping displacements. In contrast, considerable zones of slipping, diagonal to the rock sample, are formed in
the trigonal structure. The actual structure of rocks is more complex than suggested in the trigonal and polygonalmodels presented by Napier and Peirce. None the less, these models explain the way in which the structure isdestroyed in most rock types. Rocks with carbonate mineral composition follow the description of the trigonalmodel, whereas the behaviour of clastic sedimentary rocks and igneous rocks corresponds to the polygonalmodel. These phenomena directly influence the general paths of cracking and acoustic emission.
The cracking mechanisms of polygonal models apply to polymineral clastic and crystalline igneous andmetamorphic rocks with considerable quartz grains. On the strain curve they show regular oscillations ofstresses related to intragranular tensile defects of seeds-point type. These models explain the reasons for thelack of extensive post-failure volume strains and low values of residual strength and represent repetitive acoustic
oscillations model.
The cracking mechanism explained by the model of trigonal structure is characteristic of carbonate rocks, andclastic rocks with high feldspar or clayey material content, showing varying dynamics of stress and represent
acoustic irregular oscillations model. This is related to intergranular cracking with a predominance of shearing
processes and considerable postfailure volumetric strains and the pick of acoustic emission before the final
destruction. It is accompanied by a considerable residual strength.
Acoustic indicators of rock structure degradation can be determined for crystalline rocks and carbonate rocks
according to their varying acoustic emission along the rock deformation path. Especially diagnostics are theindicators for determination of the pre-failure thresholds of microdilatancy and macrodilatancy as well as for the
stable fracturing range. These three indicators are strictly related to the grade of fracturing mobility and show thenumber of acoustic events at the limit of initial stabile fracturing, and at the limit of stabile fracturing - i.e. at theunstable fracturing threshold. In crystalline rocks the acoustic indicators are related to the volume density andstrength of rock. In carbonate rocks they manifest the random pattern of fracturing and effects of attenuation by
the shearing processes.
REFERENCES
1. Brace W. F. and Bombolakis E. G. 1963. A note on brittle crack growth in compression. J. Geoph. Res.,
68, Washington.
2. Cundall P. A. and Fairhust C. 1986. Correlation of discontinuum models with psychical observations – anapproach to the estimation of rock mass behaviour. Felsbau. 4.
3. Pininska J. & Lukaszewski P. 1992. Rock failure in acoustic emission spectre. Proc. XX-th Meeting of
EWGAE, Leuven. Belgium.
4. Pininska J. 1994. Crack growth in the postcritical path of deformation of sedimentary rocks. Mechanics ofJointed and Faulted Rocks-2. Balkema. Rotterdam.
5. Pininska J. 1996. Some problems of the stress distribution on structural contacts in natural rocks bodies.
Lvow University. Sbornik Naucznych Rabot. 36. t.2. Lvow.
6. Napier J. A. L. and Peirce A. P. 1995. Simulations of extensive fracture formation and interaction in brittle
materials. Proc. Mechanics of Jointed and Faulted Rock – MJFR. Balkema. Rotterdam.

14

RELATION BETWEEN ACOUSTIC EMISSION SIGNAL SEQUENCES INDUCED BY
THERMAL LOADING AND THE STRUCTURE OF SEDIMENTARY ROCKS
Bogdan ZOGALA and Ryszard DUBIELUniversity of Silesia, Sosnowiec, Poland

ABSTRACT
Sedimentary rock samples were subjected to thermal loading. During the heating both AE activity(number of counts per 100s) and temperature were recorded. The results showed that significant
increases of acoustic emission activity and cumulative acoustic emission counts were different forvarious kinds of rocks. Four types of AE signals sequence were distinguished. The type of sequence isdirectly related to structure of sample regardless of the kind of rock.
INTRODUCTION
Mineral composition of rocks, lamination, porosity, fracturing and micro-fracturing directly influence
their elastic properties. Thus, the structure of rocks influences the course of acoustic emission (AE)induced by mechanical (1) as well as thermal loading (2). A study carried out on sedimentary rocks
subjected to thermal stresses showed that, for various kinds of rocks, AE significantly increased at
different temperatures and various kinds of rocks are characterised by different AE activity (3). It wasalso shown that AE signal sequence depends on structure of rock (4). The goal of our investigation was
to determine the relation among AE signal sequence and the structure and lithology for various kinds of
sedimentary rocks.
MEASUREMENTS
The measurements were carried out on sedimentary rock samples: mudstone, sandstone and limestone.
All the samples were taken from the Upper Silesian Coal Basin and belong to carboniferous (sandstone
and mudstone) and triassic (limestone) strata. The rock samples (cylinders of 100-mm diameter and 100mm height) were obtained by cutting the cores. On each sample, two holes were drilled to the depth of
50 mm. In the first hole, the temperature gauge and in the second one the waveguide for AE transducer
were installed. All the samples were heated in unconfined conditions up to 140o C. During the heating
both the AE activity (number of AE counts per 100s) and temperature were recorded. The heating rate
was approximately 2.1-2.3o C/min.
SAMPLES•

Mudstones were composed of sharp edged quartz grains with admixture of silty minerals, ferricoxides and siderite grains. Some of investigated samples have been laminated whereas the other hadchaotic and compact structure. Fracturing, variable granulation, and pores of several mm have also
been noticed in some of the samples.

•

Sandstones were medium and variable-size grained, laminated, and were composed of quartz,
feldspars, micas, and chips of rock with argillaceous and calcareous cement. Quartz grains werechaotic oriented and sharp edged. Colour of the samples was light grey and grey.

15

•Limestones were compact, hard, unbedded, breccious structured. It was noticed that pores withcalcite druses, cracks about 30 mm filled by sharp-edged chips of marlaceous and calcareous
breccia. Some of the samples were composed of macroclastic breccia cemented by argillaceous
substance. One also observed stylolitic sutures. Colour of the samples was white and light grey.

DISCUSSION
Investigation concerned sixty rock samples: forty sandstone, ten mudstone and ten limestone samples.
They were subjected to thermal loading to maximum temperature 140o C. Next, sequences of acoustic
emission signals were analysed. Significant increase of acoustic emission activity was observed indifferent temperature range for various kinds of rocks (table 1):• from 64.8 to 75.8o C for sandstone samples (average 70o C)• from 60.4 to 103.7o C for mudstone samples (average 86o C)• from 64.2 to 81.8o C for limestone samples (average 74o C)
Cumulative AE counts were also different for various kinds of rock. The largest for limestone samples(average 1950 signals), less for sandstone samples (average 1800 signals) and the least for mudstone
samples (average 1700 signals) (see Table 1). One distinguished four types of acoustic emission signal
sequences (Figs. 1, 2, and 3); these were found in all kinds of samples:• type 1 – main shock (crack) is very strong and is not proceeded by foreshocks. The AE quickly
decays.
• type 2 – main shock is not so strong as for type 1 and the foreshocks are observed• type 3 – main shock is not observed. First the number of signals increases then decreases.
• type 4 – main shock is not observed. High AE activity is observed to the end of the heating.
Table 1 AE Signal Sequences
Rock

-1-

-2-

-3-

-4-

-5-

-6-

-7-

-8-

-9-

Sandstone
MudstoneLimestone

40
10
10

64.8-75.8
60.4-103.7
64.2-81.8

70
86
74

98-3847
126-2687
225-3247

1800
1700
1950

4
1
1

12
3
3

16
2
4

8
4
2

1 – number of samples; 2 – temperature range of AE significant increase; 3 – average temperature of significant AE increase;4 – range of AE cumulated count number for particular samples; 5 – average AE cumulated count number;
6 – number of samples for sequence type 1; 7 - number of samples for sequence type 2; 8 - number of samples for sequence
type 3; 9 - number of samples for sequence type 4

The sequence type is found to relate to the structure of rock. Sequences of type 1 and 4 were obtainedfor homogenous rocks with low porosity and few cracks. The main feature of the rocks is uniform
distribution of stresses. The heterogeneous cracked rocks with low porosity were characterised bysequence of type 2. Sequences of type 3 were obtained for extremely heterogeneous cracked rocks withhigh porosity. The stress distribution in these rocks is not uniform. The largest number of AE signals
was observed for samples characterised by sequence type 3 and 4 whereas the least AE counts forsamples characterised by sequence type 1. Figure 4 shows percentage shares of particular sequencetypes for tested rock samples; i.e., mudstone (a), sandstone (b) and limestone (c).
We also compared four AE signal sequences with sequences obtained in our previous researchconcerning the thermal memory effect in the course of AE (5-8). We found that the thermal memory

16

Count number/100s

100Type 1

50

0
0

50

100

150

o

Temperature [ C ]Count number/100s

150

Type 2
100

50

0
0

50

100

150

o

Temperature [ C ]Count number/100s

200Type 3

150
100
50
0
0

50

100

150

o

Temperature [ C ]Count number/100s

200Type 4
150
100
50
0
0

50

100

150

o

Temperature [ C ]Fig. 1 Four types of sequences of AE signals for tested 40 sandstone samples.
17

Count number/100s

80Type 1
40

0
0

50

100

150

100

150

100

150

100

150

o

Temperature [ C ]Count number/100s

50

Type 2
25

0
0

50
o

Temperature [ C ]Count number/100s

60Type 3
30

0
0

50
o

Temperature [ C ]Count number/100s

100Type 4
50

0
0

50
o

Temperature [ C ]Fig. 2 Four types of sequences of AE signals for tested 10 mudstone samples.

18

Count number/100s

200Type 1

150
100
50
0
0

50

100

150

o

Temperature [ C ]Count number/100s

200Type 2

150
100
50
0
0

50

100

150

o

Temperature [ C ]Count number/100s

200Type 3

150
100
50
0
0

50

100

150

o

Temperature [ C ]Count number/100s

250Type 4

200
150
100
50
0
0

50

100

150

o

Temperature [ C ]Fig. 3 Four types of sequences of AE signals for tested 10 limestone samples.

19

a)type 4b)
type 110%c)type 4type 1

type 4type 1

20%

10%

20%

10%

40%
type2

type2

30%

30%

type2
30%

type 3type 3type 3

20%

40%

40%

Fig. 4 Percentage share of particular sequence types for tested rock samples. a) mudstone samples, b)
sandstone samples, c) limestone samples.
effect was observed in homogeneous and heterogeneous rocks (sequence type 2, 3, 4), but was notobserved in extremely homogeneous rocks (sequence type 1).
CONCLUSIONS
1.
2.
3.
4.

Thermal stresses induce four types of AE signal sequences in sedimentary rocks.
AE sequence type depends on the structure of sedimentary rock sample.
Cumulative AE counts are large for sequence type 2, 3 and 4 and the least for sequence type 1.
Previous and present research showed existence of the thermal memory effect in the course of AE
for sequence type 3 and 4. For sequence type 1, the thermal memory effect is not observed

REFERENCES
1. Mogi K., 1977: Dilatancy of rocks under general triaxial stress states with special references toearthquake precursors. J. of Phys. of the Earth, 25, Suppl. 1977, 203-217.
2. Chen Y., Wang C., 1980: Thermally induced acoustic emission in Westerly granite. Geoph. Res.
Letters, 7(12), 1089-1095.
3. Zogala B., 1992: Application of AE for observation of sedimentary rock samples fracturing subjectedto thermal loading. PhD Thesis, Faculty of Earth Sciences, University of Silesia, Katowice,(unpublished, in Polish)
4. Zogala B., Dubiel R., 1998: Analysis of acoustic emission signals sequence induced by thermal
stresses in mudstone samples. Proc. 23rd European Conference on Acoustic Emission Testing,
TUV, Vienna, 53-58.
5. Zogala B., 1999: Physical basis of the maximum temperature memory in the course of acoustic
emission in sedimentary rocks. Acoustics in Technology, Medicine and Culture, Krakow, 85-90.
6. Zuberek W.M., Zogala B., 1996: Memory of maximum stresses and maximum temperatures duringrock deformation. Publ. Inst. Geoph. Pol. Acad. Sci., M-18 (273, 133-138.
7. Zuberek W.M., Zogala B., Dubiel R., 1998a: Laboratory investigations of the maximum temperature
memory effect in sandstones with measurements of acoustic emission and P-wave velocity. Proc. of
6th Conference on Acoustic Emission / Microseismic Activity in Geologic Structures and Materials,
Trans Tech Publ. (ed. H.R. Hardy Jr.), 157-168.
8. Zuberek W.M., Zogala B., Dubiel R., Pierwola J., 1998b: Maximum temperature memory insandstone and mudstone observed with acoustic emission and ultrasonic measurements. Int. J. of
Rock Mech. & Min. Sci. 35, No 4-5.

20

ACOUSTIC EMISSION/ACOUSTO-ULTRASONIC DATA FUSION
FOR DAMAGE EVALUATION IN CONCRETE
Apostolos TSIMOGIANNIS, Barbara GEORGALI* and Athanassios ANASTASSOPOULOS
Envirocoustics S.A., Athens, Greece*
Hellenic Cement Research Center Ltd., Athens, Greece

ABSTRACTThe nature of concrete poses a number of difficulties to conventional non-destructive testing (NDT)
methods. Due to the existence of several geometric discontinuities, such as voids and particle
interfaces, microscopic damage is not usually evaluated by NDT. To retrieve this information fromconcrete, techniques such as microscopy and chemical analysis are used. Damage accumulation on a
microscopic level may not always affect the static behavior of a structure but is crucial for dynamic
loading conditions. Although acoustic emission (AE) techniques can detect damage at early stages on
structures that can be loaded to typical loads (bridges, piers etc.), they cannot be applied to structuresthat are not suitable for loading (buildings). In the present work, mortar specimens are monitored
using, simultaneously, AE and Acousto-Ultrasonic (AU) techniques, during compressive strength tests.
The purpose is to monitor damage by AE and evaluate this damage with AU measurements, atechnique independent of load. The results show the efficiency of AU in characterizing the level ofmicroscopic damage in mortar specimens and are in agreement with AE monitoring during loading.
AU results are also compared against microscopy findings regarding the amount of damage in thespecimens.
INTRODUCTION
The majority of modern infrastructure and buildings use reinforced concrete as the main construction
material. It is of importance to monitor and evaluate the structural integrity of such constructions.
Especially in areas with seismic activity, this becomes of crucial importance in order to ensure thesafety of buildings and other concrete based structures. This is emphasized when the mechanicalproperties of concrete are considered: concrete is a delicate mixture requiring precise mixing of thevarious elements for each use, according to manufacturer’s specifications, and care must be taken
during solidification so as to achieve the desired properties. Microscopic damage in concrete caused
under such conditions is difficult to monitor due to the nature of concrete (porosity, aggregates, etc.)
although it can accelerate damage and weaken the structure to future adverse loadings. Conventional
NDT methods [1] face a number of difficulties when applied to concrete and even more so whenapplied in an attempt to locate or characterize damage on the microscopic level. Therefore, it has beenstandard practice to assess the condition of concrete by the extraction of specimens and the use of
optical microscopy and chemical analysis to assess the effects of adverse loads, environment, etc.
Acoustic Emission (AE) can provide information about developing damage at early stages and theapplication of advanced data analysis can provide crack orientation and mode [2,3]. As AE requires
loading of the structure, it cannot be applied to buildings. Acousto-Ultrasonics (AU) can be used as adynamic method to assess the level of damage as the interaction of an induced stress wave with themedium will introduce medium-related alterations to the original wave [4]. Based on this assumption
experiments were performed using several techniques on mortar cement specimens.

21

The results are indicative of AU capabilities in assessing sustained damage. Furthermore, AU signal
characteristics, as opposed to ultrasonic velocity measurements, have demonstrated high sensitivity tochanges in the material at low loads. As a result, during the experiments, AU was considered as ameans of non-destructively assessing some of the mechanical properties and behavior of concrete. The
results are in agreement with the production of AE by the material and, also, with microscopy results of
the unloaded specimens.
MATERIALS
The mortar cement mixtures used forthe present work are presented inTable 1 with some details regarding
their composition. For each type andage, two specimens were tested. The
specimens were kept in a 90%
humidity
controlledtemperatureenvironment as required by standards
for concrete and were removed fromthis environment 2 to 3 hours prior to
testing.

ID

Age (days)

A/C

W/C

A
A
A
B
B
C
C

2
7
28
2
7
2
7

4
4
4
3
3
3
3

0.60
0.60
0.60
0.50
0.50
0.65
0.65

Table 1. Specimen specifications and data.

INSTRUMENTATION & EXPERIMENTAL SETUP
The equipment used were a PAC MISTRAS-2001 AE system
and a PAC C-101-HV pulser. The sensors used for pulsing andreceiving were PAC R6, 60kHz resonant, with PAC 1220A
preamplifiers. The sensors used were chosen for their highsensitivity, frequency response for concrete measurements [5]and their ability to be used for ultrasonic velocity measurements according to ASNT recommendations [1].

Load

AU
pulserAE
sensor

Load

Fig. 1. Experimental setup.
Microscopy results are available for specimens A for all threeages. Prior to the test each specimen was loaded to 150kN to remove AE produced from debris (dust,
small particles etc.) on the surfaces of the specimen and to allow for any irregularities on its surface tosettle. All specimens were compressed to failure and monitored throughout simultaneously by AE and
AU. During compression one AE sensor was constantly receiving AE signals from the specimen. The
pulses were generated at specific loads. Synchronous triggering of pulser and AE systems was used.
The setup is shown in Fig. 1. The AE acquisition system recorded both features and waveforms forfurther analysis.
MICROSCOPY
The application of by petrographic (microscopical) examination to building materials such as mortar
cement or/and concrete, detects the quality of aggregates, cement paste characteristics, water to cement
ratio (w/c), structure porosity, cracking and defects, etc. The procedure is according to ASTM
standards [7]. The microscopic examination is performed on epoxy impregnated “thin sections”(thickness<25 µm), typically using magnifications of 30-250x. The results shown are for specimens A
for all ages. The following parameters were measured:
22

80

0.07

70

0.06

60

0.05

50

0.04

40

0.03

30

0.02

20

0.01

10

0

Specific
Surface(1/mm)(a)(ìm)(L), Ave. Void Size (ìm)(d)0.08

Air Void (%v)(A%), Spacing Factor

Specific Surface (1/mm)

Air void Size: The average air void size decreases with age as the size distribution of air voids shifts tosmaller sizes with age. This can be explained, either by allocation of air voids, or by reduction ofpore size, due to filling with hydration products (see Fig. 2).
Air void content: The air voids percentage can be said to remain constant, if the method error isconsidered.
Specific Surface and Spacing Factor: The spacing factor (the maximum cement paste distance from theair void) increases with mortar age. Air void specific surface increases with mortar age, giving
confirmation of the decrease in air void size. Given the total air void volume as constant, this canbe explained by the large pore allocation to smaller ones. It must be emphasised that with thepetrographic examination the identified air void size is greater than 10 µm, and as a result they
don’t enlist to the micro-pores category, which are filled by cement paste during the plastic stage.

Air Void(%v)(A%)Specific
Factor(ìm)(L)

Figure 2. Microscopy results for
mortar cement specimens A.

Ave. Void
Dia. (ìm)(d)

0
0

10

20

30

Age (days)

Fig. 3. Clustering for specimens A. 2 days. Energy vs Time (s).

23Percent Hits per Class vs Age
70.0

Percent Hits

60.0
50.0

1

40.0

2
3

30.0

4

20.0

6

10.0
0.0
0

5

10

15

20

25

30

Time (days)

Fig. 4. Percent of total hits per class for all ages.

ACOUSTIC EMISSION
The first observation regarding AE data is the fact that the specimens exhibited different behavior
depending on their age. The 2 day specimens produced significant AE at lower load levels, with asubsequent decrease in activity until the fracture load is approached. As the specimens age (7 and 28days) this behavior changes with most AE produced near the fracture load. In addition, aged specimens
demonstrated the Kaiser effect. The onset of AE was clearly after the 150kN initial load. This can bejustified taking into consideration the increased elasticity of the 7 days specimens and even more so the
2 day specimens. This can be observed repeatedly in all specimens with various aggregate to cement(A/C) and water to cement (W/C) ratios. To draw more conclusions from this type of behavior and to
be able to assess the observed AU signals further, a more detailed analysis of the AE data is performed
using NOESIS, a commercially available Pattern Recognition (PR) and Neural Networks (NN)
Software package. The classification scheme is not for distinguishing among a number of failure
mechanisms, but instead is used to separate data in different categories so as to review the behavior andprogress of each type of signals among different specimens and improve the understanding of the data
produced by each specimen. Some of these classes of signals may be related to a physical phenomenon
and a discussion is made on this with results supported by AE, AU and microscopy findings. In
addition general considerations regarding the behavior and aging of concrete are used in this respect.
Data from specimen A (2 days) were used for the initial clustering. The original data were normalizedusing a non-linear space transformation based on logarithmic functions. to avoid biasing due to large
vector distances. In addition, a reduced set of six features (AMP, DUR, SIG. STRENGTH, CNTS,
RSTM, ABS ENER) was used for the unsupervised algorithm. The algorithm used was the Max-Min
Distance [6] algorithm as implemented in the Noesis software. The final classification is composed of 7
AE classes and the two AU classes. The results were then used to train a classifier so as toautomatically apply to other data sets. The training method used was the k Nearest Neighbor Classifier(k-NNC)[6] using Octagonal distance type for optimized performance in the present type of data.
Typical clustering results are shown in Fig. 3. Figure 4 presents the percentage of total hits per class for
all ages of specimens A.

24

Class 1 comprises signals, which appear in older specimens. It is apparent from Figures 3, 4 and 8 thatthese are low energy signals, which appear throughout the compression test but mainly at fracture. The
behavior of other type of specimens is similar but reduced A/C ratio samples produce more AE in class
1. This, along with the fact that class 1 appears in older specimens, may suggest that class 1 is related
to mechanisms in the cement paste. These may also be related to air content as well as void size,
parameters, which are affected by age.
Class 2 is another important class with medium energy signals which diminishes as the specimens age.
This class of signals appears from low loads and demonstrates high activity at intermediate loads. The
decrease of the number of signals in this class and its behavior with the applied load is indicative. The

Fig. 5. Class amplitude distribution (top) and cumulative hits (bottom) for all ages for specimen A (2, 7 and 28
days left to right)

same reasoning holds for class 2, which appears at low loads and is more pronounced in new
specimens. Class 2 appears to be produced by mechanisms related with the aggregates as it diminisheswith age and with A/C ratio. The behavior of Class 3 is different and the phenomena, which cause it,seem to become after 7 days. This class may be related to internal friction as it appears only very earlyin the specimens’ lives. Figure 6 illustrates typical amplitude distributions and hit cumulative plots forthe three discussed classes. The above results will also be discussed in relation to the AU data, which
can provide an impression of permanent or recoverable changes in the microstructure of the material.
ACOUSTO-ULTRASONICS
The experiments conducted during the present work demonstrate clearly that AU signals can show
oncoming failure at early stages. It is obvious from all specimens that AU signals weaken significantly
after approximately 80% of maximum load. An important observation regarding AU measurements istheir sensitivity at lower loads.
25

Throughout the loading procedure specimens produced AE depending mainly on their age andcomposition and AU data exhibited a significant variation indicating changes in the specimens,
throughout each test. Transmitted AU energy increased as the load was applied, indicating that theFig. 6. Signal Strength vs Time for specimen A 7 days.
Ultrasonic Velocity
5000

Velocity (mm/ìsec)

4500

4000

3500
LA1 2days
LA2 2days
LA1 7days
LA2 7days
3000

LA1 28days

2500
0

0.2

0.4

0.6

0.8

Max Load Fraction

Fig. 7. Ultrasonic velocity for various specimens. The velocity
remains constant until approx. 0.8 of max load is reached.

26

1

Total Energy per Class vs Age
50.0

Percent of Total Energy

40.0
1
30.0

2
3

20.0

4
6

10.0

0.0
0

5

10

15

20

25

30

Age (days)

Fig. 8. Percent Total AE Energy per class.

specimens became better wave-guides, which may
be an indication that they become denser. Thismay be related to the elasticity of concrete and theelastic or plastic collapse of voids. This wasobserved for loads up-to about 50% of maximum
load. As specimens age, the initial behavior is ofvariations in AU energy with a general trend for
decrease. The initial fluctuation and the generaldecrease tendency may indicate some sort of
micro-structural damage.

Fig. 9. Typical behavior of Class 6 (*) and Class 2(.) with AU energy in normal and log scale.

Using ultrasonic velocity measurement [1], it can easily be observed that the velocity drops sharply atabout 80% max load. Velocity measurements are insensitive, though, to any changes the material
undergoes at relatively low loads and therefore the sensitivity of the technique in assessing minor
damage cannot be determined. Other AU characteristics vary significantly as the load is increased fromzero to some value whereas the velocity of the wave remains constant and can thus provide more
information about the material’s behavior.
DISCUSSIONAs mentioned, the behavior of AU signals is unique in the sense that the specimens become betterwave guides as the load is applied. After a certain load some break-down appears to take place and thesignals’ energy decreases. Waveform analysis using Fast Fourrier Transforms (FFT) did not indicate
any significant change in the frequency spectrum. FFTs of AU signals throughout a number of tests
were studied but the only information related to the total energy transmitted through the material withinsignificant frequency changes or shifts.
The behavior of AU signals can in general be correlated to AE and microscopy results. It is interesting
to observe the behavior of AU signals with respect to class 6. The increase or variations in the AU
signals’ energy appears at the same time with class 6, especially in younger specimens (2 and 7 days)(see Figure 9). Class 6 is composed of relatively high energy signals at small loads. The appearance ofa high energy class at small loads along with the change in AU characteristics may indicate a change inthe structure of the material, such as friction, void collapse etc. The energy contained in class 6 signals
remains constant for all specimen ages.
27

At early loads, when variations in AU characteristics occur, other classes of signals show variations in
the amount of AE produced, such as classes 3 and 2. These classes, as mentioned, may be correlatedwith mechanisms in the material and can thus affect AU.
CONCLUSIONSIt has been demonstrated in the present work that a combination of various techniques can provide
important information and better understanding of the results of each technique. In addition, applicationof pattern recognition techniques has proven a powerful tool for signal discrimination as a means foradvanced data analysis in Acoustic Emission.
The results are indicative of AU sensitivity and ability to assess damage in mortar specimens. AU canbe related to physical changes in the specimens and can thus provide information regarding theirinternal structure. This is crucial for the use of AU techniques utilizing signal characteristics as
opposed to ultrasonic velocity, in an attempt to assess early stages of damage in concrete. Althoughconcrete is somewhat different in its behavior than mortar, initial work has shown similar behavior andresearch is being focused in that direction.
REFERENCES[1] ASNT, Non-Destructive Testing Handbook, vol.7, “Ultrasonic Testing of Bridges and Buildings”,
pp. 680-684.
[2] S. Yuyama, T. Okamoto, M. Shigeishi, and M. Ohtsu, “Quantitative Evaluation and Visualizationof Cracking Process in Reinforced Concrete by a Moment Tensor Analysis of Acoustic Emission”,
Materials Evaluation, 53(6), 1995, pp. 751-756.
[3] S. Yuyama, T. Okamoto, T. Kamada, M. Ohtsu, T. Kishi, “A Proposed Standard for Evaluating
Structural Integrity of Reinforced Concrete Beams by Acoustic Emission”, Progress in Acoustic
emission VIII, The Japanese Society of NDI, 1996, pp. 295-304.
[4] M. Uchida, T. Okamoto, T. Shibata, D. Mori, and M. Ohtsu, “Structural Integrity Evaluation on
Reinforced Concrete Members”.
[5] A. Tsimogiannis, A. Anastassopoulos, “Experimental Set-up Design for Acousto-Ultrasonics on
Concrete Specimens”, MHKKYNES Project, Athens, 1999
[6] Envirocoustics ABEE, “Noesis Reference Manual”, rev.0, 1999.
[7] ASTM 856-83, “Standard Practice for Petrographic Examination of Hardened Concrete”.

28CONCRETE CROSSBEAM DIAGNOSIS BY ACOUSTIC EMISSION METHOD
Zdenek WEBER, Petr SVADBIK, Marta KORENSKA, Lubos PAZDERATechnical University of Brno, Brno, Czech Republic
ABSTRACTCentred cyclic bending load was applied to a reinforced concrete crossbeam. Crack controls werecarried out by AE method and simultaneously by visual reference. Simultaneously, the rise and growthof the cracks were measured. The amplitude of AE events depends on the crack velocity, which itself
depends on the stress intensity of the crack. The increase of AE energy and counts during cyclic loading
indicated the absence of Kaiser effect in the reinforced concrete crossbeam. The frequency content of
AE signals is the function of the transducer's frequency response. The experiment demonstrates highsensitivity of the AE method at monitoring stress states. These results provide information about
behaviour of building structures in standard and extreme situations.
INTRODUCTION
In a project of new variable bridge system with a span of 6 to 30 m, the most important connection was asteel concrete girder with a monolithic steel-reinforced concrete crossbeam (Fig. 1,2,3). The crossbeam
model was designed and made at scale 1:1 and was tested all at Brno University of Technology. On thebasis of positive results by numerical analyses and loading tests, it has resulted in building the integrated
bridge system across the river Ploužnice in Stružnice at Ceska Lipa at end of year 1999. The
experimental check of reliability of coupled girder, the detection of transmission of tension in the areasof negative moment under hold and ways to find fault of the construction before the exhaustion of theultimate tensile capacity were the main aims in this work.

Fig. 1. Reason about segment test model in longitudinal direction.

A part of static load test of the model steel-concrete monolithic crossbeam was:- a few cycled loading (100 cycles each) with subsequent measurement of its ultimate loading-capacity,- the behaviour of the construction at cracks and their expansion (acoustic emission method)- monitoring responses on this loading by X-ray non-destructive testing on the shear connector.

29

Fig. 2. Select test model.

Fig. 3. Tested model of composite steel concrete girder.

This article describes measurement results and monitoring by acoustic emission (AE) method,verification of correlation between fatigue and AE activities, generated during the controlled cyclicalload test.
The monitoring of AE, generated by cracks during the tests, proceeded on the test specimens aged 7, 28
and 90 days and further on the final crossbeam model from February to the end of April 1999 (Fig. 4,5).

Fig. 4. Age 7 days, three points bending load.

30

Fig. 5. Age 28 days, four points bending load.

EXPERIMENTAL SET UP
Following AE systems were used in the cyclic tests:- 6 channel PAC LOCAN 320 system with preamplifiers and filters. The four channels were arrangedon planar localisation in the middle part of upper surface of the girder (in corners of area 40 x 60 cm2).
These were piezoelectric sensors with the resonance frequency of 225 kHz, The fifth channel with abroadband (from 100 to 1000 kHz) sensor and the sixth one with a resonance (225 kHz) sensor wereplaced at the bottom of the steel frame in the lower parts of the crossbeam.
- the notebook CARRY 6500 with an extension box containing 3 measuring cards for monitoring andsampling AE signals and collecting loading information.
RESULTS
The measuring of ultimate tensile capacity of the model crossbeam was achieved with loading graded in% from theoretically computed ultimate load-carrying capacity. It is at 30, 60, 90, 100, 150 and 200 %.
Simultaneously, the start and development of cracks were measured. The measurement results areshown in Fig. 6 to Fig. 11. The solid line in these graphs presents the cumulative AE, and the dot-anddash line gives the level of force.
Fig. 6: The model was first step-by-step loaded to 20, 40 and 60 kN. Already from 40 kN, there areindicated structural disturbances, which were visually checked and drawn in after each stop of loading.
Fig. 7: The holds were removed and model was permanently loaded by dead weight (about 19.3 kN).
Then, it was further super-loaded by a press to 60.8 kN. Recorded fall in tension load was caused by adefect of hydraulic pumps.
Fig. 8: In this period, the measurement model was subjected to 20 load cycles from 17.8 to 80.9 kN. In
figure, it is evident that Kaiser phenomenon disappears in the initial three cycles.
31

Fig. 9: The model was put through variable loading in the remaining 80 cycles with the force range
being the some as the previous experiment (below). The loading characteristic was deleted from graph tobetter show results.
Fig. 10: In this test period, the model was loaded step by step from 82 kN to 141.5 kN and then to 168
KN. There was model evaluation, visually recorded new cracks and then it was examinedradiographically at the surroundings of selected shear connectors. The crossbeam loading continued
until 252 kN; it is 1.5 times the theoretical ultimate load-carrying capacity. In this moment, the sample
showed a deflection of 32 mm. The next load step was not applied by reason of double defects of thepump.
Fig. 11: In this last cycle, the model was loaded to the previous maximum value of 252 kN. After this,load was raised step by step to 275 kN. Shortly after reaching this value, the general collapse of thesteel-concrete girder occurred; namely, first, local crushing of the lower left parts of pressured flange
and followed by the local warp of the right parts of pressure flange. Very fast fall of force followed andexperiment was finished.
CONCLUSION
The AE measurement demonstrates its extreme sensitivity to the start and successive development ofbend and shear cracks in tested concrete specimens and the whole steel-concrete construction. AE
signals contain information to enable the precise determination of cracks and their subsequent activities
that affect the qualities of the tested structure. The loss of Kaiser phenomenon indicates accurately the
moment of tested matters passing from the elastic to plastic material status. Here, AE signals haveespecially high amplitudes (over 70 dB).
This research has been conducted by CEZ J22/98 No.~261100007 and GACR 103/97/P140.
REFERENCES[1] Strasky, J., Svadbik, P., Schmid, P.: Experimental Verifying of Reliability Coupled Steel Concrete
Girder with Monolithic Crossbeam, CV/69/98, October, 1999, pp. 53.

32

Fig. 6

Fig. 9Fig. 7

Fig. 10

Fig.. 8Fig. 1133

WAVEFORM BASED ANALYSIS TECHNIQUES FOR THE RELIABLE
ACOUSTIC EMISSION TESTING OF COMPOSITE STRUCTURES
M. SURGEON, C. BUELENS, M. WEVERS*, and P. DE MEESTER*METALogic n.v., Heverlee, Belgium, *KU Leuven, Leuven, Belgium

ABSTRACT
Although the acoustic emission (AE) technique has now aged for more than thirty years, the analysis
techniques that are used in practical field testing are still very basic. The last decade has seen agrowing awareness of the dangers involved in reducing an AE signal to its basic parameters and thus
eliminating most of the information. This awareness has resulted in the development of practical AE
analysis techniques that are based on the complete waveform rather than on the parameters. These
techniques also make use of a growing insight into the theoretical principles of AE signal generationand propagation, which is evidenced by e.g. the use of the classical plate wave theory. This paper will
lay out the principles of the classical plate wave theory and will demonstrate how this simple theory
combined with a complete waveform acquisition can lead to a more reliable testing of composite
materials.
INTRODUCTION
The acoustic emission technique (AE) has for many years been considered as the prime candidate forstructural health and damage monitoring in loaded structures. It offers the user a number of inherent
advantages, the main of which are its continuous and in situ monitoring capabilities and the possibility
to examine the whole volume of a structure simultaneously with a limited number of sensors.
The increasing use of composite materials in loaded structures and their complex damage development
has created a need for an efficient and reliable NDT technique that can be used during the service life ofthese materials. AE clearly has the potential to serve as a continuous damage detection technique forcomposites and during the past decades many studies have been undertaken to develop the technique tohigher levels of performance. Three main types of data analysis have been extensively explored so far.
That is, AE activity analysis (focussing on the amount of signals that are detected during a test), AE
parameter analysis (studying evolutions in the basic signal parameters like amplitude, duration orenergy) and AE frequency analysis (analyzing the frequency content of AE signals). Although many
applications can be envisaged in all of the areas where composite materials are being used or could be
used, it is surprising to see that the number of practical applications exploiting the AE technique hasremained relatively limited. The main reasons for this are the limitations of the analysis techniques
discussed above, which make it difficult to extend laboratory results to industrial structures. Commonly
encountered problems are the large amounts of gathered data, the difficult separation of noise from realdamage signals, the material anisotropy, the large wave propagation paths, etc. Summarizing theseobservations, one can state that AE has remained mainly a qualitative technique and that its further
acceptance and practical use require a more quantitative approach based on theoretical concepts, which
can reliably take into account source phenomena and wave propagation effects.
Another attempt at providing a better theoretical background for AE testing is now known as modal
acoustic emission (MAE) or waveform based acoustic emission. MAE starts from the observation that
AE waves are mechanical in nature and should therefore be treated as such. Following the general34

theory of wave propagation in solids, AE waves should propagate through a structure in a variety ofmodes. The separation of these modes at the sensor could make it possible to extract information about
the source event that produced the wave. Additionally, wave propagation theory offers theoretical tools
to study the influence of attenuation and dispersion.
This paper will first provide a simple, yet practical, theoretical background for AE signal analysis. It willthen be demonstrated how this theory can be used to make composite testing more reliable bydemonstrating, on a laboratory scale, how signal recognition and discrimination, noise elimination andsource location can be performed in a more consistent manner.
PLATE WAVE THEORY
A comprehensive overview of wave propagation theory as it applies to solid materials can be found inreference 1. Solutions to wave propagation problems in structures of arbitrary geometry can be obtainedby using the three-dimensional equations of the elasticity theory. In the case of plate-like structures,
however, a simpler theory can be used, i.e. classical plate wave theory. According to this theory,mechanical waves propagate through plate-like structures in three modes: the extensional mode (particle
displacement in the plane of the plate and in the direction of wave propagation), the flexural mode(particle displacement perpendicular to the plane of the plate) and the shear mode (particle displacementin the plane of the plate and perpendicular to the direction of wave propagation). MAE has up to now
made extensive use of the extensional and the flexural mode.
Based on the classical plate wave theory, the velocities of propagation of both modes can be calculated.
Plate wave theory predicts a non-dispersion extensional mode: all frequency components of this mode
propagate at the same velocity. Although practical results show that this is a reasonable approximation,
it will not be satisfied completely. An analysis of the extensional mode based on higher order theories
shows that the extensional mode exhibits limited dispersion behavior in which the velocity of
propagation decreases with increasing frequency. For the flexural mode, plate wave theory predicts
dispersion behavior in which the velocity increases with increasing frequency. A more detailed overview
of classic plate wave theory and the way it has been applied in AE testing can be found in references 26.
MATERIAL AND EXPERIMENTAL TECHNIQUESAll tests performed in this study made use of a carbon/epoxy composite material, which was produced inthree different 8 ply cross-ply lay-ups: [0, 903 ]s, [02 , 902 ]s and [03 , 90]s. Tensile samples were usedhaving a length of 150 mm, a width of 12 mm and a thickness of 1 mm. Tensile tests were performed on
the MTS 810 loading frame. All tests were monitored by attaching two broadband AE sensors (Digital
Wave Corp., B1025) to the specimens. The AE signals captured by these sensors were fed into a
Fracture Wave Detector (Digital Wave Corporation) system.
RESULTS
Wave mode recognition
Figure 1 shows a signal that was generated by transverse matrix cracking in a [0, 903 ]s laminate. The
figure shows both the time and frequency domain of the signal. As is indicated on the time domain
graph, the signal can generally be divided in two zones. The wave package that arrives first at the sensor
shows a behavior in which the period of the subsequent cycles decreases with increasing time. The
35

frequency content of this mode increases with increasing time which also means that the lower
frequency components arrived first at the sensor and propagated at the higher velocities. As was
discussed before, this dispersion behavior is typical for the extensional mode. The wave package that
arrives at approximately 40 µs exhibits a behavior, in which the period of the subsequent cycles
increases with increasing time. The frequency content of this mode decreases with increasing time andthus the higher frequency components arrived first at the sensor and propagated at the higher velocities.
This dispersion behavior is typical for the flexural mode.
Both plate wave modes can be recognized in this signal. Generally, the extensional mode propagates at ahigher velocity than the flexural mode and exhibits a higher frequency content. As is indicated on thefrequency domain graph, the range between 400 and 800 kHz corresponds to the extensional mode andthe range between 0 and 200 kHz to the flexural mode.
E

Amplitude (V)

0.4

a

F

0.2
0
-0.2

0

10

20

30

40

50

60

70

80

90

1400

1600

1800

2000

-0.4
Time (µs)
F

EPower (dB)

0.3
0.2
0.1
0
b

0

200

400

600

800

1000

1200

Frequency (kHz)Fig. 1: Matrix crack signal generated in a [0, 903]s sample: a) time domain, b) frequency domain(E: extensional mode, F: flexural mode)

The wave package that arrives at approximately 40 µs exhibits a behavior in which the period of thesubsequent cycles increases with increasing time. The frequency content of this mode decreases withincreasing time and thus the higher frequency components arrived first at the sensor and propagated at
the higher velocities. This dispersion behavior is typical for the flexural mode.
Both plate wave modes can be recognized in this signal. Generally, the extensional mode propagates at ahigher velocity than the flexural mode and exhibits a higher frequency content. As is indicated on thefrequency domain graph, the range between 400 and 800 kHz corresponds to the extensional mode andthe range between 0 and 200 kHz to the flexural mode.

36

Discrimination between damage phenomena
The two main damage phenomena that are active during tensile testing of a cross-ply composite
laminate are transverse matrix cracking at the early stages of testing and fiber fracture during the laterstages of testing. An example of a matrix crack signal as it was generated in the [0, 903 ]s lay-up was
given in Fig. 1. Further investigation of the matrix crack AE signals revealed that the frequency content
of these signals increased as the 90-ply thickness decreased.

Amplitude (V)Figure 2 shows a signal that was attributed to fiber fracture, due to the load level at which it appeared.
The signal exhibits a dominant extensional mode and its main feature is its higher frequency content ascompared to the matrix crack signal. This is reflected in the frequency domain: the signal exhibits large
frequency components above 1000 kHz. This is in contrast with the matrix crack signal (see Fig. 1) inwhich no significant content could be observed above 1000 kHz. It thus seems feasible to basediscrimination between different damage phenomena on the properties of the plate wave modes.

a

0.6
0.4
0.2
0
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-0.6

10

20

30

40

50

60

70

80

90

1400

1600

1800

2000

Time (µs)

Power (dB)

0.3

b

0.2
0.1
0
0

200

400

600

800

1000

1200

Frequency (kHz)Fig. 2: Fiber fracture signal generated in a [0, 903]s sample: a) time domain, b) frequency domain

Noise elimination
One of the main problems preventing the widespread use of AE in practical applications is theelimination of noise signals from the data set. Based on the classic parameter based analysis procedures,
this elimination has proven to be very difficult. As will be shown here, noise signals can be eliminated ina more consistent manner based on the complete waveform and the modal properties. The main noise
phenomena that are active during laboratory type tensile tests are EMI (electromagnetic interference)and grip noise. Figure 3 shows an example of an EMI signal, measured by two sensors. The
37

shape of these waves is quite different from the ones shown above. Both signals are detected at exactly
the same time. Additionally, the signals are very high frequent in nature and do no exhibit plate wave
characteristics or propagation effects. It should be noted here that a traditional AE analysis would havetreated this signal as a signal with amplitude comparable to the one of the real damage signals, a shortduration and a high number of counts. Reducing the wave to these parameters would have made it very
difficult to eliminate this noise type.
Figure 4 shows an example of a grip noise signal. A clear signal was only observed at sensor 2. The
signal appears not have propagated to sensor 1, which suggests that it originated outside the sensor
region, at the sensor 2 side. The signal at sensor 2 does exhibit plate wave characteristics, but they aremarkedly different from the ones of the damage signals as the low frequency content is much higher.
Both examples demonstrate how more consistent noise elimination should be based on the complete
waveform and the modal properties of AE signals.
Source location

Amplitude (V)AE does not only offer the user the possibility to determine when damage occurs and what type ofdamage is active, but it also makes it possible to obtain information about the spatial location of thedamage. Based on the arrival time of an AE wave at a limited number of sensors, a source location canbe calculated, if the velocity of propagation in the material under study and the position of the different
sensors are known. The key element in a good and accurate location procedure is the determination of
the arrival times of the AE wave at the different sensors. Arrival times are traditionally determined byusing a fixed threshold value: the arrival time of a wave is the point where it first crosses the threshold.
This procedure is prone to an error as it does not take into account the modal nature of the AE wave.
Using a fixed threshold, one can never be sure which part of the wave first reaches the threshold. A good
location implies that the arrival times are determined on a part of the wave that has traveled with thesame velocity to all of the sensors.
0.3
0.2
0.1
0
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-0.2
-0.3

10

20

30

50

60

70

80

90

60

70

80

90

Time (µs)

aAmplitude (V)

40

0.3
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-0.2
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10

20

30

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50

Time (µs)

bFig. 3: EMI noise signal: a) sensor 1, b) sensor 2.
38

Amplitude (V)
a

0.15
0.1
0.05
0
-0.05 0
-0.1
-0.15

10

20

30

40

50

60

70

80

90

60

70

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90

Amplitude (V)Time (µs)

0.15
0.1
0.05
0
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10

20

30

40

b

50

Time (µs)
Fig. 4: Grip noise signal: a) sensor 1, b) sensor 2

A detailed discussion of this problem can be found in reference 8. During the tests performed in thiswork, two sensors were used and a linear location procedure was applied. Some signals were observedthat could pose problems to the classic AE location procedure as for some threshold values, the arrival
times are determined on the extensional mode at one sensor and on the flexural mode at the other sensor.
Using a fixed wave velocity, this led to location errors up to 35 %. This demonstrates that for an
accurate source location, the modal nature of AE waves should always be taken into account.
CONCLUSION
Waveform based AE analysis techniques use simple theoretical concepts as a theoretical background to
study AE signals generated in composite plates. Here, it was demonstrated how plate wave modes canbe recognized in real damage AE signals. Furthermore, a number of examples showed how thetechnique can discriminate between different damage phenomena, how it can be used to consistently
eliminate noise signals from the data set and how it can improve on the location procedures as they are
offered by traditional AE systems.
On a more general note, it is believed that more reliable techniques are needed if AE is to further
improve into a generally accepted testing technique. Potential users of the technique are still put off by
the empirical nature of the classic analysis techniques, which lack a theoretical basis and a generalvalidity. It is believed that the evolution into an analysis of the complete waveform and its properties canbe a big step into the right direction.

39

REFERENCES(1) Graff, K.F., Wave motion in elastic solids, Dover Publications, New York, 1975
(2) Gorman, M.R., Plate wave acoustic emission, J. Acoust. Soc. Am., 1991, 90(1), 358-364(3) Gorman, M.R., Prosser, W.H., AE source orientation by plate wave analysis, Journal of Acoustic
Emission, 1990, 9(4), 283-288(4) Gorman, M.R., Ziola, S.M., Plate waves produced by transverse matrix cracking, Ultrasonics, 1991,
29, 245-251(5) Prosser, W.H., Advanced AE techniques in composite materials research, Journal of Acoustic
Emission, 1996, 14(3-4), S1-S11(6) Ziola, S.M., Gorman, M.R., Source location in thin plates using cross-correlation, J. Acoust. Soc.
Am., 1991, 90(5), 2551-2556

40

OPTICAL FIBRES FOR IN SITU MONITORING THE DAMAGE
DEVELOPMENT IN COMPOSITES AND THE RELATION WITH ACOUSTICEMISSION MEASUREMENTS
M. WEVERS, L. RIPPERT and S. VAN HUFFEL
Katholieke Universiteit Leuven, Leuven, Belgium
ABSTRACTThe complex damage development process in composite materials demands a system that could
continuously monitor their damage state in particular structural applications. Fibre optic sensors
embedded in the composite material could offer an alternative for the robust piezoelectric transducers
used for acoustic emission (AE) monitoring. For simplicity and robustness reasons, intensity-modulated
optical fibres were chosen to detect damage in CFRP composite laminates based on the microbending
concept. Advanced signal processing techniques based on time-frequency analysis have been applied on
the signals collected during loading of the CFRP composites. The short-time Fourier transform has
been computed and noise reduction algorithms (adaptive filtering and spectral subtraction filtering)
have also been used. The transient signals being detected can be correlated with AE signals, analysedwith a modal AE system. The signals are attributed to the initiation of damage in the materials and thus
the optical signal contains, besides the level of overall strain, information in the elastic energy released
whenever damage is introduced in the host composite.
INTRODUCTION
The emergence of optical fibre communication technologies in the 1970’s has enabled the development ofembedded optical sensors for process condition monitoring and for smart materials/structures applications (1,2).
We also initiated a study to incorporate optical fibres in carbon-fibre reinforced epoxy laminates in order tomonitor the fatigue behaviour characterised by a gradual damage development. The method of embedding theoptical fibres and their influence on the mechanical behaviour of the host material has already been evaluated fora number of laminates and the embedding positions have been optimised (3,4,5).
The present paper is focused on the signal processing of the optical fibre data, done in collaboration with the
Department of Electrical Engineering. The signal processing has been done in order to find a relation with theinternal strains in the composite produced on one hand by the external loading (stresses and temperature) andon the other hand by the internal damage development. Especially for the latter an attempt will be made tocorrelate the signals measured with the AE technique, in which we already have proven its competitiveness morethan once (6,7) and the signals of the embedded optical fibres. With optical fibres embedded in composite
materials and intelligent data processing of the optical fibre signals, one can integrate a NDT-system into thiscomplex material, or derived component or structure, similar to the neural system in a human body.
The fibre optic sensors have several advantages compared to the electronically based sensors like piezoceramics such as light-weight, all passive configurations, low power utilisation, immunity to electromagnetic
interference, high sensibility and bandwidth, compatibility with optical data transmission and processing, long
lifetimes and low cost (as long as using silicon fibres). Disadvantages exist with reparability as long as optical41

fibres have to be integrated into the material and placed according to major occurring stresses and strains to
obtain reliable data.
PRINCIPLE OF OPERATION
To detect damage, two kinds of optical sensors can be used: phase-modulated sensors (interferometers) andintensity-modulated sensors. The former is usually more sensitive but also very fragile. A sensing system, which
is simple and robust enough for industrial use, is offered by intensity-modulated optical sensors. The intensity
variations of the transmitted light are caused by a perturbing environment. If for instance the optical fibre is bent
(8), small amounts of light are lost through the cladding because the condition of total reflection is violated. The
amount of intensity loss depends on the amount of bending. The stress field in a composite material is influenced
by the external loading and the internal damage in the material. This may cause the optical fibre to bend in thematerial so that a decrease in intensity of the transmitted light can be seen. Initiating and growing damage isassociated with acoustic stress waves propagating in the material. When a wave encounters an optical fibre, thisbends locally and so some light might also be lost. A high sampling rate (SR) can be used to detect thosetransient signals released by matrix cracking, delamination or fibre fracture phenomena. To reveal thisinformation from the optical signals, signal analysis tools such as filtering, time analysis and time-frequency
analysis is required.
EXPERIMENTS
Our previous study addressed the choice of fibres, the embedding procedure and the influence of the opticalfibres on the mechanical properties (monotonic tension, three and four point bending and tension fatigue) ofdifferent carbon-epoxy composite laminates. The next step was to evaluate the performance of the optical fibre
NDT system.

Fig. 1: The sensing system

1 : Laser source
2 : Optical coupler
3 : Optical fiber
4 : Composite specimen
5 : Photodiode + amplifier
6 : Computer (oscilloscope card)
7 : Workstation
8 : AE sensor

8
5
1

2

3

2

3

4

6
8

7

Laminates were produced from a Vicotex 6376/35/137/T400 C/epoxy prepreg. The prepreg was cut andstacked into a (02°, 904°)s lay-up. The optical fibre was embedded in the 90° direction in the middle plane of42the specimen. A polymeric bore tube was put around the optical fibre at its exit point from the composite
specimen. It shrank around the fibre during the cure and so protected this weak point. The samples tested had
the following dimensions: 150 mm length, 25 mm width and 1.2 mm thickness.
A He-Ne laser source was used to power the multi-mode optical fibre embedded in the carbon-fibre reinforced
composite material. The output light intensity was collected by a photo-diode, and was sent to a computer via
an oscilloscope card as seen in Fig. 1 of the sensing system.
The optical signal post-processing was done on a SUN workstation using MATLAB software, in particular
the signal processing toolbox. A program was written to filter the signal, to compute its STFT and to visualise
the changes in its power spectrum over time (9). Additional tools were developed to extract damage related
information from this time-frequency analysis.
An acoustic emission (AE) system was also used to monitor the damage development in this composite
laminate: the Wave Explorer from Digital Wave Corp. This system is equipped with broadband sensors (Digital
Wave B1025) with a nearly flat frequency response in the 50–3000 kHz frequency range. It uses the platewave theory as a theoretical background and analyses the waves according to their mechanical nature, namely,
extensional and flexural waves. It is called a modal AE (MAE) system (10,11). MAE allows a more convenient
way to identify the kind of damage by looking at the frequency content of the acoustic waves produced. The
presence (or absence) of extensional and flexural modes is the key to the damage mode characterisation. It has
been proven to work in an efficient way for matrix cracking and fibre fracture (12). It also allows a clear
recognition of noise grip and EMI.
On the oscilloscope card, three channels were used. One channel was used to collect the optical signal, anotherchannel collected the optical signal filtered using a capacitor to get only the ac component amplified 11.5 times,
and a third one to collect a trigger signal sent by the AE system each time an AE event is detected. The
sampling rate (SR) was set to 10 kHz.
Tensile tests were performed on Instron 4505 universal testing machine with a 100 kN load cell. To prevent
grip failure, aluminium end tabs were bonded to the specimens using a two-components Araldite 2011 epoxy
glue. The displacement rate was 0.5 mm/min and a Labview program drove the tensile machine and theoscilloscope card.
RESULTS AND DISCUSSION
Ten specimens were tested and at the beginning of each tensile test, pencil-lead break tests were performed forcalibration purpose.
Low pass filtering
The tensile load curve of one test is shown in Fig. 2. During the first 90 s, several pencil-lead break tests were
performed to calibrate the AE system. Then, the loading was applied until the final fracture. After 261 s of test,
there was a sudden decrease in the applied strain due to some damage near one of the aluminium tabs. Figure 3
shows the optical signal for the same test. A 4th-order low pass (LP) Butterworth filter was applied with a 5 Hz
cut-off frequency. The curve has been reversed for better comparison with the preceding one. The optical
43

intensity starts to decrease when the loading starts to be applied. A low-frequency oscillation can be seen on
the curve and is due to vibrations produced by the Instron machine. The final fracture is clearly seen and thestrain release at 261 s also appears as a change of slope on the curve. It is thus shown that the optical signal
contains information on the strain produced by external loading.

Fig. 2: The loading versus time curve.

Fig. 3: The optical signal filtered with a low pass
Butterworth filter.

Some sharp spikes are also hidden in the curve of Fig. 3. To see them more clearly, the same low pass filter
was applied to the ac component of the optical signal (amplified 11.5 times). As can be seen on Fig. 4 thesignal is constant except for some big spikes. The time instant at which those spikes appear is the same as thetime of occurrence of some AE events. These AE events can be related to damage inside the material, so those
spikes can also be related with the damage, which causes a sudden strain energy release in the material. By
removing the 5 digits offset and taking the absolute value of this signal, a threshold can be set and damage
detection is achieved.

Fig. 4: The ac component of optical signal filtered with a low pass Butterworth filter.
44

The main limitation is the sensitivity; the events detected by this method are only the most energetic ones. The
less energetic events detected by AE cannot be seen on the optical signal or are smaller than the curve
oscillations and can therefore not be detected by the threshold technique. Removing the polyimide coating fromthe optical fibre may increase the system sensitivity.
Most of the high-frequency components are filtered out with this low-pass filtering technique so the signal cannot
be used for damage identification. Another drawback is that changes are less sharp and some delay may also
appear. This method shows that damage detection is possible with intensity modulated optical sensors based on
the microbending concept, but some more advanced signal analysis techniques are required to have informationon the kind of damage so this low-pass filtering has to be replaced.
Noise reduction
If one has to look for small effects on the optical signal the Signal to Noise Ratio (SNR) has to be increased.
The biggest noise source is from the laser power supply (50 Hz and the harmonics from the net). An adaptive
filter was used to remove this noise only up to 1 kHz (13) because this filter was computation time consuming.
Subsequently, spectral subtraction, a filtering technique used in speech processing (14), was applied to see
more clearly the expected ‘optical events’. This technique requires that the background noise environment
remains locally stationary to the degree that its expected spectral magnitude value just prior to an expected
event equals its expected value during the event. It is also assumed that significant noise reduction is possible byremoving the effect of noise from the magnitude spectrum only.
These techniques were applied to pencil lead break tests (Fig. 5) and on optical signals from real damage (asindicated by AE) during tensile tests (Fig. 6). They allow a good noise reduction without reshaping too muchthe signal.

Fig. 5: The original optical signal, the signal filtered with an adaptive filter andwith the spectral subtraction method of a pencil lead break.
45

Fig. 6: The original optical signal, the signal filtered with an adaptive filter andwith the spectral subtraction method of a real damage event.

46

Fig. 7: Final fracture of a specimen in the time domain (top), its spectrogram (middle) and the frequency projection of thespectrogram (bottom).

Fig. 8: The filtered optical signal of a pencil-lead break in the time domain.

Time-frequency analysis: STFT
Since the expected signals were non-stationary (transient), their frequency content, visualised by the Fouriertransform, varied in time. The short-time Fourier transform (STFT) can be used to visualise the frequency
content of a signal over time (13). This so-called spectrogram is visualised in Fig. 7 (middle) for the 400–3500
Hz frequency range. This figure corresponds to the specimen final fracture event that can be seen in the time
domain in the top curve. For better view and analysis, the frequency projection of the spectrogram can becomputed (see bottom).
Damage identification
The filtered optical signal corresponding to a pencil lead break was extracted and a time-frequency analysis(STFT) was performed. Both the AE time signal and the optical time signal (respectively seen in Figs. 8 and 9)
show a small extensional component followed by a big flexural mode. This is coherent with what can be
expected from a pencil lead break test done on the surface of the specimen. Waves produced by a pencil-leadbreak test are quite similar to those produced when damage occurs in a composite material.
47

Figure 9: The AE signal in the time domain at two sensors typical for an in-plane lead break test.

Figures 10 and 11 show the ac optical signals corresponding to AE events recorded during the tensile test
(middle and bottom). The time-frequency analysis clearly identifies special features in the signals, which can be
attributed to matrix cracking or fibre fractures based on their time of occurrence.

Fig. 10: The ac optical signal, the filtered signals (adaptive filter and spectral subtraction method) and the frequency
spectrogram of an AE event detected in the middle of the tensile test.

48

Fig. 11: The ac optical signal, the filtered signals (adaptive filter and spectral subtraction method) and the frequency
spectrogram of an AE event detected at the end of the tensile test.

CONCLUSION
It has been shown that an intensity modulated optical sensor based on the microbending concept can be used
for continuous damage monitoring of a carbon-fibre reinforced laminate composite. The NDT system is simple
and robust, but requires some advanced signal analysis tools like adaptive filtering, spectral subtraction filtering,and time-frequency analysis (STFT).
The intensity-modulated optical fibre sensor (microbending principle) can detect pencil-lead breaks, the overall
strain in the composite and strain variations due to damage development during loading. The similarities
between optical and MAE signals should permit damage identification. The damage location has not beenstudied so far, this may require to embed several optical fibres in the specimen.
ACKNOWLEDGEMENTS
The authors would like to thank Ing. J. Vanhulst for his precious help with the data acquisition system, and K.
Eneman for providing the code for the applied filtering techniques.

49

REFERENCES
1. D. BUTTER, G.B. HOCKER, Applied Optics, 17(18), (1978), 2867.
2. N.D. GLOSSOP and al., Optical fibre damage detection for an aircraft composite leading edge,
Composites, 21, (1990), 71-80.
3. SURGEON, M., WEVERS, M., Static and dynamic testing of a quasi-isotropic composite with embedded
optical fibres, Composites Part A, 30(4), (1999), 317-324.
4. SURGEON, M., WEVERS, M., Using optical fibre technology to develop a damage detection sensor forcomposite materials: preliminary research, Proc. the 7th ECNDT, Copenhagen, May 1998, Vol. 1, pp.
1048-1056.
5. SURGEON, M., WEVERS, M., Quantifying the damage state of a quasi-isotropic CFRP with embedded
optical fibres during fatigue testing using acoustic emission and microfocus radiography, Proc. the 7th
ECNDT, Copenhagen, May 1998, Vol. 1, pp. 193-200.
6. WEVERS, M., VERPOEST, I, DE MEESTER, P., Identification of fatigue failure modes in carbon fibre
reinforced composites with the energy discriminating acoustic emission method, Journal of Acoustic
Emission, 8(1), 1989, S272-S276.
7. WEVERS, M., Listening to the sound of materials: acoustic emission for the analysis of material behaviour,
NDE&E, 30, 1997, 99-106.
8. BERTHOLD III, J. W., Historical review of microbend fiber-optic sensors, Journal of lightwave
technology, 13(7), 1995, 1193-1199.
9. S. VAN HUFFEL, J. CRAEMERS, B. LENAERTS, H. DANIELS, G. NAULAERS, P. CASAER,
Episodes of apnea and bradycardia in the preterm newborn: impact on cerebral oxygenation measured bynear infrared spectrophotometry, Proc. EUROPTO Conf. on Photon Propagation in Tissues IV, Sweden,
1998, SPIE, Vol. 3566, pp. 112-123.
10. S. ZIOLA, I. SEARLE, Automated source identification using modal acoustic emission, Review of Progressin Quantitative Nondestructive Evaluation, 1997, Vol. 16A, pp. 413-419.
11. M.R. GORMAN, Progress in detecting transverse matrix cracking using modal acoustic emission, Review
of Progress in Quantitative Nondestructive Evaluation, 17A, 1998, 557-564.
12. SURGEON, M., WEVERS, M., Modal analysis of acoustic emission signals from CFRP laminates,
NDT&E International, 32, 1999, 311-322.
13. J.G. PROAKIS, D.G. MANOLAKIS, Digital signal processing: principles, algorithms, and applications, 3rded., Prentice Hall, Upper Saddle River, NJ, 1996.
14. S.F. BOLL, Suppression of acoustic noise in speech using spectral subtraction, IEEE Transactions on
acoustics, speech, and signal processing, ASSP-27, N° 2, 1979.

50

LAMB WAVE SOURCE LOCATION OF IMPACT
ON ANISOTROPIC PLATESH. YAMADA, Y. MIZUTANI, H. NISHINO, M. TAKEMOTO and K. ONO*
Aoyama Gakuin University, Tokyo, JAPAN, *UCLA, Los Angeles, CA, USA.
ABSTRACTThis study reports the development of a new source location method using Lamb waves on anisotropic
carbon-fiber reinforced plastic (CFRP) plates. We monitored Lamb waves produced by pencil-leadbreaks and steel-ball impacts using four resonant-type AE transducers mounted on 300-mm squarecorners within 500-mm square plates and determined arrival time of 65 kHz A0 -Lamb waves, extracted
by using a wavelet transform. For square arrangement, source location was first narrowed into one offour quadrants by the first arrival to the nearest sensor. Next, the source location in the particularquadrant was determined by sequential iteration. Here, the measured arrival-time differences werematched with those computed by moving a virtual source position in selected steps. We developed analgorithm providing automatic determination of arrival times and coordinate computation. It takes lessthan one second for each source location identification. Orientation dependence of Lamb waves for the
CFRPs are experimentally measured and included in the algorithm. Accuracy of the source location fora unidirectional-CFRP plate is better than 5.6 mm within the 300 x 300 mm square. The average errorof 2.4 mm was obtained for a quasi-isotropic CFRP plate. Results for several other CFRPs will bereported.
INTRODUCTION
Accurate source location of an impact and fracture is an important engineering problem for the precise
identification of a damaged area. The source location of acoustic emission (AE) signals is impracticalfor many structures of fiber reinforced plastics (FRPs) due to strong orientation and frequency
dependence of wave velocities (anisotropy and dispersion) and large attenuation. The velocity
anisotropy can be measured, and for samples of limited dimensions, we can achieve three-dimensionalsource locations within 1.0 mm of a microfracture source in unidirectional FRPs [1-3]. The source
location in thin plate is reduced to a planar (two-dimensional) problem, but a difficulty arises due to thedispersive nature of the Lamb waves [4]. Strong orientation dependence of Lamb wave velocities poses
another problem. Several recent studies [5-9] attempted to improve source location methods. For
example, Kwon et al. [9] recently utilized wavelet and inverse-wavelet transform for locating leadbreaks on aluminum and cross-ply CFRP plates. They utilized the arrival-time differences with thresholdcrossing method of single frequency A0 -component. This method improves the source location accuracy,
but the anisotropy issue remains unresolved. We have developed a new method for the source location
based on dispersive, anisotropic Lamb waves. Here, measured arrival-time differences are matched withthose computed by moving a virtual source position in small steps. We report the algorithm for source
location and source location accuracy of impact on CFRP plates.
SOURCE LOCATION METHOD
The present source location method consists of the following steps; 1) Perform wavelet transform ofdetected waveforms, extracting the A0 -mode component at a selected frequency. This frequency is
chosen to be the major component of the observed signals and to best differentiate the desired wave
mode from others. 2) Determine the arrival times of the detected waveforms. The arrival time is definedat the instant when the wavelet coefficient reaches 20% of the peak value. (The position of the peak51

value, or peak-arrival time, can also be used.) 3) Select a zone. Utilizing the sequence of the arrival
times, the source location is confined to a zone; e.g., one of the four quadrants for a square fourtransducer arrangement. 4) Determine the source location by sequentially minimizing the differences ofthe measured arrival times and the arrival time differences computed by moving a virtual source positionin the quadrant determined in the previous step. The position is moved by a preset amount (typically 10
to 20 mm) in the X- and Y-directions. When a minimum is found, the preset amount is halved and aminimum is searched again in the immediate neighborhood. This is repeated several times until a certain
level of accuracy is achieved. 5) In the calculation of step 4, experimentally measured, anisotropic groupvelocity of A0 -mode Lamb waves is used. The velocity is frequency dependent and the values mustcorrespond to those of the selected frequency.
MATERIALS AND VELOCITY PROFILES
We used in experiment three types of carbon-fiber reinforced plastic (CFRP) plates of 500 mm square.
One is 24-ply unidirectional (UD-)CFRP, where the X-direction refers to the fiber direction of UDCFRP. The second plate is 32-ply and quasi-isotropic (0o 4 /45o 4 /90o 4 /-45o4 )s and the X-direction is along
the 0o top/bottom lamina fibers. The third type is cross-ply CFRPs ((0o n /90o n )s, n=4, 6, 8) of various
thickness (2.0, 3.0, 4.0 mm). We measured the orientation dependence of A0 -Lamb waves, produced bypencil-lead breaks. The waves were detected by two transducers (PAC, type PICO) located at 40 and 90
mm along the angle θ (measured from the 0o fiber). Examples of detected waveforms and their wavelet
coefficients at 65 kHz, in the direction of θ = 0o (fiber direction), 30o and 90o for UD-CFRP, are shownin Fig. 1.

Fig. 1 Examples of Lamb waves and their 65 kHz components in UD-CFRP plate as a function of the
propagation direction θ.
The wavelet coefficients were extracted by a wavelet transform with Gabor function as the motherwavelet. Velocities were measured by dividing the inter-transducer distance (50 mm) by the first-peakarrival time differences ∆t of 65 kHz A0 -Lamb. Orientation dependence of 65 kHz A0 -Lamb velocities
for UD-, quasi-isotropic and cross-ply (thickness=2.0 mm) CFRP are shown in Fig. 2. Measured data
was approximated by a 6-th order polynomial equation (solid line). Velocity anisotropy of the quasi-

52

Fig.2 Orientation dependence of 65 kHz A0 -Lamb velocities in (0o4 /45o 4 /90o 4 /-45o4 )s CFRP plate (top),
UD-CFRP plate (the center) , and cross-ply CFRP of 2.0mm thickness (bottom).
53

isotropic plate is small, or 6 %, whereas a strong velocity anisotropy exists for the UD-CFRP with theratio of maximum to minimum velocities of 1.7. It is also noted that the velocity variation for the quasiisotropic plate is asymmetric to the Y-axis. That is, the velocity at θ = 135o (1312 m/s) is faster by 152
m/s than that (1160 m/s) at 45o probably due to the eight -45o plies at mid-thickness.
SOURCE LOCATION
Figure 3 shows the schematic illustration of source location method. Source location experiment usedfour AE transducers (PAC, type PICO; 4.0 mm diameter; nominal resonant frequency, 0.45 MHz). These
were mounted on the corners of a 300 mm square on 500 mm square CFRP plates. The origin of thecoordinate is set at the channel 1 transducer. Lamb waves were produced by simulated impacts (pencillead break and steel-ball drops). Outputs of the transducers were amplified 40 dB and digitized at aninterval of 800 ns with 1024 points at 10 bit amplitude resolution, and fed to a work station (Sun Spark
Station 5) for analysis. Wavelet transform is useful in getting arrival-time information with a high S/Nratio and sharp peak definition. Shown in the top row of Fig. 4 are examples of Lamb waveforms
produced on the quasi-isotropic CFRP plate by a pencil-lead break (left) and a steel-ball drop (right) at#2 position, corresponding to the coordinate (150, 225). These waves were monitored by channel-4transducer. The waves produced by the pencil-lead break contains both the S0 -Lamb component

Fig.3 Schematic illustration of source location method of Lamb waves produced
by impacts at #1 to #9 positions on CFRP plate.

54

Fig.4 Examples of Lamb waveforms (the top) produced by pencil-lead break (the left) andsteel ball drop (the right) on (0o 4 /45o 4 /90o 4 /-45o4 )s CFRP plate. The bottom two figures
represent the time transient of wavelet coefficients at 65 kHz.
(arriving first, high-frequency, low-amplitude) and the late-arriving large amplitude A0 -Lambcomponent. The wave due to steel-ball impact contains only the A0 -Lamb component, having lower
frequency and large amplitude. The bottom two figures represent the time transient of 65 kHz wavelet
coefficient, corresponding to the 65 kHz-components of the signals. The S0 -components can not be seenin these figures because of their low amplitude.
Selection of a particular frequency component for the source location depends on two factors. One is to
use the strong frequency components of propagating Lamb waves. This is affected by the mechanism ofwave generation, wave propagation characteristics and transducer parameters. The other is a sufficientvelocity difference of the S0 - and A0 -Lamb waves, so that the A0 -Lamb arrival is adequately separated
from the S0 -Lamb wave component. For example, in the quasi-isotropic CFRP plate used here, thevelocity of A0 -Lamb at 65 kHz is calculated as 1.3 mm/µs while that of S0 -Lamb is 5.5 mm/µs. These
were obtained by Adler's matrix transfer method [10]. At 250 kHz, both velocities are similar and it is
impossible to separate the A0 -Lamb from the S0 -Lamb waves. This means that the utilization of low
frequency Lamb waves is better for determining the arrival times of impact-induced waves. The use ofeven lower frequencies is limited by the lack of transducer sensitivity, although the attenuation is lower.
It is also expected that spatial definition becomes poor as the wavelength increases proportionately.

55

a. Source Location on Quasi-isotropic CFRP Plate
The source location on a quasi-isotropic CFRP plate was determined by first exciting Lamb waves bybreaking a pencil-lead and by dropping a steel-ball (7 mm diameter, 1.4 g mass, 20 mm height) at
position #1 to #9 in Fig.3. Square transducer arrangement was used. The upper row of Fig. 5 shows
typical waveforms excited by pencil-lead breaks at position #2 (150, 225) and monitored by four
resonant transducers (ch.1 to 4). Their 65 kHz wavelet coefficients (or A0 -components in this case) are
shown in the bottom row. From the wavelet coefficients vs. time curves, we determined the arrival

Fig.5 Examples of Lamb wave (top) and their 65 kHz A0 -components (bottom) produced by pencillead break at #2 position and monitored by four AE transducers on (0o 4 /45o 4 /90o4 /-45o4 )s CFRP plate.
times of the detected waveforms, which were taken at the points in time when the wavelet coefficientreaches 20% of the peak value. This compensates for the attenuation of wave amplitude. We also
compared them with the positions of the peak value, or peak-arrival times, but the 20%-threshold
approach was slightly better in final location accuracy. Although the peak position coincides better withthe arrival of the particular frequency component judging from the shape of the waveform, broadness ofthe peaks probably contributes to the observed discrepancy. The determination of the arrival time was
performed as a part of computer program along with the wavelet transform. Owing to a good S/N ratio
and sharply rising wavelet profiles, arrival times can be accurately identified. Using the arrival timedata, we obtained next the sequence of the arrival times. This identifies the source location within one of
the four quadrants for the square four-transducer arrangement used. The final step of this source location
method is to use measured arrival time differences and orientation dependence of A0 -Lamb waves, andto estimate a source location. The differences of the measured arrival times are matched to computed
arrival time differences, by using one of iterative processes. Results are shown in Fig. 6 for pencil-leadbreak (left) and steel-ball drop sources (right), which indicate an original position by a square andestimated source position by an X. The estimated source locations agree well with the given locations.
The location results are given in Table 1. The maximum error of 6.8 mm and average error of 2.4 mm
were obtained for pencil-lead break sources on this quasi-isotropic CFRP plate. For steel-ball drops, themaximum error of 6.3 mm and average error of 3.0 mm were obtained. Computation time was less than
one second. The importance of utilizing anisotropic Lamb wave velocities can be demonstrated by acomparison with source location results with an isotropic velocity. Table 2 summarizes the results,
which indicate the maximum error of 26.4 mm and average error of 17 mm. Even though 65 kHz A0 Lamb velocities only varies from 1.17 mm/µs to 1.33 mm/µs (or 6%), this velocity variation decreases
the location accuracy to an unacceptable level.

56

Fig.6 Comparison of given source location (oo ) and estimated location (X) of pencil-lead break (theleft) and steel-ball drop (the right) on (0o4 /45o 4 /90o 4 /-45o4 )s CFRP plate.
Table 1 Source location of pencil-lead break (the left) and steel-ball drop (the right)
on (0o 4 /45o4 /90o4 /-45o 4 )s CFRP plate.

b. Source Location on UD-CFRP Plate
The source location analysis of pencil-lead breaks and steel-ball drops is performed on a UD-CFRP
plate that has stronger anisotropy. Specimen size and transducers layout are the same as those used forthe quasi-isotropic CFRP plate. Procedures are identical to those used earlier and 65 kHz A0 components were utilized. The source location results for pencil-lead breaks and steel-ball drops are
summarized in Table 3.

57

Table 2 Source location on (0o4 /45o 4 /90o 4 /-45o4 )s CFRP plate using 65 kHz A0 -component assuming
the symmetric velocity distribution to the Y-axis.

Table 3 Source location of pencil-lead break and steel-ball drop on UD-CFRP plate.

The source location for pencil-lead break was determined within 5.6 mm maximum error with theaverage error of 3.1 mm. Accuracy of source locations for steel-ball drops is also good, with the average
error of 4.0 mm and 8.4 mm maximum error. The results demonstrate that, in spite of a higher velocity
anisotropy of UD-CFRP, the source location can be accurately estimated by the present method.
c. Source Location on Cross-ply CFRP Plates
The source location analysis of pencil-lead breaks is performed on cross-ply-CFRP plates of threedifferent thickness (2.0, 3.0 and 4.0 mm). Specimen size and transducers layout is the same as thoseused for the quasi-isotropic CFRP plate. Procedures are identical to those used earlier and 65 kHz A0 components were again utilized. The source location results for pencil-lead breaks are summarized inTable 4. The source location for pencil-lead break was determined within 6.5 to 11.8 mm maximum

58

Table 4 Source location of pencil-lead break on Cross-ply-CFRP plates.

error with the average error of 4.8 to 7.0 mm for these plates. The results demonstrate that, in spite of alesser velocity anisotropy of cross-ply CFRP plates, the source location accuracy was comparable to theother plates. This implies that the accuracy limitation is due to the lack of homogeneity in the plates
used rather than the source location algorithm.
CONCLUSION
We developed a new source location method applicable to anisotropic plates, and demonstrated its utility
for the source location of quasi-isotropic, unidirectional and cross-ply CFRP plates with velocity
anisotropy and dispersion. The orientation dependence of A0 -Lamb velocities for CFRPs plates wasmeasured at selected frequencies by a wavelet transform of AE signals. This velocity data wasincorporated for the source location analysis. The computer algorithm developed provides performing
wavelet transform, determining the arrival time of a selected frequency A0 -Lamb component andcomputing the source coordinates. The source was first estimated to one of four quadrants within 4transducers by using the arrival times, and then accurately determined by sequential iteration. Here, themeasured arrival-time differences were matched with those computed by moving a virtual source
position in pre-set steps. The source locations of pencil-lead breaks in 300 x 300 mm square enclosed byfour transducers on both the quasi-isotropic and unidirectional CFRP plates were determined with the
maximum (average) error of 6.8 (3.1) mm. Steel-ball drop sources were located with the average error of
3.0 mm for the quasi-isotropic plate and 4.0 mm for the UD-CFRP plate. Speed of the source location
was less than one second in all the cases.
ACKNOWLEDGEMENT
M. Takemoto is grateful for the financial support received from the Ministry of Education (10875130).
A part of this research was supported by the fellowship to Y. Mizutani from the Japan Soc. for
Promotion of Science.
REFERENCES CITED[1] K.S. Downs and M.A. Hamstad, J. Acoustic Emission, 14(3-4), (1996), S61-S73.
[2] W.H. Prosser, J. Acoustic Emission, 14(3-4), (1996), S1-S11.
[3] T. Kinjo, H. Suzuki, M. Takemoto, JSME Int. Series A, 41-3 (1998), pp. 359-365.
[4] M.R. Gorman and W.H. Prosser, J.Acoust Emission, 9, (1991) 283-288.
[5] S.M. Ziola and M, R. Gorman, J. Acoustic Soc. Am., 90(5), (1991) 2551-2556.
[6] K.Y. Hjang, W.H. Lee and D. Kim, Progress in AE, (1999) IV-58-65.
[7] V. Venkatesh and J.R. Houghton, J. Acoustic Emission, 14(2)(1996)61-68.
[8] Y. Promboon and T.J. Fowler, Source Location on Fiber Reinfroced Composites, paper delivered at59

6-th Int. Sym. on AE from Composite Materials at Texas, (1998, June).
[9] O.Y. Kwon and Y.C. Joo, Progress in Acoustic Emission, (1998) IV9-17.
[10] E.L. Adler, C.K. Jen, G.W. Farnell and J. Sloboszewicz, Ultrasonic Int. '85, (1985) 733.

60

CHARACTERIZATION OF THE DAMAGE AND FRACTURE MECHANISMS
IN Ti3SiC2 USING ACOUSTIC EMISSION
P. FINKEL, R.K.MILLER, M.A.FRIESEL, R.D.FINLAYSON, P.T.COLE,
M. W. BARSOUM* and T. EL-RAGHY*
Physical Acoustics Corporation, Princeton Junction, NJ, USA* Drexel University, Philadelphia, PA, USA
ABSTRACTIn this paper we report on the study of the acoustic emission (AE) produced during room temperature
deformation of polycrystalline, fully dense, bulk samples of the ternary carbide, Ti3 SiC 2 . This material isa damage tolerant ceramic that deforms plastically by a combination of delamination of individual
grains, shear, and kink band formation. To further evaluate the deformation and fracture mechanisms,
AE was monitored during compression tests. The AE activity is a strong function of strain rate and grain
size. Unlike regular metals, this ceramic does not exhibit a peak of AE activity associated with the pointof non-linearity in the stress-strain curve. An attempt has been made to quantify the AE and correlate itto the various damage mechanisms occurring during room temperature compression. AE can be used tocontinuously monitor the evolution of the damage, and its onset.
INTRODUCTION
Acoustic Emission (AE) is a well-proven technology for studying the mechanisms accompanying
deformation and fracture. The AE produced during deformation of solids is a function of their elastic
moduli, temperature, and microstructural features such as grain size, strength and fracture toughness.
The various damage processes (dislocation motion, crack initiation and propagation, plastic deformation,etc.) radiate elastic waves that can be detected by piezoelectric transducers. Typically, each of thesemechanisms is accompanied by a different AE signature, and in principle it is possible to differentiate
between them. Furthermore, AE activity can be used as an indication of the onset of failure. The
determination of failure modes by various AE signals and their correlation with mechanical properties isa base for developing evaluation criteria for damage characterization of ceramic materials.
To date, two limitations have restricted the use of AE to study the fracture of brittle solids in general,and ceramics in particular. The first is the high AE event rates that sometimes exceed 1000/sec [1]. A
solution to this problem is to use the MISTRAS-2000 system (Physical Acoustics Corp.) that is capableof performing waveform and signal recording with a maximum event rate exceeding 1200/sec. The
second limitation, which is more difficult to overcome, is related to properties of ceramics being brittle
and damage intolerant by their nature. Recently, however, a new class of materials was discovered, thatare extremely damage tolerant [2-5]. The power of combining AE with this new class of materials isdemonstrated in this work.
For the current study we selected the ternary carbide, Ti3 SiC 2 recently fabricated and characterized infully dense, bulk, single phase samples [2]. This material is rather stiff (Young’s moduli ≈300 GPa[2,6,7]) but relatively soft (Vickers hardness, ≈4 GPa). At the same time, it is readily machinable byregular high speed tool steels with no lubrication or cooling required. In addition to the aforementioned
characteristics, Ti3 SiC 2 is also oxidation [8] and creep resistant [9]. Most relevant to this work is the factthat Ti3 SiC 2 deforms by a combination of regular glide kink and shear band formation [10,11]. The
dislocations are overwhelmingly confined to the basal planes [12], are mobile, and multiply at room
temperature [11].
61We have also shown that highly oriented macro-grained samples are ductile at room temperature anddeform by forming macroscopic shear bands that, for the most part, extended across the samples tested.
Because the deformation is constrained to the shear bands, the latter are areas of considerable upheaval,
with delaminations, crack bridges and other evidence of intense deformation clearly obvious in postdeformation micrographs. The details and sequence of events that lead to the formation of the shear bands
has not been studied in detail. The results presented in this paper, do shed some light on the problem. To
date polycrystalline samples loaded in compression at room temperature failed in a brittle manner. In our
most recent work, we demonstrated that the mechanical response of Ti3 SiC 2 is a strong function of strain
rate [13]. The results shown in this paper clearly show that when loaded slowly, even randomly oriented
polycrystalline samples will exhibit some plasticity at room temperature. Finally, it is important to notethat since Ti3 SiC 2 does not possess 5 independent slip systems, it does not possess a yield point. The
transition is thus one from elastic to in-elastic deformation, which is often not easy to determine fromload deflection curves. The stress at which the transition occurs is henceforth referred to as the inelastic
deformation stress or IDS.
This paper describes the quantitative results from application of the AE for determination of the role themicrostructure and test parameters play during the deformation and fracture of Ti3 SiC 2 . The goal of thiswork is to carry out a detailed study of the acoustic emission accompanying initiation and subsequentdamage evolution in Ti3 SiC 2 samples during compression. The analysis of AE data allows us to evaluate
the effect of the grain size and strain rate on AE activity and also gain further insight into thedeformation mechanisms of this material.
EXPERIMENTAL PROCEDURE
The material for fabricating Ti3 SiC 2 samples was prepared using reactive hot isostatic pressing (HIPing)[14]. In this work, three microstructures were tested: a fine grained (FG) with average grain size of 5µm; a coarse-grained (CG) with average grain size of 100 µm, and a duplex microstructure (DM) which
consisted of very large grains of the order of 500-800 µm plates, about 100 µm thick, embedded in amatrix of 100 µm grains. All tests were performed on cylindrical specimens (7 mm diameter and 15mm
length) machined by electro-discharge machining.
Figure 1 shows the set-up of AE monitoring hardware used in this study. The compression tests were
performed on an Instron-type testing machine at room temperature. The nominal strain rate was varied
from 0.00056 to 0.0056 s-1. In a few samples the strain was measured with a specially designed fiber
optic strain gage that attached to the lateral side of the specimen. The MISTRAS-2000 system (PhysicalAcoustics Corporation) used in this work is capable of performing waveform and signal recording with apeak event rate exceeding 1200/sec. The AE signal was recorded with a broadband PAC S9208 andresonant PAC R15 transducers mounted on the free side of the specimen under test.
RESULTS AND DISCUSSIONS
Stress-strain curves of two CG samples tested at 0.00056 s-1 are shown in Fig. 2. In both cases, adeviation from nonlinearity is observed at ~ 200 MPa, which is followed by what can be best described
as a region of distinct hardening, or in-elastic deformation, until failure at ~ 500 MPa. One sample failed
at a strain of 1.2 %, the other at ~ 2 %. In these tests the strain was directly measured using the opticalstrain gauge. The reproducibility between the two runs is excellent. The straight line represents thestiffness calculated based on the measured modulus of 333 GPa [6]. Also plotted in Fig. 2 (lower curve)
is the cumulative AE event rate for Sample # 1. Below 200 MPa, there is very little AE activity. Beyond
the IDS, the AE activity (RMS, hit rates and energy) increases monotonically until fracture.
62

Specimen

Preamplifier
40 dB

PC with continuous AE
Monitoring

AE sensor

Stress, MPa

Fig. 1. Set-up of the AE monitoring hardware used in this study.

800
750
AE event rate
700
for sample #2
650
600
calculated modulus
550
E = 330 GPA
500
450
400
350
300
250
200
150
IDS
100
50
0
0.000
0.005
0.010

200
180

sample #2

160
140
120
100

sample #1

80
60
40
20
0

0.015

0.020

Strain , εFig. 2. Stress – strain curve and cumulative AE events vs strain for CG sample.

In order to evaluate the effect of experimental variables in this work, the AE was measured as a functionof strain rate. Figure 3 shows the AE RMS voltage Vrms plotted as a function of strain rate ( dε/dt )(cross-head speed) at 95% of the stress to failure. A least squares fit of the data (dashed line) yields aslope of ~ 0.6. The solid line represents (slope 0.5) the normal strain rate dependence Vrms ~ (dε/dt)0.5 .
This normal strain rate relationship implies that the AE source mechanism, associated with moving
dislocations, remains unchanged over the range of strain rates measured [15]. The origin of this behavior
in Ti3 SiC 2 is not clear yet and more work is required.
The results shown in Fig.4 demonstrate the effect of grain size on AE activity obtained during themonotonic compression test using a strain rate of 0.00056 s-1. This figure displays the load (top set of
curves), cumulative RMS (middle set of curves) and cumulative hits (bottom set of curves) versus time
for the FG (a), CG (b) and DM (c), samples. As can be seen, cumulative hits and RMS increase with agrain size increasing. Note the change of scale of the y-axis when going from the FG to the CG and DM
63

10

-3

10

-2

RMS AE voltage,µV

95% of stress to failure

1010Fig. 3. RMS voltage vs. strain rate for
CG sample.

slope 0.5

least squres fit
slope 0.6

1

1
10

-3

10

-2

Strain Rate(crosshead displacement, inches/min)Stress

RMS

Hits

a.

b.

c.

Figure 4. Time history AE results for three various grain size sample: (a), CG (b) and DM (c).

samples. These results clearly show that the AE activity is a strong function of microstructure. This is ina good agreement with the theoretical approach [16]. Furthermore, as the point at which the cumulative
AE event rate increases dramatically (Fig. 4b, lowest curve) corresponds to a stress of ≈ 250 MPa, it iscomparable to the IDS determined from Fig.2 (≈200MPa), and the former must be associated with theinitiation of damage. This can be used to quickly and accurately measure the IDS in these damage
tolerant ternary compounds; a qua ntity that has not been easy to determine.
It is important to contrast the behavior of Ti3 SiC 2 with that of ductile metals and other more brittle
ceramics. In metals, including Ti, the AE activity and RMS are at a maximum at the yield point. Typicalceramics behave more like the FG response shown in Fig. 4a. The AE activity of DM samples starts at64

early stages of deformation is almost an order of magnitude higher than that for FG samples. The AE
behavior of the CG and DM samples is distinctly different (see Fig.4b and Fig.4c). In light of what iscurrently known about the deformation modes of Ti3 SiC 2 , i.e. grain delaminations, kink and shear bandformation in individual grains this is not too surprising.
It is reasonable to assume that the AE signature of the aforementioned deformation modes is different
than that due to the simple glide of dislocations along basal planes that must precede them. In the CG
and DM samples, it is also reasonable to assume that the first AE events occur at stresses that are much
lower than those required for the formation of the first macroscopic shear bands. This in turn suggeststhat there may be several AE signatures. The analysis of individual waveforms and their spectra support
this notion. Figure 5a and 5b show the AE hits amplitude distributions, waveforms and their spectra for
CG sample at two levels of the applied stress: (a) initial stage I, (≈ 300 MPa, just after IDS); (b) stage II(at ≈ 450 MPa, near failure stress). The AE signals measured at stage I were typically burst type with arelatively low energy per event and with dominant low frequency component centered around 150 kHz(Fig. 5a, lower curve). This AE is most likely due to dislocation motion, array formation and kink bandformation and possibly initial delaminations. At the higher strains (Fig. 5b, lowest plot) the spectrum isdistinctly different; the events are high energy and contain higher frequenc ies. These are AE signals of
the second type. It is therefore reasonable to associate this AE signal with the formation of themacroscopic shear bands. The amplitude distribution plots (Figs. 5a,b, top plots) also indicate
differences. The amplitudes in both stages have a peak between 40-50 dB. However, a second peak can
be observed in loading stage II (Fig. 5b, top curve, marked with the arrow) between 60 and 70 dB. This
indicates the presence of at least one other mechanism after the IDS. In order to distinguish AE signals
from the different damage mechanisms, it also possible to apply pattern recognition analysis.
The Physical Acoustics Corporation NOESIS software that was used for this purpose illustrates (Fig. 6)
the evolution of the various AE features during the loading of a DM sample. The onset of the shear band
can be clearly identified, as the signals forming cluster #1 have relatively low mean frequenc ies.
One important result of the performed study is that deformation induced AE is shown to be very
effective and useful for the evaluation of fracture mechanisms in Ti3 SiC 2 . Also, we have proved that AE
frequency spectrum data and pattern recognition analysis could be used to characterize various AE
sources associated to damage evolution of this material. The AE results demonstrate the unique damage
tolerance characteristics of Ti3 SiC 2 ; the amount of damage these solids can accumulate before failing isquite remarkable even at room temperature. Finally, our AE results clearly indicate that both the design
stresses, which probably would have to be less than the IDS, and the amount of damage the material canwithstand before failure, are strong functions of grain size and microstructure.
CONCLUSIONSIn this study acoustic emission was used to evaluate the damage mechanisms occurring during room
temperature compression of Ti3 SiC 2 . The AE generated during compression tests of the Ti3 SiC 2 samples
has been measured and characterized. Three types of microstructures were investigated, and themeasured AE was found to be dependent on the grain size and strain rate. Analysis of the AE signals
suggests that more than one mechanism was responsible for the signal. Based on this work, AE can beeffectively used to evaluate the fracture mechanisms and damage evolution in this material. Further
studies on the quantitative correlation between AE and material micro-mechanics are under way and will
be published soon.

65

REFERENCES
1. A.Warner, et al, Monitoring of Deformation Induced Microcracking in Polycrystalline NiAl,
Mat.Res.Soc.SymposProc.364, p.543, 1995.
2. M. W. Barsoum and T. El-Raghy, “Synthesis and Characterization of a Remarkable Ceramic:
Ti3 SiC 2 , J. Amer. Cer. Soc. 79, [7] 1953-56 (1996).
3. T. El-Raghy, A. Zavaliangos, M. W. Barsoum and S. Kalidinidi, Damage Mechanisms Around
Hardness Indentations in Ti3 SiC 2 , J. Amer. Cer. Soc., 80, pp. 513-516, (1997).
4. I. M. Low, S. K. Lee, B. Lawn and M. W. Barsoum, "Contact Damage Accumulation in Ti3 SiC 2 ", J.
Amer. Cer. Soc., 81, 225-28 (1998).
5. T. El-Raghy, M. W. Barsoum, A. Zavaliangos and S. Kalidindi, "Processing and Mechanical
Properties of Ti3 SiC 2 , Part II: Mechanical Properties”, J. Amer. Cer. Soc., 82, 2855-2859 (1999).
6. P. Finkel, M. W. Barsoum and T. El-Raghy, "Low Temperature Dependence of Elastic Properties of
Ti3 SiC 2 ", J. Appl. Physics, 85, 7123-7126 (1999).
7. R. Pampuch, J. Lis, J. Piekarczyk and L. Stobierski, "Ti3 SiC 2 -Based Materials Produced by SelfPropagating High Temperature Synthesis and Ceramic Processing", J. Mater. Synthesis &Processing, 1, 93 (1993).
8. M. W. Barsoum, T. El-Raghy and L. Ogbuji, “Oxidation of Ti3 SiC 2 in Air”, J. Electrochem.
Soc.,144, 2508-2516 (1997).
9. M. Radovic, M. W. Barsoum, T. El-Raghy, J. Seidensticker and S. Wiederhorn, “Tensile Creep
Properties of Ti3 SiC 2 in the 1000-1300 °C Temperature Range”, Submitted for Publication.
10. M. W. Barsoum and T. El-Raghy, Room Temperature Ductile Carbides", Met. Mat. Trans., 30A,
363-369 (1999).
11. M. W. Barsoum, L. Farber and T. El-Raghy, “Dislocations, Kink Banks and Room Temperature
Plasticity of Ti3 SiC 2 ”, Met. Mat. Trans., 30A, 1727-1738 (1999).
12. L. Farber, M. W. Barsoum, A. Zavaliangos, T. El-Raghy and I. Levin, "Dislocations and Stacking
Faults in Ti3 SiC 2 ", J. Amer. Cer. Soc., 81, 1677-81 (1998).
13. M. Radovic, M. W. Barsoum, T. El-Raghy, J. Seidensticker and S. Wiederhorn, “Tensile Properties
of Ti3 SiC 2 in the 25-1300 °C Temperature Range”, Acta Mater. in press.
14. T. El-Raghy and M. W. Barsoum, "Processing and Mechanical Properties of Ti3 SiC 2 : Part I:
Reaction Path and Microstructure Evolution", J. Amer. Cer. Soc., 82, 2849-54 (1999).
15. S. Y. Hsu and K. Ono, Proceedings of the Fifth International AE Symposium, pp.283, JapaneseSociety for Nondestructive Inspection, Tokyo, Japan.(1980).
16. H. G. N.Wadeley, C.B. Scruby, J. H. Speake, International Metals Review, 25, No.2, p.41. (1980).

66

a.

b.

Fig. 5. Two types of typical AE amplitude distributions, waveforms, and power spectra in CG sample corresponding toload stage I (a) and stage II (b) ( see text).

Fig. 6. AE time history for DM sample analyzed with pattern recognition technique.
67

EVALUATION OF MARTENSITIC TRANSFORMATION DYNAMICS OF Cu-Al-Ni SHAPE
MEMORY ALLOY SINGLE CRYSTAL BY ACOUSTIC EMISSION METHOD
Kenichi YOSHIDA, Shinichi KIHARA* and K. SAKAMAKI
Department of Mechanical Engineering, Tokushima University, Tokushima, Japan*Yamaguchi NEC Corporation, Yamaguchi, Japan
Abstract
Cu-Al-Ni shape memory alloy exhibits a super-elasticity, which shows perfect recovery of plastic
deformation. The super-elasticity used in this study results from a perfect reversible martensitic
transformation. Acoustic emission (AE) behavior during the stress-induced martensitic transformation
has been investigated using two types of specimens of Cu-Al-Ni shape-memory alloy single crystal, eachof which has different stress concentration factors. AE total event counts generated during martensitic
transformation did not depend on the specimen volume. AE event rate and the amplitude distribution
were associated with the nucleation and growth of martensite plates during loading and theirdisappearance and shrinkage during unloading. Sequential change of AE waveforms during deformation
was different in the two specimens because of differing martensite plate shapes. The martensitic
transformation rate obtained by AE waveform analysis increased with increase of fatigue damage.
Introduction
Martensitic transformation is known as a transient shear process in materials. The dynamic property ofthe transformation must give an important information on the nature of the transformation [1]. In
general, the martensitic transformation proceeds both by nucleation and growth of martensite plate [2].
The dynamic behavior of martensitic transformation phenomena has been measured by transient
electrical resistivity change in Fe-Ni alloy [3], magnetic permeability change in Fe-Ni alloy [4] and AE
method in 304 stainless steel [5,6]. These results have two weak points to explain the dynamic behavior
of individual martensite plates. One is that the studies included the constraints due to grain boundaries
because materials used were polycrystalline. Another is that these could not be distinguish between
nucleation and growth of martensite plates because of difficulty of in situ measurement.
In this paper, the dynamic properties of the stress-induced martensitic transformation in two types of
Cu-Al-Ni shape-memory alloy single crystal were investigated using AE method. The martensitic
transformation used in this study was the β 1’(DO 3 structure)<=> β 1’(18R structure) transformation,
which can be observed by an optical microscope during tensile deformation at room temperature.
Therefore, the nucleation and growth behavior of martensite plates was examined in detail.
Experimental procedures
Material used was a Cu-14.1mass%Al-4.1mass%Ni alloy (M s=250K, Af=280K) for stress-induced
martensitic transformation. It was melted in argon in a high-frequency induction furnace and cast into aplate-shaped copper mold. It was next put into a graphite mold. An oriented single crystal of the CuAl-Ni alloy was grown using a seed crystal by the Bridgman method at a speed of 32 mm/h. They were
solution-treated at 1273K for 1 hr. and then quenched into water at room temperature in order to keep
68the β 1 matrix phase. Two kinds of tensile specimens (shown in Fig. 1) were prepared by an electrondischarge machine, polished mechanically and then electro-polished in a solution of phosphoric acidsupersaturated with chromium trioxide. These were nearly smooth (specimen No. 1) and double-side
notched (specimen No. 2) specimens. Stress concentration factors in these specimens were 1.01 and
2.65, respectively. For volume dependency on AE activity, rectangular samples (size: 12 mm x 4 mm x
0.9~1.8 mm were also used. The orientation of the single crystal specimen was determined by the
back-reflection Laue method. The specimen had (001) surface and [100] tensile direction, respectively.

Fig. 1 Specimen shapes and AE sensor locations.
Tensile test was carried out with an Instron-type testing machine at a strain rate of 5.6 x 10-5 s -1. Fatigue
test by plate bending was also carried out with a Schenk-type testing machine at a cyclic rate of 6 Hz.
Surface appearance during tensile test was observed and photographed microscopically by an optical
microscope.
Acoustic emission measurements were performed using 2-channel AE monitoring system (PAC:
MISTRAS-2001). The AE signals were detected by two AE transducers (M5W) with wide frequency
band. These were directly attached on the grip part of a specimen with a quick-acting glue. The distance
between the two transducers was 50 or 20 mm. Detected AE signals were amplified by a constant gainof 60dB through a band-pass filter of 0.1 to 1.2 MHz. Acoustic emission parameters measured consistedof the AE events, the peak amplitude and the waveforms. Threshold level was 50dB, whichcorresponded to 316 µV at the preamplifier input.

69

Results and Discussion(1) Effect of specimen volume on AE activity
All the specimens exhibited super-elasticity and completely recovered the matrix phase upon unloading
because of the lower Af point of the material. When a given plastic strain is exclusively due to themartensitic transformation, an amount of transformation is considered as proportional to the given
plastic strain energy. So effect of specimen volume on AE activity has been examined. All the specimens
were given the same plastic strain energy per unit volume and an amount of transformation changed by
the specimen volume. Results obtained during loading and unloading at the constant plastic strain of 2%
are shown in Fig. 2. Total AE event counts decreased with an increase in the specimen volume. AE
activity does not seem to depend on the amount of the martensitic transformation in material, but
strongly depends on the dynamic behavior of the transformation. Especially the tendency becomesstronger under the reverse transformation. This indicates that it is possible to track nucleation andgrowth of transformation by AE method.

Fig. 2 Effect of specimen volume on total AE event counts (constant plastic strain of 2 %).
(2) Difference of AE activity under different stress conditions
Relations between load-elongation curve, AE event rate and amplitude in the smooth (No. 1) and notched(No. 2) specimens during loading are shown in Fig. 3 and 4. Typical sequential surface appearances at
the four elongation values by optical microscope are also shown. Less AE events were detected in thesmooth specimen than those in the notched specimen, but higher AE amplitude was observed in thesmooth specimen. According to the surface appearances in Fig. 3 and 4, the AE activity is related to the
nucleation and growth of a few, large martensite plates in the smooth specimen and to the nucleation ofmany small martensite plates in the notched specimen.
70

(3) Nucleation and growth behavior of the martensitic transformation
Acoustic emission behavior in the smooth specimen was different from that in the notched specimen in
the early stage of the transformation as mentioned above. In the case of the smooth specimen, martensite
plates extended to a single direction nucleated at first, and these grew in length and width directions andexpanded through the thickness direction during yielding. Acoustic emission, which occurs sporadicallyduring nucleation in the early stage of the transformation, had small amplitude, but AE changed tomedium or high amplitude with the growth in length and width directions and still higher with expandedmartensite plates. In the case of the notched specimen, many small amplitude acoustic emissions weredetected when numerous small-sized martensite plates nucleated near the notch roots into two or three
directions. There was only a small number of medium and high amplitude acoustic emissions because
many martensite plates nucleated near the notch roots did not grow in the width direction because ofstress concentration. Many higher amplitude acoustic emissions occurred with growth to the length andthickness directions.

Fig. 3 Relations between load-elongation curve, AE event rate and amplitude during loading; in specimen
No. 1 (smooth). Sequential surface appearance at different elongation by optical microscope.

Fig. 4 Relations between load-elongation curve, AE event rate and amplitude during loading in specimen
No. 2 (notched). Sequential surface appearance at different elongation by optical microscope.

71

Remarkable characteristics of acoustic emission waveforms in the smooth and notched specimens were
found. In order to understand that, we divided into three stages the region from the loading start to yield
point: Stage 1: starting point of loading to the nucleation region of martensite plates, Stage 2: mainly thegrowth region of martensite plates, Stage 3: near yielding. FFT analysis of detected AE waveforms has
been performed in each transformation stage. A typical frequency spectrum of the waveform during
tensile deformation is shown in Fig. 5. First and secondary peaks were selected as the characteristic
parameters. Changes of these parameters during tensile deformation in the smooth and notched
specimens are shown in Fig. 6 and 7, respectively.

Fig. 5 A typical frequency spectrum of detected AE waveform.

Fig. 6 Changes of first and second frequency peaks in waveforms detected in each stage during tensile
deformation in Specimen No. 1 (smooth)72

Fig. 7 Changes of first and second frequency peaks in waveforms detected in each stage during tensile
deformation in Specimen No. 2 (notched).
First frequency peak is distributed near 200 kHz. This frequency seems to be characteristic of themartensitic transformation. The second peak frequency shifted to higher frequency in the latter half ofStage 2 in the smooth specimen but steeply to higher frequency in the latter half of Stage l in the notched
specimen. The shift of the second frequency to higher frequency seems to depend on a degree of
constraint against growth to length and thickness directions of the martensite plates. The shift suggeststhat the rise time in the martensitic transformation becomes short as discussed next.
(4) Rise time change of the martensitic transformation with fatigue damage
The rise-time analysis of the martensitic transformation has already been conducted using AE waveform
analysis in the previous papers [7,8]. Takashima et al. tried to approximately conduct the source
characterization using the dynamic Green's function in an infinite isotropic medium, assuming theGaussian error function as the time shape function characterizing the dynamic property of the acoustic
emission source. As a result, the mean rise time of the source was calculated by the gradient of thespectrum as the log-log expression in the frequency range of interest. Results obtained were apparently
valid because the rise time of pencil-lead breaking as reference in this method was consistent with that inother method [9]. We applied the method to the martensitic transformation with fatigue damage. Changeof the mean rise time due to the fatigue cycles in the smooth specimen during loading and unloading areshown in Fig. 8. Both of the mean rise times decrease with an increase in the fatigue cycles. Even if
fatigue is given to 30000 cycles, we could not find out a macrocrack on the specimen surface. In thiscase, we could evaluate the growth rate of the martensite plates, which changed from 310 m/s to 500 m/s.
The martensitic transformation is indicated to be able to occur more easily with increase of fatigue
damage. Therefore, it is clearly possible to non-destructively evaluate the fatigue damage in the stage
before crack formation in this type of material using AE waveform analysis.
73

Fig. 8 Effect of mean source rise time on fatigue cycles in loading and unloading process. Mean source
rise time was obtained by the acoustic emission wave analysis of Takashima et al. Decrease in meansource rise time corresponded to increase in growth rate of martensite plate of 310 to 500 m/s.
Conclusions
Acoustic emission behavior during stress-induced martensitic transformation of Cu-Al-Ni shape-memory
alloy single crystals, which exhibit super-elasticity, has been investigated using the smooth and notched
specimens. Results obtained are as follows:(1) There is no effect of specimen volume on AE activity.
(2) Sequential AE activity consisting of AE events, amplitude and waveforms in the smooth specimen isdifferent from that in the notched specimen because of different stress concentration.
(3) Acoustic emission method is effective to evaluate such dynamic processes as nucleation and growthof martensitic transformation.
(4) It is possible to evaluate this type of material with fatigue damage using the acoustic emission
waveform analysis.
References
1) T. Saburi, C. M. Wayman, K. Takata and S. Nenno: Acta Metall., 28(1980), 15-32.
2) K. Otsuka, C. M. Wayman, H. Sakamoto and K. Shimizu: Acta Metall., 24(1976), 207-226.
3) R. F. Bunshah and R. F. Mehl: Trans. AIME, 197(1953), 1251-1256.
4) T. Kakeshita, K. Shimizu, T. Maki and I. Tamura: Scripta Metall., 14(1980), 1067-1070.
5) K. Takashima, Y. Higo and S. Nunomura: Phil. Mag., 49A(1984), 231-241.
6) Z. Z. Yu and P. C. Clapp: Metall . Trans., 20A(1989), 1601-1615.
7) K. Takashima, Y. Higo and S. Nunomura: Phil. Mag., 49A(1984), 221-229.
8) K. Yoshida, H. Takagi and K. Sakamaki: J. JSNDI, 46-11(1997), 821-829.
9) T. Ohira and T. Kishi: J. Japan Inst. Metals, 46-5(1982), 515-525.

74

THE IDENTIFICATION OF BASIC FATIGUE PARAMETERS ON
ELECTRORESONANCE PULSATOR WITH HELP OF THE ACOUSTICEMISSION TECHNOLOGY
Pavel MAZAL, Jill PETRAs - Bmo University of Technology, Fac. ofMech. Engineering
Technicka 2, CZ 616 69 Bmo, Czech republic

Abstract
Exemples ofchosen experimental results were introduced in the paper, that had been gained during theapplication of the technology of acoustic emission on the observing offatigue failure of carbon steel
and nodular cast iron with the surface treated by low temperature plasma nitriding. Gained parameters
show that this non-destructive testing method is also suitable in the area of fatigue properties
evaluation on the electro-resonance pulsators. Different types ofAE data treatment are presented here.
1. INTRODUCTION
One of the important research areas, which are being solved in the Institute of Design in BUT Bmo is
the problem of influencing ofthe fatigue limit and other fatigue parameters of materials by help of up-todate surface treatment. Low-temperature plasma nitriding, oxinitriding, laser treatment of the surface
etc. are examples of these treatments [1]. Electro-resonance testing machine RUMUL Cracktronic
observes fatigue qualities of materials treated in this way. Technical solution of this pulsator makes
possible only very restricted analyses offatigue processes that really happen in loaded material.
Due to the fact that we have been dealing with the application of the acoustic emission technique (AET)in the area of contact loaded materials, we try to use this modem technology of non-destructive testingof damaged materials and constructions with such samples, that were loaded with classical fatigue on
Rumul machine. First experimental results that proved the possibility to use AET for fatigue process
observing on above mentioned pulsator have already been presented at the conference AE'99 [2]. First
experiences have been deepened and the basic database of necessary AE parameters has been graduallycreated.
2. MATERIAL AND EXPERIMENTAL EQUIPMENT
In this paper, chosen results gained by cyclic loading of carbon steel CSN 41 2060 (= C55) and nodular
cast iron CSN 42 2305 (= GJS-500-7), the surfaces of which have been treated by low temperatureplasma nitriding (430°/26 hours) are introduced. By this heat treatment, the surface of material issaturated with nitrogen in gas atmosphere. The result ofthis quite complicated process is the appearanceof very thin hard layer on the surface (so-called "white layer") and diffusion layers of various thickness(usually 0,2 - 0,3 mm - "dark layer"), that are gradually getting into basic structure. The example ofmetalographic cut ofplasma nitrided nodular cast iron with appointed areas can be seen in figure 1.
By a number of materials, plasma nitriding causes important growth of microhardness and especiallydevelopment of favourable pressure stress in surface layer. Fatigue limit of the material usually grows
due to these changes. The area of so-called time fatigue limit is very important for common construction
application. In this area, the moment of the initiation of microcracks and especially the length offollowing period of growth of fatigue crack is an important criterion. These parameters are very difficult
to identifY on electro-resonance loading equipment.

rotating jaw

white

Jayer

fixed jaw

specimen
AE sensor,;"... 0,1 mm .Fig.! Example oftested plasma nitrided
structure (nodular cast iron) (NitaI)

Fig. 2 Schematic illustration offatigue test
arrangement with AE sensing

Three types of AE analysers have been used for analysing of poSSIbilities of distinguishing individual
phases of fatigue process in our case. The process of testing itself was descn1>ed in details in anotherpaper [2]. Schematic illustration offatigu.e tests arrangement with using of acoustic signal sensing can beseen in figure 2. The pulsator works in the area of flat four points bending with the frequency of loading
obout 130 - 150 kHz. Symmetric alternating loading cycle was used for all experiments. AE sensors
with waveguide had to be used due to the limited size of samples.
3. EXAMPLES OF EXPE..'-LL".LaJ~ TAL RESULTS
Samples of typical possibilities of AE signal processing in the area of fatigue loading are shown infollowing pictures.
In picture 3 there are sununation curves, in which counts from the beginning of loading are gradually
added. This curve can be drawn even for individual levels and gradual connecting of higher individual
measured levels of the signal can be seen. Individual basic periods of fatigue process can usually be seenquite wen on the curve.
8,OOE+05-level No.1 (D-4dB)

w<

6,OOE+05-level No.2 (4-&18)

4,OOE+05- a l l levels (Q-4OdB)

0cB
o r:::

:;l

..

~

~

-level NO.3 (~12dB)

0

E U
E

..

steel

2,OOE+05
3.

O,OOE+OO
1

101

201

time [sec.]

301

Fig.3 Records oftota! number ofAE counts during fatigue loading in all levels and levels No. 1,2,3.

45000
IV

30000

c c
::J

...

CI)

I...!!
J!!J!!

PX39

white layer_CI) ....
U

Det.B

Det.A

::J

o 0

u~

~

~

15000

o
3001

1

6001 time [sec.] 9001

Fig. 4 Counts rate record in nodular cast iron in the whole process of fatigue test (AE IDC, levels 1-3)
Further possibility how to show acoustic activities in cyclic loaded material is its expressing by so called
counts rate. In this case the number of counts measured in certain time period is drawn in graphic.
In picture 4 a record can be seen that was gained on nodular cast iron with surface treated by plasma
nitriding. Mentioned sample was loaded in the area of so-called time fatigue limit (durability cca 150000
cycles). On the basis of quite detailed knowledge of behaviours of this material, we suppose that
individual parts ofthe record can be interpreted as follows:
1) Slight softening ofthe whole volume ofthe material appears in the initial period (Fig.5).
2) Thin surface layer, which is very hard and fragile, cracks in case that the amplitude of loading is highenough. 1bis process shows important growth ofthe number ofrecorded AE counts in the record.

9000

9000
PX 39 - begin. stage

PX 39 - Del A

S

e 6000

SIV 6000

I.
J!!

J!!
c

0

3000

8 3000

0

0

C
::J

u

::J

1

101

201

time [sec]

1000

1500

2000
time [sec]Fig.5 Detail of initial period of growth of fatigue crack in nodular cast iron - the area of crack of whitelayer (left) and following difficult spreading in nitrided layer (right) - Det A in Fig.4.

3) This obstacle is followed by comparatively quiet period in which damage in diffusion layer (darklayer) under the white layer is gradually cumulated. Due to the important oversaturation of the structure
by nitrogen, quite high-pressure stresses appear, that restrict effective value of fatigue loading and,lower amplitudes ofloading can stop spreading of fatigue cracks. Higher amplitudes cumulate sufficientenergy cumulated for new re-starting ofthe whole fatigue process - Fig.5 - right.

20000 - - - - - - - - - - - - - - - - - - - - - - - -....
PX.39 - Det. B

s

~

S

10000 + - - - - - - - + - - - - - ; - - . , - - - - - - - - - - : - - - : - r - - ; I I

C

:::s

o

(,)

01-:~~~~~~~~~,:.=:~~=~
8500

7500

10500

9500
time [sec]Fig.6 Counts rate in the last period ofthe fatigue test (nodular cast iron) - Det. B in FigA.
4) In the graphical dependence the releasing of energy and the beginning of spreading can be seen again.
In the period of fatigue cracks spreading step shape of records can be seen by nodular cast iron. We
suppose that this character of AE corresponds with jump damage of nodular cast iron. Fatigue cracks by
its spreading do not grow regularly, but they gradually get over individual areas of basic material
between graphitic globules and they are gradually connected into the only one main crack. Step
character ofAE records could correspond with these jumps.
The activity in higher levels is gradually being increased with the development of fatigue damage. It
might be possible to get certain information for objective evaluation of the state of fatigue damage ofmaterial from so called histograms, in which AE activities in individual levels (mV) of recorded signal
are observed.

500

500
3490-3500 sec.

2500-2509 sec.

u400

u400

-

1:300

-

0

0

Q)

Q)

tI)

tI)

t I)

t I)

1:300

:::s

:::s

(,)

(,)

-200
0

-200
0

E

E
:::s 100:::s 100

tI)

tI)

0

0
1

31

61
levels

91

1

31

61

91

levelsFig.7 Typical amplitude distribution - histograms - from the different periods offatigue loading (levels
1-100 = 0 - 800 mY) (analyser AED FTA4).

The shape of the AE events itself can be considered to be an important soUrce of information about
fatigue process in progress. Our up-to-date knowledge shows that it is possible to watch very importantdifferences in the shape of the event in individual phases of fatigue process. Typical examples ofcharacteristic shapes of this parameter are introduced in figure 8. To be able to make use of thischaracteristic it is necessary to gather much wider sets of information than those that are available up tonow.

24lIII!I!!~

Hanning 2048/512

16
8
O~---Jl!~_:.....:x:...---

200
Hanning 2048/512

600 800
200
400
Hanning 2048/512

600 800
400

200

A

B

Fig.8 The shape ofthe typical events in initial area offatigue loading and in the area of spreading of
main fatigue crack (material A - carbon steel, material B - nodular cast iron) - coordinates: amplitude[mY] vs. time [J..ls], amplitude vs. frekvency [kHz].

4. DISCUSSION ABOUT RESULTS AND CONCLUSION
Introduced results of chosen typical ways of acoustic emission signal treatment show wide possibilities
of application of this modem NDT method for the identification of fatigue process even on the electroresonance fatigue testing machine. Mastering and automation of measuring with use of AE signal mightenable more detailed analysis of cyclic process in quite complicated structural states, e.g. materials withvarious surface treatments.

The technology of acoustic emission makes possible observing of status in the whole loaded volume ofmaterial and it might enable observing ofcracks that appear under the surface. These cracks are difficult
to detect up to now. Precious working out of the process and mastering the elimination of undesirable
influences to AE signal, it might enable to evaluate the intensity of initial softening, eventually hardeningof material and especially, it might enable to identify the moment of the initiation of microcracks and the
beginning of spreading of short cracks.
Processing of above-mentioned procedure will require a lot of experimental work and subsequent
software processing of common trends. This work requires a lot of time and material. That is why it isrecommendable to co-ordinate the work in this field on international level. We would appreciate any
information about other places dealing with similar problem.

Acknowledgements:
Work in this field have been made possible thanks to the support o/Grant Agency CR - Grant project
nr. JOl/96/K264.
References et literature
1) MAZAL,P.- STUCHLIK, J.- DLOUHY',!.: Fatigue properties of structural materials with plasma nitrided andlasered surfaces. In:ll th Congres ofIFHT, Florence, 1998, Vol.3, p.91-100.
2) MAZAL,P.- RICHTER,J.: Application of AET on fatigue testing machine RumuI. In: Acoustic Emission'99,
Brno, 1999, p.157-164.
3) HUANG,M. et al: Using Acoustic Emission in Fatigue and Fracture Materials Research. JOM, VoI.50, No.l1,
November 1998, web page.
4) WEVERS,M. et al: The fatigue failure of steel wires monitored with acoustic emission. In: 23 rd EC on AE
testing, Vienna, p.114-119, TUV Austria, 1998.
5) LEE,C.S. et al: AE measurement of fatigue crack closure. Scripta Metall. et Mater., VoI.32, No5, pp.701-706,
1995.
6) SIEDLACZEK,J. et aI.: AE of the 45HNMFA structural steelduring Low Cycle fatigue. Journal of AE,
Vo1.10, No.3/4, p.l-11, 1992.

THE PHASE OF CONTACT DAMAGE AND ITS DESCRIPTION BY HELP OF
ACOUSTIC EMISSION
Jirí DVORÁCEK, Jirí PETRÁŠ, and Luboš PAZDERATechnical University of Brno, Brno, Czech Republic

Abstract
Characteristics of acoustic emission (AE) signal from individual phases of contact damage are
explained in the contribution. The shape of AE signal by running-in and by permanent damage of thesample surface are described. Their comparison of emission duration, time of running-in, number ofovershooting, maximum value of amlitude and energy of emission event is done on a sample of typicalchanges of AE signals in time. A sample of amplitude resolution of AE signal for individual phases of
the test is shown. AE signal was sensed by testing of material resistance against contact fatigue on
testing stations. The samples that underwent contact loading had been made of standard bearing steel.
The contact was lubricated by plastic lubrication. AE signal was sensed and processed by 16-channel
acoustic emission analyser AE FTA16. For the signal analyses, we selected not only the frequency
analysis with utilisation of the Fourier transform, but the time-frequency transformation, the Wigner–
Ville transform.
Experimental equipment
The experiments were done on the experimental testing stations Axmat. It is located in the laboratoriesof the Institute of Design of the VUT FSI in Brno. This equipment is designed for tests of bearing
material resistance against the contact fatigue. Test principle consists in loading of a flat sample throughlayer of rotating balls by force, which develops contact stress of 5 GPa. The contact is lubricated byplastic lubricant. The test runs until when a permanent damage, so called pitting, occurs on the sample.
This time is recorded and statistically evaluated. Sample surface deterioration process was observed by
the introduction of the AE signal and its evaluation by a suitable mathematical system. In thecontribution are presented results of sample from bearing material tests. The AE signal at a running-in
and at a permanent surface damage is described.
TheoryFor the signal analyses, we selected not only the frequency analysis with utilisation of the Fouriertransformation, but the time-frequency transformation of the Wigner–Ville transform. Calculation of the
Wigner-Ville transform WV(τ,f) is done by means of the correlation function by the integral relation:
WV (τ , f ) = ∫ x(t + τ 2) ⋅ x(t − τ 2) ⋅ exp(− j 2πft ) ⋅ dt ,where τ is the time parameter,
f is the frequency parameter,
x(t) is the signal being analysed.
This transform belongs among the basic time-frequency distribution, which is not limited by the
Heisenberg's uncertainty relation. However its disadvantage is, similar to the Fourier transform,
interference at the calculation and especially the necessary high calculation performance.

81Fig. 1 History of AE
Description of results
Experiment results are shown in the Figs. 1 to 11. In Fig. 1 is demonstrated the whole course of the test.
AE count rates in 5 seconds intervals are recorded. In Fig. 1, areas 1 and 2 are marked out, from whichwere taken samples of the AE signals for further analysis. Signal analyses of the area 1 are introduced in
Figs. 2, 4, 6, 8 and 10 and they are from the running-in phase. Signal analyses of the area 2 are shown in
Figs. 3, 5, 7, 9 and 11. These are from the phase, where permanent surface damage occurred.
From the Wigner-Ville spectrum from the AE signal of the area 1, we find significant frequency,
especially in the band around 1 MHz in time of about 20 to 30 µs, and further around 2 MHz in time ofabout 5 µs. This is in coincidence with both the frequency spectrum and the time change, where it may
be the burst type of acoustic emission signal. The higher values in area over 2.5 MHz correspondprobably an electric noise.
For the AE signal from the area 2 (Fig. 1), pitting occurred on the surface. By the Wigner-Ville
spectrum, the most meaningful frequency range is estimated between 0.5 to 1.5 MHz and that in timeintervals of about 15 µs and further between 24 and 32 µs. Here, in contrast with the previous spectrum,
no apparent maxima over 2 MHz are found.
Conclusion
The time-frequency transformation, Wigner-Ville transform, enables more complex view on the acoustic
emission signals than the Fourier transform and time change. It significantly improves information about
the acoustic emission signal.

82

Publications
1) Mazal, P., Dvorácek J., Kolár, D.: Determination of Contact Fatigue Properties byEmission Technique, 7th ECNDT, 1998, Copenhagen, Denmark.

Acoustic

2) Dvorácek J., Petráš J., Mazal P., Kolár, D.: Acoustic Emission and Contact Properties of Surface
Layers of Materials, 23rd European Conference on AET, 1998, Wien.
3) Kolár, D., Mazal P., Dvorácek, J.: Tribology characteristics and contact fatigue of structural steel
and grey cast iron with laser and plasma nitrided surface in comparison with bearing steels, 11th
IFMT-4th ASM Europe, 1997, Paris.
4) Mazal, P., Kolár, D., Dvorácek J.: Acoustic Emission Technique Utilisation for Pitting Observation of
the Grey Cast Iron with Heat Treated Surface, 11th IFMT - 4th ASM Europe, 1997, Paris.
5) Dvorácek J., Mazal P., Kolár, D.: Diagnostic possibilities of contact loaded surfaces by Acoustic
emission utilisation, International science-technical conference „Machinery technology and rolling
bearings ´97“, 1997, KTU ŽU, Žilina.
6) Mazal P., Dvorácek J., Petráš J.: Method and technique of Acoustic emission development in theInstitute of Design of the Technical University in Brno, Zborník referátov Medzinárodnej XXXVIII.
konferencie katedier astí a mechanizmov strojov, 1997, SjF STU, Bratislava.
7) Mazal P., Dvorácek J., Petráš J.: Acoustic emission at contact loading of grey iron with heat treatedsurface, Quality and reliability of machines, 1997, KSS SPU, Nitra.
9) Mazal P., Dvorácek J., Kolár, D., Petráš J.: Detection of the contact failure stage by Acoustic
emission., Defektoskopie 97, 1997, NDT, Praha.
10) Dvorácek J., Mazal P.: Utilisation of the Acoustic Emission for Discovering of Material Pitting. In:
4th Symp. DESIGN 96, 1996, University of Rijeka, Vol.1., Opatija, Croatia.
11) Mazal P., Dvorácek J., Petráš J., Dušek F.: Experiences with AE10C device utilisation at contact
fatigue tests, Acoustic Emission 96, 1996, VUT, Brno.
12) Korenská M., Pazdera L., Smutný J., Weber Z.: Application of Acoustic Emission Method on
the Loaded Timber with Different Humidity Analysed by Wigner Spectrum, CIB W 40 Meeting,proceedings, Czech Technical University, Faculty of Civil Engineering, Prague, August 30 – September
3, 1999, pp. 165-170.
13) Smutný J., Pazdera L.: Philosophy of Measurement and Time-Frequency Analysis of Vibration from
Railway Transport, Railway Engineering '99, 2nd International Conference, London, UK, 25-26 May,
1999, (pp. 1-10) CD, ISBN 0-947644-39-3.

Acknowledgement
The contribution was elaborated in frame of complex program of the GACR No. 101/96/K264 - Limit
states of construction materials with utilisation of non-standard test methods. This research has been
conducted at the Department of Physics as part of research project of Grant Agency Czech Republic
GACR No. 103/97/P140.

83

Fig. 2 Signal AE by running-in.

Fig. 3 Signal AE by pernament damage.

Fig. 4 Fourier transform of signal in Fig. 2.

84

Fig. 5 Fourier transform of signal in Fig. 3.

Fig. 6 Wigner-Ville spectrum contour graph.

Fig. 7 Wigner-Ville spectrum contour graph.
85

Fig. 8 Wigner spectrum 3D graph.

Fig. 9 Wigner spectrum 3D graph.

Fig. 11 Wigner spectrum image graph.

Fig. 10 Wigner spectrum image graph.

86

ACOUSTIC EMISSION MONITORING OF HYDRIDE CRACKINGIN ZIRCONIUM
A. BARRON and C. ROWLAND*
Bodycote, Daventry, UK and *Pancom, Cambridge, UK
ABSTRACTThis paper describes the use of acoustic emission (AE) to detect the onset of delayed hydride cracking(DHC) in hydrogen damaged zirconium laboratory notched specimens. The onset of the DHC is an
important factor in the integrity of an in-service component. The testing method described will help toquantify the number of cycles required to initiate the DHC and determine the critical section of thethermo-mechanical duty cycle, at which the DHC mechanism would initiate. The DHC mechanism andthe experimental method of AE monitoring during thermo-mechanical cyclic loading are discussed. The
development of on-line linear location via wave-guides proved to be vital to eliminate extraneous noise
because of the test environment. The advantage of AE over the previous use of the DC potential drop
technique is shown and the detection of DHC on the negative temperature slope of the thermomechanical cycle is demonstrated.
INTRODUCTION
General BackgroundIn service, zirconium alloy components may be subjected to loads induced from thermal warm-up andcool-down cycles, causing cyclic stresses and strains, which are potentially a significant factor for the
integrity of these components due to the possible onset of delayed hydride cracking.
Zirconium alloy components can accumulate high levels of hydrogen pick-up during their service
lifetime. In zirconium and its alloys, excess dissolved hydrogen precipitates as zirconium hydrides.
These zirconium hydrides are brittle, and tend to precipitate preferentially at high stress points such as
discontinuities or notches in stressed components subjected to thermal cycles. If failure of either thehydride or the hydride interface occurs the surrounding ductile material matrix will arrest crack growth.
As hydride formation is enhanced by the application of stress, another and possibly larger hydride canthen form at the newly generated crack tip. Further crack propagation can then occur, caused by thisrepeated hydride formation and failure cycle until total fracture of the component occurs. Thismechanism of crack initiation and slow propagation is called delayed hydride cracking (DHC).
The Hydride and Thermo-Mechanical Problem
As part of the design justification for DHC of certain zirconium alloy components, there is a need tocharacterise the hydride behaviour of the zirconium alloy material under simulated service conditions.
To create a realistic, although worst case scenario, notched laboratory specimens were subjected to athermo-mechanical cycle, which represents the major, and most damaging, events in the service cycle.
The definition of a thermo-mechanical cycle is when the stress intensity at a stress concentration
changes simultaneously with the temperature. The combination of stress and temperature is the key
factor. Hydrogen is concentrated by the stress field below the notch, and on cool down, hydrides
precipitate locally in this area. These brittle hydrides can then be ruptured by either an increase in the
stress intensity on cooling or by the local stresses applied as the brittle hydride phase precipitates.
87Careful manipulation of the hydrogen level in the material and the thermo-mechanical cycle applied canhopefully mimic (in test specimens) the actual service conditions experienced when the material is in its
deteriorated condition. A specially developed pulsed DC potential drop (DCPD) system had been usedpreviously to monitor fatigue crack initiation on similar notched bend specimens under isothermal test
conditions [1]. The early detection of hydride cracking, rather than fatigue cracking at its very initialstages, is essential if laboratory techniques are to be used in characterising the damaging thermomechanical hydride formation fracture process. The use of acoustic emission (AE) was therefore
considered necessary as it was thought that the temperature changes in the thermo-mechanical cycles
would render the potential drop technique impractical. There is also a possibility that the hydride
cracking may initiate sub-surface, and of a localised nature, which would prove difficult to detect with
DCPD.
Literature Review
Work on material more susceptible to hydride precipitates [2] observed that the temperature at the onsetof AE production during hydride formation in Nb, Ta and V was dependant upon the level of hydrogenconcentration. It was concluded that the AE detected was due to cracking of precipitate particles rather
than phase transformation or plastic deformation phenomena.
Investigation into brittle crack growth in metals [3] concluded that in some materials a one-to-one
relationship exists between macroscopic crack movements and AE. A correlation was also derived
between the amplitudes of the emissions and the energy release or size of the cracking event.
A study of stress corrosion cracking in high strength steels [4] used an electrical resistance technique(DCPD) and AE for simultaneous measurement of the stress corrosion cracking. No particular problems
were highlighted in operating the two systems, although the testing was all performed at room
temperature. The AE system was found to be more sensitive to the initiation of the stress corrosion
cracking than the DCPD system and this was attributed to non-uniform crack propagation during theearly stages of cracking. Initial cracking took the form of localised tunnelling, but eventually the crack
front broadens across the test piece thickness and then propagates as a whole. As the DCPD techniqueeffectively averages the crack length under its field of view it will therefore underestimate the length ofthe crack growth in the early stages. The AE technique, being sensitive to the elastic stress waves
generated by the initial cracking, will therefore be better at detecting crack initiation in the brittle
material.
Research work has been performed on the influence of hydrides on the ductile fracture of zirconium
alloy [5]. Using AE techniques and static tension tests, two types of hydride induced embrittlement wereidentified. The first is the delayed hydride cracking process caused by stresses below the yield stress ofthe material and at comparatively low temperatures (<100ºC). The other is the whole scale reduction of
the alloy toughness due to the precipitation of a large number of hydride platelets. The fracture
toughness (K IC) of the zirconium hydride was found to be 1 MPa√m at room temperature and 3 - 4
MPa√m at 300ºC.
Fracture initiation studies on zirconium hydrides [6], again using AE and static tension tests, showedthat if, at room temperature, the average hydride platelet length was in excess of approximately 50 - 100µm that a critical applied stress was the governing factor in the failure initiation of the hydride.
Subsequent investigation [7] studied the initiation behaviour at elevated temperatures. Although thestatic tension tests performed in this study were under isothermal conditions this is the first reference tothe use of AE at significant elevated temperature (300ºC) to measure hydride cracking.
88The results from the specimens showed that up to 100ºC to 150ºC cracking at the hydrides was initiatedat slightly below the yield stress of the material. When the hydrides fractured, bursts of AE eventsoccurred in large numbers. However, at higher temperatures, the hydrides were able to flow with theductile matrix without cracking, and hence very few AE events were recorded.
All the reviewed work used AE techniques either at room temperature or at elevated isothermal
conditions, recording cumulative AE data to display damage trend analysis. The work reported in thispaper involves AE techniques to detect the individual failure initiation of reoriented hydrides, in notched
specimens, using thermo-mechanical cycling to create a delayed hydride cracking mechanism
OBJECTIVES§§

To develop laboratory techniques to allow the AE activity from the initiation of delayed hydride
crack formation in Zr-2.5Nb alloy to be detected during thermo-mechanical loading.
To develop laboratory techniques to allow the simultaneous use of AE and DCPD crack detection
techniques during thermo-mechanical loading.

TEST ARRANGEMENT
The notch specimen (see Fig. 1) was loaded by a cantilever configuration and mounted in an insulated
hot chamber. Wave-guides and the DCPD are attached to the specimen as shown in Fig. 3, showing the
AE sensors outside the hot chamber. The heating method was a forced air circulation system, where
heating and cooling rates of at least 1°C/min. was achieved to perform the thermo-mechanical cycle.

Fig. 1: Notch specimen.

89

Fig. 2: Location calibration test rig.
The AE sensors (model Pancom P15W) are resonant at 150 kHz. To avoid coupling interface problems
the element of the sensor is embedded into the conical head and was screwed on to the 6 mm mild steel
wave-guide to provide easy removal and a consistent interface. Likewise the junctions of the specimen
and wave-guide are also threaded joints. The AE system provided on line location analysis of the AE
activity; this was very important because of the high background noise from the rollers, PD andthermocouple wiring, plus differential expansion activity at the test rig interfaces. The test arrangement
was also designed with different lengths of wave-guides on either side of the test specimen. This
arrangement was used to shift the null point of the specimen and wave guide assembly to some point
remote to the notched area on the test sample, thereby any spurious electrical noise hitting both AE
sensors simultaneously would then not resemble an AE event at the notch. To verify the ability to locate
signals coming from the notch during the feasibility study, a calibration specimen was used which had
three wave-guides directly attached to the specimen, see Fig. 2.
Two test parameters were fed to the Vallen AE instrument AMSY4; the load output from the servo
hydraulic testing machine and the temperature from a ‘K’ type thermocouple, the latter being connected
directly onto the specimen. The preamplifier gain was 34 dB and the threshold of AMSY4 set to 50 dB.

90

Fig. 3: Test arrangement.

Fig. 4: Thermo-mechanical cycles.
A typical example of the thermo-mechanical cycle is shown in Fig. 4, noting the initial temperature
cycles at no load to provide ‘shakedown’ of some of the mechanical elements in the system.
91

RESULTS
Location calibrations were performed using pencil-lead breaks (2H, 0.7 mm diameter x 2 mm length) on
the special external wave-guides (see Fig. 2), at room temperature and various elevated temperatures up
to 290 C. The results of the calibration showed that location could be obtained around the notch to an
accuracy of ±6 mm. This correlates well within the maximum theoretical accuracy expected when taking
into consideration:
1. the depth of the specimen (4 mm)
2. the two different velocities of mild steel and zirconium.
3. wave mode changes introduced at the material/structure interfaces.
Sources of erroneous noise found during the initial thermo-mechanical trials included:a) the interface between the jig and the hot box.
b) the high temperature ceramic based adhesive to secure the DCPD current pick up leads.
c) wave-guide and specimen junctionsd) reaction points of the specimen.
The noise from a) was greatly reduced by transferring the main rig reaction point outside the hot box,
then it was not subjected to the thermal cycling as shown in Fig. 2. Extensively modifying the methodby which the specimen wiring was restrained removed the noise condition of b). The remaining noisefrom c) and d) was overcome by using on line linear location, which allowed analysis of the data
without stopping the test. Calibration using pencil-lead breaks were made at the beginning and the endof the test at the notch. A repeatability of ±4 mm was obtained.
Initial trials using thermal cycling at a high constant load showed that the DCPD changed linearly withtemperature at an approximate rate of 1 µV/°C. There was a correlation between the AE output
characteristic of hydride failure and a change in the DCPD output. Although the DCPD system used wasstable and sensitive (typically 5 µV change for 50 µm fatigue crack growth) it became apparent the AE
was more sensitive, being able to detect isolated hydride failure.
The following results show a typical thermo-mechanical test. Figure 5 shows the total energy against theload and temperature applied to a specimen over nine full thermo-mechanical cycles. It can be seen from
Fig. 5 that there are two main regions of AE activity;
1) combination of low temperature/high load and2) combination of high temperature/ low load.
The result of filtering the data on those two criteria is shown in Figs. 6 and 7, respectively. Figure 6
shows that the cracking at the notch, occurring on the rising slope of the load and whilst the temperature
is falling. Whilst from Fig. 7 there is no activity at the notch. All the data in Fig. 7 is erroneous noisefrom the reaction points and the wave-guide specimen interface, generally associated with fatigue
cycling during the steady state section.
Hydride cracking was first found in the 4th cycle as shown by Fig. 8, with the load rising (196 N) and thetemperature falling (100°C). This demonstrates the effectiveness of parametric filtering of the data,
which enhanced the ability to show clearly an individual hydride cracking at the notch.
Performing a series of these tests has allowed the interaction of the crack tip stress intensity (K) and thenumber of cycles to the initiation of the DHC mechanism to be investigated. The critical section of thethermo-mechanical duty cycle, at which the DHC mechanism would initiate has also been identified.

92

CONCLUSIONSThe background level of AE associated with temperature and load cycling was such that a twin sensor,
linear location method of AE testing was necessary. The linear location method enabled AE activity
from the notch area of the specimen to be distinguished from the rest of the AE activity. This linear
location method was not sensitive to temperature cycling.
Two crack detection systems, AE and DCPD, were completely compatible. The instrumentation
associated with each technique did not influence the results obtained from the other. This was achieved
for thermo-mechanical cycling conditions. There is a high degree of confidence that an AE signatureassociated with hydride failure for this specimen configuration has been identified. This characteristic
signature was found to be extremely repeatable.
The conventional DCPD technique failed to discriminate individual hydride failures and ultimately was
unable to find small hydride cracking. The conclusion is that a stage has been reached when the AE
system can detected hydride cracking, which cannot be found using conventional DCPD or metallurgical
techniques.
The test program showed that manipulation of the crack-tip stress intensity during the thermomechanical cycle influenced the time to initiation of the DHC and the subsequent rate of DHC
mechanism. Under static load conditions, the initiation of hydride failure was consistent with expected
ductile-to-brittle transition temperature. The developed AE technique was able to establish the thermomechanical conditions, at which the DHC mechanism would initiate.
It can be concluded that this programme of development has produced an AE testing technique, which iscapable of detecting the initial stages of spontaneous hydride cracking characteristic of the DHC. The
sensitivity of the system is such that localised hydride failure can be detected at a stage not normallypossible using existing techniques, which are compatible with thermo-mechanical cycling.
REFERENCES[1] Bodycote Materials Testing Ltd, 1996, Isothermal Notched Fatigue Testing, Unpublished Report.
[2] G. Cannelli and R. Cantelli, 1979, Acoustic Emission Stimulated by Hydride Formation in Niobium,
Tantalum and Vanadium, Advances in Acoustic Emission, Dunhart Publ., Knoxville, Pages 330- 335.
[3] J.C. Radon and A.A. Pollock, 1972, Acoustic Emission and Energy Transfer During Crack
Propagation, Engineering Fracture Mechanics, 4, 295 - 310.
[4] G. Green, P. McIntyre, 1981, A Study of Stress Corrosion Cracking in High Strength Steels Using
Acoustic Emission Techniques, 5th International Conference on Fracture, Advances in Fracture
Research, 4, 2049 - 2057.
[5] S.A. Nikulin, M.A. Shtremel, V.G. Khanzhin, B.M. Fateev, V.A. Markelov, 1993, Influence of
Hydrides on Ductile Fracture in the Zr-2.5 Nb Alloy, Nuclear Science and Engineering: 115, 193-204.
[6] M.P. Puls, 1987, The Influence of Hydride Size and Matrix Strength on Fracture Initiation at
Hydrides in Zirconium Alloys, Metallurgical and Materials Transactions, 19A, June 1988.
[7] R. Choubey and M.P. Puls, 1993, Crack Initiation at Long Radial Hydrides in Zr-2.5Nb Pressure
Tube Material at Elevated Temperatures, Metallurgical and Materials Transactions, 25A, May 1994.

93

Fig. 5: Summation of AE energy from nine thermo-mechanical cycles, showing two regions of activity; 1)
combination of low temp/high load & 2) combination of high temp/low load.

Fig. 6: Showing cracking at the notch with rising load and falling temperature.
94

Fig. 7: Showing erroneous noise at low loads and high temperatures.

Fig. 8: Showing the initial crack at the notch during the fourth thermo-mechanical cycle.

95

Examination of Plate Valve Behaviour in a Small Reciprocating Compressor using
Acoustic Emission
J.D. Gill, R.M. Douglas, Y.S. Neo, R.L. Reuben and J.A. Steel
Dept. of Mechanical and Chemical Engineering, Heriot-Watt University, Edinburgh. EH14 4AS.
SUMMARY
The gradual failure ofcompressor valves due to spring failure or damage to the valve face or seat canlead to loss of plant efficiency. It would be ideal for plant operators to be able to ascertain thecondition of the valves on a continual basis to avoid expensive shutdowns or extremely conservative
replacement procedures. The use of a non-intrusive detection technique, such as offered by acoustic
emission, has proved to be the least influenced by machine resonances and other external sources ofvibration. By examining the acoustic emission signal associated with the operation ofthe valves it has
been possible to detect discernable differences resulting from changes in the condition ofthe valve.
INTRODUCTION
Compressors are, in theory, quite simple machines with few components to malfunction. The majority
of compressors in operation today employ plate valves that open in response to the pressure in thecompression chamber as opposed to a timed mechanical force. Whereas acoustic emission monitoringhas been successfully applied to large compressors employing poppet valves [1] no work has yet been
published on smaller compressors employing the more common plate-type valves. Common faults insuch valves are delay in valve opening, fluttering of the valve while in the open position and delay inclosing [2,3], each of which are associated with valve degradation or process conditions. The effective
timing of valves has been regularly used to signify faults in the operation of compressors [4,5], but thisrequires the use of pressure measurements. A large percentage of industrial compressors in use today
operate in hazardous environments and/or compress hazardous gases and so the use of intrusive sensors
is not ideal as leakage of the gas into the atmosphere may occur, or a costly shutdown of the machine
may be necessary to fit the transducer. Acceleration measurements have long been used to detect thecondition of the compressor valves. However, it has been found that the signal recorded by atransducer mounted on the external surface of the compressor, is so affected by reverberation anddispersion that the signal is no longer recognizable as the originating force [3]. The use of acoustic
emission monitoring, which utilises a much higher propagation frequency and across a much larger
bandwidth tends to overcome this problem. This is because a great deal of the interference in thevibration signal is caused by low frequency structural resonances.
EXPERIMENTAL APPARATUS
A series of tests were carried out on a Broom & Wade two cylinder, reciprocating compressor (Figurela), during which both acceleration and acoustic emission were monitored. Two Physical Acoustics
D9203 acoustic emission transducers with outputs band-pass filtered between 0.1 and 1 MHz were
mounted on the cylinder head studs (Figure 1b) using aluminium clamps which also allowed a Bruel &Kjaer 4334 accelerometer to be fitted in the same axis as the piston. The signals from the two acoustic
emission sensors were fed through a signal conditioning unit which allowed the gain and averaging
time (as required) to be adjusted. Both raw and RMS data was acquired with a shaft encoder signal toallow the signal to be re-sampled with respect to crank position. All calculations involving timing of
mechanical events were averaged over ten cycles and all RMS measurements were sampled with anaveraging time of 40 ).lS. A Kistler 6121Al in-cylinder pressure transducer was also fitted to givedetailed information on the behaviour of the valves during the tests.

Accelerometer

AE Transducer I

Discharge Valveousmgs

In-Cylinder
Pressure
Transducer

AE Transducer
Position 3
Inlet Valve Housings
Figure 1a: Broom & Wade D41 W Reciprocating
Compressor Cross-Section

Figure Ib: Compressor Cylinder Head Set-up

Two discharge valve and one inlet valve conditions were examined to simulate; a damaged discharge
valve seat face, a discharge valve sticking partially open as a result of the ingress of dirt into the systemand progressive fatigue failure of the inlet valve spring. As the valve seat and face are both metal eventhe slightest damage to either face will cause gas to leak past the valve. The inlet and discharge valves
are identical and consist of the valve seat and guard, two single-coil helical springs and the valve plate(Figure 2a).
Castellated

Cut Springut

Valve Seat

Valve

Valve Spring

Valve Spring

Valve Guard

GrooveBolt

Figure 2a.: Valve Assembly

Figure 2b: Damaged Valve Seat

Figure 2c: Cut Valve Spring

The seat failure was simulat d by introducing a small groove (Figure 2b) into the seat to allow gas toleak past the valve. The second experiment consisted of attaching a shim, O.5mm in thickness, to thevalve seat to prevent the valve from seating properly simulating foreign matt r on the valve face. The

fatigue simulation consisted of gradually reducing the spring stiffness by introducing a series of cuts ofvarying depth into one or both of the springs (Figure 2c).
NORMAL OPERATION
Figure 3a shows the behaviour of the valves and a p-V trace of a typical reciprocating compressor, andFigure 3b shows the RMS signal obtained from the acoustic emission sensor attached at position 1(Figure Ib).
Disc:hargeValve
Motion09"...,----

f7,06
p-VOiagram

,

~"

ill.4

f

03
0.2

Suction Valve

Motion01oFigure 3a: Typical Reciprocating Compressor Behaviour

1~',-t .rt . _./,'
l ::?'

InWViWl,"""*""

.\

o

""-

V."~

....._..... ~.-.

~

m

.

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00

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m

315

~

ClnshIfIPosiIoIlI~eesATOCI

Figure 3b: Re-sampled RMS Normal Running AE Signal.
AE Sensor Position I & Cylinder Pressure Trace

Superimposed on this is the in-cylinder pressure trace used to determine the general operation of thecompressor. The large AE peaks with high rise time correspond to each of the main valve events, aslabelled. The size of these peaks varies and, because of the timing of the mechanical events in the twocylinders, some AE peaks coincide
FAULT CONDITIONS
The following sections describe the effects that each of the fault conditions has on the AE and pressurerecord of the compressor.
Grooved Discharge Valve Seat
Figure 4a shows the cylinder pressure trace with the compressor operating with the damaged valve seat
(Figure 2b) while Figure 4b shows the AE and pressure trace for the compressor operating with thedamaged valve seat. The compressed air is discharged into a common manifold (i.e. both valvesdischarge into the same pipe), which is then allowed to discharge to atmosphere through a relief valve,maintaining a peak pressure (there are two peak pressure pulses in the cycle caused by each of the twocylinders) of approximately 8.6 bar in the manifold. As a result of this, when there is a fault causing
the discharge valve not to seat correctly, pressure in the manifold will cause compressed air to leakback into the cylinder and also reduce slightly the pressure in the manifold. This effect has a number of
influences on all valves on the compressor. The pressure differential between the inlet manifold(effectively atmospheric) and the cylinder pressure (directly related to the work done by the piston) is
reduced. Therefore, more work is required by the piston to open the valve i.e. the valve opens later inthe cycle. For the same reason, the inlet valve closes earlier in the cycle although this is masked in the
acoustic emission record by the discharge valve in the adjacent cylinder closing. At bottom dead centre(BDC) and the beginning of the compression stroke the pressure in the cylinder should beapproximately atmospheric. However, as a result of the poor seating of the valve, the pressure is
marginally greater and steadily increasing as the pressure in the discharge manifold slowly leaks backinto the cylinder.

0'

'"l:

0'

0.8

08

0.7

ii"O.7

§
~O.S

~

~05

1

.i

0.3

1\

t3

02

\.

02

\\

0.1

\

\,

"_.

InIe1Vllltve~~"":":'~~~.goo,----:-~500~~,=OOOO====""'500~=;;20=oo:==22500.;=::;;;3000:;=-,d3500

0.4

\.

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C')'Iinder Volume

Figure 4a: p-V Diagram - Normal Operation (solid line)
Damaged Seat (dashed line)

\
'0

135

180

225

270

315

360

CrBnk§haft Position lDegrees ATOC)

Figure 4b: Re-sampled rms AE & Cylinder Pressure Tracefor Damaged Valve Seat (Sensor Position I)

The air in the discharge manifold continues to leak into the cylinder until the piston has done enoughwork to overcome the pressure differential (between cylinder pressure and inlet manifold pressure) andopen the discharge valve. This increase in the cylinder pressure by the leaking valve (and also the
reduced pressure in the discharge manifold) causes the valve to open earlier in the cycle. The groove inthe valve seat causing leakage into the cylinder causes an 11 % reduction in peak pressure in the
discharge manifold.
Using the acoustic emission signal it is possible to detect the change in timing of the discharge valve
opening. In the case of the grooved valve seat the discharge valve opens at 295° as opposed to 312.5°
for normal running. It is also possible to detect the valve opening time using the acceleration data.
However, the acceleration data, although it detects mechanical events in the cycle (i.e. opening andclosing of the valves) does not detect the fluid mechanical noise generated by the leaking of gas into
the cylinder. This is seen in the AE signal as a high frequency portion beginning as soon as thedischarge valve closes at approximately top dead centre (TDC). This low amplitude high frequency
signal continues until approximately 270° after BDC when the cylinder pressure equals the pressure in
the discharge manifold. At this point the flow of gas into the cylinder ceases.
Unseated Discharge Valve Plate
0.25

.3

Discharge ValveI--I

2Ctosing

0.25

2 Opening

1,
0.2

+

{:O.l

i .,

Discharge valve\

1 Opening242.5 Degrees

11
20penlnJ0

D1seharlleVai

1 Closlllg

~

~

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100

m

m

~

Crankshaft Position [Degrees ATOC]

Figure 5: Re-sampled rms AE - Acquired from Position I with Valve Closing Fault

Figure 5 shows the AE signal acquired from position 1 (Figure 1b) with the shim attached to the valve
seat. As can be seen the much more severe fault that prevents the valve from seating completely hascaused the discharge valve opening point to be advanced to 242.5° (an advance of 70°). The highfrequency content of the signal during the inlet stroke has increased in amplitude as a result of thegreater flow of gas into the cylinder during this period of the stroke.

Fatigued Inlet Valve Spring
Figure 6a shows the rms AE signal obtained from position 4 with a normal spring and a broken spring.
The compressor is operating under no load conditions i.e. it is venting to atmosphere.
11_5,------,-------------,
Normal1--...

i~:~~~

3mm cui &----~mcut

1,\,
\
\

?mmcut&\mmcut~6mmc &
6mmcu

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45

90

135

180

225

09

270

Crankshaft: Position [Degrees A"TOC]

Figure 6a: Re-sampled rms AE Normal Running &Weakened Inlet Valve Spring (signal inverted)

Figure 6b: Inlet Valve Opening Times versus Fault
Condition

As the spring pressure is gradually weakened the compressor is required to do less work to overcome
the inlet valve spring. Therefore, the valve opens earlier in the cycle (Figure 6b). However, thiscondition is only detectable under no load conditions. When the compressor is operating under load
the cylinder pressure immediately after the discharge valve closes (i.e. approximately TDC) is
marginally less than the discharge pressure. Therefore, the compressor has to do more work to open
the inlet valve i.e. the piston has to move further down the suction stroke. At a working pressure of 8.6
bar the inlet valve opens at 45° after top dead centre (ATDC). At this point the piston is moving with amuch greater velocity as a result of the reciprocating motion. This causes the valve to open moresuddenly and therefore, it has not been possible to detect the change in spring stiffness while thecompressor is under load. However, in larger machines, the behaviour has been found to be more akinto no-load conditions in terms ofthe inlet valve opening timing. Therefore, it is expected that it would
be possible to detect weakened springs on large industrial machines under load conditions.
Valve Flutter
The final experiment involved reducing the speed of the compressor to induce valve flutter. Valveflutter damages both the valve spring and, in particular, the valve face and seat, due to the increased

1-

Inlet Valve
Initial Opening[12000/AEstufflngPleleFault

AE Normali1~~6000~!- UV\~J1-\.""" ~··----~~~·~~ ~!.4000a~ ~c- \lJ!J~
,

•

jI

-J'_=~=AE=-"'11·20000>--_...:.::V'=1ve4F~5Iutt=-"----::",-----~"',---------c!
Cfankshaft Position (Dl!9'"ees AlOCj

Figure 7: Re-sampled rms AE & Cylinder Pressure Tracewith Inlet Valve Flutter

Figure 8: Ethylene Compressor rms AE Trace - Normal
Running & Stuffing Box Gas Leakage

number of impacts. Figure 7 shows the effect that this has on the AE signal. As can be seen in thepressure signal the valve opens and closes a number of times after initial opening. This is clearly
detected in the AE signal as three pulses.
CONCLUSIONS
Acoustic emission has been demonstrated to be a suitable replacement for cylinder pressure
measurements for cases where the environment will not permit such measurements to be made. It has
proved capable of detecting the mechanical operation of the valves on a small, two-cylinder
compressor. Furthermore, it has been shown that, whereas vibration measurement can only detectmechanical events [3], acoustic emission measurements can be utilised to detect gas flow within thecompressor. This has already been shown to be useful on a much larger scale [1]. Measurements(reported elsewhere) have been made on a large, two-stage, horizontally opposed reciprocating
compressor used to boost the pressure of ethylene from 260 barg to 3200 barg. Although this machine
was fitted with poppet valves as opposed to plate valves, the AE response due to gas leakage shows
very similar characteristics to those observed here. The compressor was found to have ethylene leaking
past the stuffing plate into the sump of the machine. This was detected using the acoustic emission as aprolonged high frequency burst (Figure 8), although in this case the amplitude was much greater
because of the increased working pressure.

Work is still being carried out on the Broom & Wade compressor with further work required to gain afull understanding of the effect that various faults have on the acoustic emission signal. However, it
would appear that, at present, it is possible to detect certain faults that could not be directly detectedusing vibration data. It is also possible to detect the faults that can be detected with vibration
measurements with greater confidence.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the funding provided by the EC under its Brite-Euram programme,
Project number BE96 3491.
REFERENCES[1]
Gill, J.D. et at. Monitoring of a Large Reciprocating Compressor. Condition Monitoringand Diagnostic Engineering Management (COMADEM) '98. 1 pp. 317-326, 1998
[2]
Matsumura, M. et al. Behavior and Analysis of Reciprocating Compressor Valve. Kobelco
Technology Review. 14 pp. 20-24[3]McCarthy, D.J. and Lyon, R.H. Recovery of Impact Signatures in Machine Structures
Mechanical Systems and Signal Processing. 9(5), 1995, pp. 465-483[4]Wang, D. S. and Pin, L. Vibrational Analysis of the Motion Process of Compressor RingValve Plates. Purdue Compressor Engineering, Lafayette, Ju11984, pp. 203-217[5]Li, C. J. and Yu, X. High Pressure Air Compressor Valve Fault Diagnosis using
Feedforward Neural Networks. ASME: Winter Annual Meeting, New Orleans, 1993, pp.
375-380 (ASME: Manufacturing Science and Engineering).

A NEW METHOD OF AE SOURCE LOCATION IN PIPES
USING CYLINDRICAL GUIDED WAVES
Hideo NISHINO, Fukutoshi UCHIDA, Sunao TAKASHINA
Mikio TAKEMOTO and Kanji ONO*
Faculty of Science and Engineering, Aoyama Gakuin Univ., Setagaya, Tokyo JAPAN*Dept. Materials Science and Engineering, UCLA, CA, USA
SUMMARY
We have developed a novel location method of damage in a pipe using cylindrical guided waves detectedby a single AE sensor mounted on the pipe. For the source location in the axial direction, we use arrival
time difference between L(0, 1) and F(1, 1) modes at a certain frequency, extracted by wavelet transform.
The locations in an aluminum pipe are identified within an average error of 5% for wave propagating
distance of up to 500 mm. For location along circumferential direction, we utilize both the amplitude andphase information of L(0, 1) and F(1, 1) modes. A good relation between the amplitude ratio of L(0, 1)
and F(1, 1) modes and the circumferential source position is shown.
I. INTRODUCTION
Analysis of guided waves in a hollow cylinder poses much difficulty due to the complex circumferential
modes in comparison to plate (Lamb) waves. Gazis [1] first provided the theoretical background ofcylindrical guided waves (cylinder waves). Three modes of the cylinder waves are Longitudinal (L-),
Flexural (F-) and Torsional (T-). Experimentally, Fitch [2] first generated and verified the dispersion of Land lowest F-modes using a pair of piezoelectric sensors. Several studies have been attempted sinceutilizing the unique characteristics of a low energy leakage of the cylinder waves. Long range interrogationof pipes was attempted by using cylinder wave techniques [3-5]. Selective generation of a specific mode of
the cylinder wave was applied by Alleyne et al. [6] for nondestructive testing of piping in chemical plant.
They reported that the cylinder wave can propagate several tens of meters in a 76-mm steel pipe. Rose et alpresented [7] analysis of the cylinder wave attenuation due to energy leakage into liquid around a steam
generator tube. Our group made quantitative analysis of guided waves to determine the location anddynamics of environmental assisted cracking in metallic rod [8].
We propose here a novel source location method of AE signals from pipe damage. The method can obtainthe source location of AE signals propagating as cylinder waves. The AE signals are monitored by a single
AE sensor mounted at the end of the pipe. First, we estimate the source location along the axial direction
using the arrival time difference of L(0, 1) and F(1, 1) modes. For source location along circumferential
direction, their amplitude and phase information was used.
II. GUIDED WAVES IN A HOLLOW CYLINDER
We introduce the vibration mode and velocity dispersion of the cylinder wave. Phase velocity of thecylinder wave is computed from the characteristic equation, in a 6x6 matrix form derived from the wave
equation in the cylindrical coordinates with the stress free boundary condition at the inner and outersurfaces [1]. Vibration modes are Longitudinal (L-), Flexural (F-) and Torsional (T-) modes. These modes
102

Fig. 1. Circumferential vibration mode.
are described as L(0, m), F(n, m) and T(n, m), where n and m are the circumferential and radial(thickness) mode parameters, respectively. L-mode is axially symmetric, sometimes called as a breathingmode. Schematic illustrations of circumferential modes are shown in Fig. 1.
Meitzler [9] and Silk et al. [3] classified all the high order circumferential modes as the F-mode. In theirclassification, T-mode had only the circumferential fundamental mode (n=0). It is natural, however, thatthe n-parameter of T-mode is not limited to zero. Each high order T-mode is coupled only with a SH wave
at the cut-off frequency (the so-called longitudinal-shear wave in [1]). This new classification allows us tocorrelate the three cylinder modes to plate waves at diminishing wall thickness-to-outer diameter (t/d)
ratios; i.e., L- and F-modes to Lamb waves and T-mode to SH mode plate wave [10].
Figure 2 shows phase velocity dispersion of L- and F-modes for an aluminum pipe of 5 mm diameter and
1 mm thickness. They were computed for the longitudinal wave velocity of 6400 m/s and the shear wave
velocity of 3040m/s. Velocity dispersions of A0 and S0 Lamb waves for 1 mm thick aluminum plate arealso shown. Shape of the dispersion curves is identical for constant t/d. Therefore, the horizontal axis isnormalized by the product of frequency (f in kHz) and wall thickness (t in mm). This behavior is the sameas that of Lamb wave dispersion curve [11].
Figure 3 indicates the characteristic features of the velocity dispersions. Effects of t/d on the velocity
dispersion of L(0, 1) and L(0, 2) modes are shown for t/d = 1/2, 1/3, 1/5, 1/10 and 1/16. Note t/d=1/2 isfor a solid cylinder or a rod. With reducing t/d or increasing frequency-thickness product ft, L(0, 1) andL(0, 2) modes approach the velocity dispersions of A0 and S0 Lamb waves in a plate whose thickness isequal to t. At diminishing ft, the dispersion curves converge to that of a rod. This demonstrates that thecylinder waves have the characteristics between the rod waves and Lamb waves [12].
103Fig. 2 Phase velocity dispersion of cylinder wave (Aluminum pipe, d = 5 mm, t = 1 mm).

Fig. 3 Phase velocity dispersion of L(0, 1) and L(0, 2) modes for pipes with different t/d.
104

III. AXIAL SOURCE LOCATION
3-1 Method of source location
We obtain the source location of an AE signal using the velocity difference of L(0, 1) and F(1, 1) modes
monitored by a single AE sensor. As shown in Fig. 4 for a 1 mm thick aluminum pipe of 5 mm diameter,
we observe a large group velocity difference of L(0, 1) and F(1, 1) modes at low frequency range. Here,
we select the frequency f = 140 kHz, at which F(1, 1) velocity shows a maximum. Utilizing the groupvelocity vL of L(0, 1) mode and vF of F(1, 1), the source location along the z axis (z being the distancefrom the sensor in Fig. 5) is given by equation (1),

 vv 
z =  L F  ∆t ,
 vL − vF 

(1)

∆t is arrival time difference between L(0, 1) and F(1, 1) modes at the selected frequency. Though F(2,1)
modes exists in the same frequency range, a large arrival time difference between F(1, 1) and F(2, 1)
modes makes the separation of F(2, 1) from F(1, 1) possible.

Fig. 4 Group velocity dispersion with VL and VF at frequency f.
3-2 Experiment and results
We monitored cylinder wave signals by a Mach-Zehnder type laser interferometer (BMI, SH-140) having
the 20 MHz bandwidth and by a small AE sensor (PAC, PICO, nominal center frequency 0.5 MHz) at
100, 200, 300, 400 and 500 mm from the distal plane (see Fig. 5). Four aluminum pipes with d of 4, 5, 6
and 8 mm and t of 1 mm were used. The specific frequencies were selected for the F(1, 1) velocity
maxima and were theoretically determined as 210, 140, 100 and 80 kHz, respectively. Cylinder wave
signals were generated by a point focused pulse YAG laser irradiation at the distal of the pipes.
105Fig. 5 Experimental apparatus of verification.

Fig. 6 Waveforms of the cylinder wave AE's detected (a) by interferometer and (b) by AE sensor.
106Figures 6(a) and (b) show cylinder wave signals monitored by the interferometer and the AE sensor at z =300 mm for 5 mm diameter pipe. The interferometer output is radial displacement, while AE output is
expected to be velocity. The first portion of the wave around 60 µs is dominant to the L(0, 1), and second
around 120 µs is the F(1, 1) mode for each AE signal. Time transients of 140 kHz components of the two
waveforms, extracted by wavelet transform, are shown in Figs. 7(a) and (b), respectively. First peaks at
63.6 µs of the interferometer and at 63.9 µs of the AE sensor represent L(0, 1) mode, and second peaks at
120µs represent F(1, 1) mode. Using the ∆t's of 56.4 µs and 56.1 µs, the source distance z's were
estimated as 297 mm (1.0% error from 300 mm) with the interferometer signal and as 293 mm (2.2%
error) of the AE sensor output, respectively. Here, the theoretically group velocities vL and vF at 140 kHz
were 4860 and 2520 m/s.

Fig. 7 140 kHz wavelet coefficient of AE signal detected (a) by interferometer (a) and (b) by AE sensor.
Location errors for another pipe diameters and source distances are summarized in Table I (interferometersignals) and Table II (AE sensor output). Several source location attempts for 100 and 200 mm distanceswere not successful due to the difficulty in separating the L(0, 1) and F(1, 1) modes. Average error of all
the results obtained using the interferometer was 3.5%, while that using the AE sensor was 4.9%. The
107

latter was slightly larger due to a larger sensing area. Despite non-flat response of the AE sensor, we canestimate the source location in the axial direction with high sensitivity at lower frequency ranges. Thisfinding can be applied to the AE inspection of slender pipes and tubing, significantly reducing the cost ofdamage inspection.

IV. CIRCUMFERENTIAL SOURCE LOCATION
Although the source location in the circumferential direction is less critical, we present here a method forthe source location over the circumference of a pipe. The method relies on the amplitude ratio of F(1, 1) to
L(0, 1) modes. Amplitude of F(1, 1) mode (see Fig. 1) changes depending on the source location, whilethat of L(0, 1) mode is independent on the location. This is one of "the initial-value problem" of the wave
equation. Out-of-plane displacement in circumference of F(1, 1)-mode is presented by a sinusoidal
function Acos(2θ+ϕ). A is peak amplitude, θ indicates the circumferential source location and ϕ therelative circumferential position between the source and sensor. The largest amplitude is obtained for ϕ=0˚
and the amplitude is the smallest at ϕ=90˚ (Fig. 8).
An aluminum pipe of 5mm diameter and 1mm wall thickness was used in the experiment. Using thesystem shown in Fig. 5, we monitored cylinder wave signals produced by laser irradiation at five
circumferential positions (ϕ=0, 45, 90, 135 and 180˚). Detected waveforms are shown in Fig. 9.
Amplitudes of first peak A, due to L(0, 1) mode, were almost independent of ϕ, and the phase showed nochange. In contrast, the phase of peak B, due to F(1, 1) mode, changed at ϕ=90˚ and amplitude was thesmallest at ϕ =90˚ (in fact, buried in other unidentified high-frequency modes). Figure 10 shows theamplitude ratio of F(1, 1) and L(0, 1) modes as a function of ϕ. Closed circles indicate experimental data.
At ϕ=90˚, error bar is inserted because no clear F(1, 1) component at ϕ=90˚ was detectable. Solid curve108Fig. 8 Schematic illustration of F(1, 1) vibrationand detection point ϕ.

Fig. 9 Detected signals of cylindrical guided waves
for different source position ϕ.

Fig. 10 Amplitude ratio of F(1, 1) and L(0, 1) as a function of probe position ϕ.
represents a theoretically predicted amplitude variation. Here, the peak amplitude A was determined as 8.8
by a least square fit to the experimental data except the amplitude F(1, 1)/L(0, 1) = 0 at 90˚. Experimental
data agrees well with the theoretical prediction.
The present result demonstrates the feasibility of monitoring F(1, 1)/L(0, 1) and comparing it to thereference in order to identify AE source location in circumference.
109

V. CONCLUSION
We have demonstrated a new source location method of the hollow cylindrical guided waves. The source
location along the axial direction can be determined by utilizing the arrival time difference between L(0, 1)
and F(1, 1) modes. Utility of this method was verified for laser-induced signals monitored by a wideband
interferometer and an AE sensor. The axial source location was determined within 5% error using the AE
sensor for up to 500 mm. Owing to the low energy loss of the cylinder waves, the present method isapplicable to pipe inspection over a long distance.
We also showed a method for the source location in circumference. It utilizes both the amplitude and phase
information of L(0, 1) and F(1, 1) modes. A good relation between the amplitude ratio of L(0, 1) and F(1,1) modes and the circumferential source position was shown.
The application to the source location of real AE signals in long pipes remains as future challenge.
REFERENCE[1] D. C. Gazis, J. Acoust. Soc. Am., 31, (1959) 568.
[2] A. H. Fitch, J. Acoust. Soc. Am., 35, (1963) 706.
[3] M. G. Silk and K. F. Bainton, Ultrasonics, 17 (1979) 11.
[4] W. Bottger, H. Schnerder and W. Weingarten, Nucl. Engr. Design, 102 (1987) 356.
[5] M. J. S. Lowe, D. N. Alleyne and P. Cawley, Ultrasonics, 36 (1998) 147.
[6] D. N. Alleye and P. Cawley, J. NDE, 15 (1996) 11.
[7] J. L. Rose, J. J. Ditri, A. Pilarski, K. Rajana and F. Carr, NDT & E Int.. 27 (1994) 307.
[8] M. Takemoto, F. Uchida, T. Sato, Y. Mizutani, H. Nishino and S. Yamazaki, J. Jpn. Soc. Corrosion
Eng., 48 (1999) 733, in Japanese.
[9] A. H. Meitzler, J. Acoust. Soc. Am., 33 (1961) 435[10] B. A. Auld, "Acoustic fields and waves in solid", 2nd ed., Krieger, (1990) New York.
[11] I. Victrov, "Rayleigh and Lamb wave", ed. Lewis, Plenum Press, (1967) New York[12] H. Nishino, S. Takashina and M. Takemoto, to be submitted.

110

AE METHOD FOR PRESSURE VESSEL DIAGNOSTICS AT A REFINERY.
B.S. KABANOV, V.P. GOMERA, V.L. SOKOLOV AND A.A. OKHOTNIKOV
Kirishinefteorgsintez, Kirishi, Leningrad region, RUSSIA

ABSTRACT
Kirishinefteorgsintez was the first refinery in Russia, which introduced AE methods. Due to theadvantages of the AE method for increase of reliability of equipment and the desire to test moreequipment units in less time, own AE group was created and hydraulic and pneumatic tests of pressure
vessels are accompanied by AE testing. The localization of defective zones by the AE method has
proved to be more effective than the application of traditional NDT methods. Per Russian regulations,
all pneumatic tests must be accompanied by AE testing. There is a big demand for the replacement ofhydraulic tests by pneumatic tests, as the refinery operates many special vessels, which cannot bepressurized by water. For interpretation, the traditional AE criteria are used: the localization of AE
sources, verification of the Kaiser effect, pressure holding AE etc. AE source location with different
wave velocities in the relatively thin shells (the different Lamb waves modes) and some cluster analysis
algorithms are also used. 205 vessels have been tested since 1992. 29 of these vessels had been repaired
due to the result of the AE testing. An AE test database has been formed and includes results of the AE
testing and the corresponding follow-up inspections.
EXAMPLES OF AE TEST METHODS IMPLEMENTATION FOR VESSELS
The importance of the AE method used for testing the refinery’s equipment shall be demonstrated byexamples of defect detection. The capability to detect these defects with traditional control test methods,
without AE was miserable. The results are obtained with AMSY4 from Vallen-Systeme, although therefinery has used and still uses a LOCAN AT, manufactured by PAC.
EXAMPLE 1
Test subject is a heat-exchanger shell made of carbon steel with stainless steel cladding; thickness: 20mm; pneumatic test (scheme is presented in Fig. 1). Some results of planar location are shown in Fig. 2.
They had been used for the inspection of vessel shell areas with AE source location analysis. Other
instruments of post-analysis had been applied for more precise localization and classification of AE
activity zones. For example, some parts of such analysis are shown in Fig. 3. Left diagram shows
amplitude vs. counts of 3 channels in different colors, and indicates much higher amplitude at channel
14 compared to channel 6 and 13. These high amplitude signals (85-94 dB) indicate that the source
might be quite near the channel 14. Right diagram in Fig. 3 illustrates the use of information about risetime in interpretation of planar location results.
The final positions of zone activities and the positions of the AE-sensors are indicated in Fig. 4. Thesezones had been classified according their nature as follows: Zone 1 was generated by relaxation in theweld between shell and fixed support; Zones 2 and 3 were generated by relaxation in the welding zones
between shell and inside elements. (Note that processes of relaxation were usually correlated in Zones 2
and 3; therefore, the signals from different sources were generating the superposition of signals and thesuperposition was being registered by sensors from the location group, formed by channels 13, 14, 6,10.); in Zone 4, by means of traditional NDT methods, with search priority near sensor 14, the defect has
been detected inside the vessel at the welding zone around a dead-end boss (it was 45 mm in diameter).
The defect has developed as a result of corrosion cracking to 8-10 mm in depth.
111crackconfigurationand localization

support rings

bosses
#6

#5
#2

2600

#14

#4

screen

#16
#13

#15

fixed
supporttube bundle
guidesguide supports(corner 110)

movingsupport6000

Fig. 1. Heat exchanger scheme with construction elements producing the registrated AE signal mainarray at pneumatic test of the vessel.

Fig. 2. Location cluster parameters, corresponding Zone 1.
112

Fig. 3. Some diagrams, used for data interpretation by correlation; Counts vs. Amplitude [1] and Rise
Time vs. Amplitude [2] for channels # 6,13,14.
Y

support rings attachment zones

fixed support(0,600)

#2

#16

#4

#1

#8

#3

Zone 1(20,469)
#15

#5

#7

#9

corner 110 weld zone

#13

#6

Zone 3

Zone 2#11(-135,110)(-167,110)

Zone 4

#12

#10

#14

X

(0,0)
(829,0)(-829,0)

crack configurationand localization

moving support

bossesscreen attachment zone

Fig. 4. AE sensor location at the shell reamer (inside view). The most active AE sources are shown.
113

EXAMPLE 2
Test subject was a vertical vessel, located in the same shell with the other vessel. Both vessels are
divided with the flat one-piece screen shown in Fig. 5. AE test accompanied the hydro-test of the uppervessel. The tested vessel material was carbon steel with cladding. Wall thickness was 16 mm.
Due to the operation stresses, perforation at some points around the perimeter of the screen had
developed: in the weld between shell and screen, cracks were open. These cracks opened only under the
inner pressure and that is why they could not be detected with the traditional inspection methods during
shut down. Some characteristics of detected AE signals from the low band indicated that there had beenleakage. Some of them are shown in Fig. 6. However, outside of the shell, the leakage could not be
observed visually. In addition, the defects in the welds could not be detected with the other test
methods. For the problem resolution, additional information had been received with the help of signal
shape visualization functions, which in turn were used for AE source quality estimation by signal shapeanalysis.

#2

corrosion
defects

#1

#4

2000
2500

weld

upper
vessel

#7

#6

500

#5

#3

defect zones
in the screen

lowvesselscreenweld

Fig. 5. Scheme of vessels for inspection (two vertical vessels, divided with a screen).

114

Fig. 6. Some diagrams for signal parameters, used for data analyses.

Fig. 7. Examples of AE signal from leakage (Chan. 3) and crack indication (Chan. 4).

Figure 7 shows an example of two typical signal shapes for different source types. Sensor #4 (bottom
signal of about 5 mV or 74 dB amplitude) was located near the weld with some, not large, corrosion
defects. Sensor #3 (top signal, just above threshold) was located near the screen and it registeredperiodic leakage through the open cracks of the defective weld. It is necessary to notice that the low
vessel was also filled with water (prepared for hydro test). Water, pumped into the upper vessel forpressurization, increased the pressure till the stress in perforation sites reached the value necessary to
open the cracks. Then, the water from the upper vessel leaked into the lower vessel until the pressure in115both vessels was equalized. Since the defect was only active while the pressures in both vessels differ,no other NDT methods would have a chance to detect the defects. AE seems to be an optimal method todetect such defects.
In the considered example, other types of defects were successfully detected and the defect locations
were also determined.
EXAMPLE 3
AE effectiveness is high for large vessels with places that are not easily accessible. For such kind ofvessels, the combination of different location algorithms, as offered by the AMSY 4 system, is mosteffective. For example, good results were obtained with a combination of spherical and zonal location atlarge spherical vessels.
Vessel characteristics: material - carbon steel, thickness -16 mm, diameter – 10.5 m, cubic capacity 600 m 3. AE testing accompanied the hydro test. Due to AE inspection, there were two corrosion zones
detected at the vessel shell. One of the zones was detected with the help of a spherical location algorithm(Fig. 8). The second zone (weld piece) was determined by zonal location. Some data, characterizing thehigh relative AE activity of sensor #8, is shown in Fig. 9. AE results were confirmed with ultrasonicinspection and the defects were removed.

Fig. 8. Location determination of corrosion defects of the vessel shell with sphere location algorithm.
116

Fig. 9. Example of graphic form, used for corrosion defect location determination of the vessel shellwith zonal location algorithm.
CONCLUSION
AE methods are currently included in the nondestructive testing program of the plant and supplement
the traditional methods successfully. Recognizing the effectiveness of AE implementation, themanagement has expanded the areas of its use and continues making investments in AE development at
the refinery.

117

OPTIMIZATION OF ACQUISITION PARAMETERS FOR ACOUSTICEMISSION MEASUREMENTS ON SMALL PRESSURE VESSELS
Franz RAUSCHER
Institute of Pressure Vessel and Plant Technology
Vienna University of Technology, Vienna, AUSTRIA

ABSTRACTFor acoustic emission (AE) measurements on small pressure vessels, it is important to perform proper
location calculation for low and high amplitude events. The rate of AE events, which can be properly
located, is also important. Therefore, a test was developed to evaluate the amplitude of the weakest
properly locatable event, and to evaluate the rate of properly locatable events as a function of theamplitude. For the test, electrical signals were generated by a digital signal generator and transformed
to mechanical waves by a piezoelectric reference source. The test is used to adjust the acquisition
parameters of the AE equipment.
NOMENCLATURE
rate
test
amplitude at source
amplitude of event

frequency of pulses within the sequencenumber of sequences with predefined parameter variation
amplitude of the electrical signal in dB with 1 µV reference at the piezoelectric
sourceamplitude of the electrical signal in dB with 1 µV reference at first hit sensor

INTRODUCTION
At the Institute of Pressure Vessel and Plant Technology, experiments on small water-filled pressure
vessels, welded and non-welded ones, were performed. Weld failures and cracks at proof tests and crack
propagation at cyclic loading have been investigated [1-3]. When acoustic emission (AE) measurement
is used at such vessels, source location is necessary for the separation of signals coming from thephenomenon to be investigated from those coming from other sources.
At these vessels acoustic waves are reflected many times and run around the vessel more than once.
Therefore, the signals need very long time to diminish. Use of low threshold levels in this situation leads
to very long duration of the signals, in addition to the problems with regard to noise. The consequence isthat the acquiring channels are busy for long time intervals, and simple location calculation for
subsequent hits is not possible. In this situation, it is necessary to optimize the set-up: It must be possible
to perform location calculation for low amplitude events, and for a reasonable rate of AE events of highamplitude hits. Therefore, a test for determining the amplitude of the smallest locatable event and therate of locatable AE events (rate of hits) depending on the amplitude was required.
The intention was to use it as a standard tool in addition to pencil-lead breaks and auto-calibration.
Therefore, it must be possible to automate the test with standard components, and to extract the resultswith the standard software used in AE testing.

118

TEST FOR DETERMINING THE QUALITY PARAMETERS
A fully digital signal generator was used to generate well-defined, precisely repeatable electric pulses.
By using the second channel of the signal generator for triggering the pulses, sequences consisting of apredefined number of pulses with a predefined rate can be generated. A computer program running on a
PC was used to set the parameters of the signal generator, to transmit the parameters to the analog inputsof the AE-equipment (AMS3 from Vallen Systeme), and to trigger the sequence of pulses. The
generated events with the parameters were recorded at the AE equipment.
In one test, the amplitude and the rate of pulses were varied within predefined ranges, and one sequence
was executed with each parameter combination. The minimal amplitude for proper location and the
maximal rates for proper location depending on the amplitude are extracted from diagrams, which cansimply be drawn by means of the AE-software. With the used signal generator it is possible to control bysoftware a range of 40 dB in the amplitude without having too much noise. For reaching higher dynamic
in the amplitude, attenuators have to be used. The maximal amplitude of the signal generator, which is
10V, gives the maximum strength of the signal.
The piezoelectric source, which converts the electrical signal into a mechanical wave, and the coupling,which transfers the wave to the vessels, are the parts, which cause problems for the repeatability andcomparability of the test. Two options for selecting the piezoelectric source seem to be suitable: The
first is to select the same type as for the sensors and performing the tests similar to the reciprocity
calibration method [4] used for sensor calibration. The second method is to use a broad band reference
source. For the tests described in this paper, the second method was chosen, applying a V103 transducer
from Panametrics. Coupling was done in the same way as for the sensors.
In some tests, the piezoelectric source was coupled in the neighborhood of locations from were theinvestigated signals are expected to come from (region of the failure, crack etc. - here called failure
region). In other tests the piezoelectric source were coupled near locations from were the noise is
expected (noise location).
Waveform of the test signals (pulse)
It must be expected, that the spectrum of the test signal influences the result. For tests, which address theinfluence of the frequency, small band signals are used. In the case of the test described here, thevariation of the frequency was avoided and a broad band signal was used. Therefore, a signal wasconstructed with a spectrum of rectangular form from 0.1 to 1 MHz (Fig. 1). The signal is based on thecosine function, and the signal length is ten times the period of the lowest frequency content (equation
below). A phase shift was used to shift the maximum amplitude of the pulse to 0.25 times the signal
length and having zeros at the beginning and the end of the signal.

119

The test signal was digitized at 2048 points and transferred to the signal generator with a precision of 14
bits. The resulting frequency for the signal generator is 20.48MHz, large enough for this spectrum.

0.3

0.2
F

k
0.1

0
4
1 10

5

1 10

6

1 10
f
k

7

1 10

8

1 10

Fig. 1: Test signal (pulse) in time (top) and frequency (bottom) domain.
Numbers for characterizing the test results
The number of properly located events at one sequence (10 pulses) determines whether proper locationis possible for the used parameter combination of the sequence. The area in which an event must be120

located, i.e. is properly located, must be chosen according to the goal of the acoustic emission
measurements. In the experiments described below, a tolerance of ±10cm in x- and y-direction from thelocation of the source was specified. This is a rather large area for the investigated vessels, but in theplanned experiments it is only important that the events are located in the correct region. A filter wasused, to filter out the properly located events, and the number of properly located events is plottedversus the parameters, which are the amplitude and the rate (Fig. 2).
If ten events are properly located in one sequence (one parameter combination), the test is passed for thisparameter combination. In this plot, the minimal amplitude at source for proper location is the amplitudeof pulses at the utmost left clusters with 10 properly located events. The maximum rate for proper
location depending on the amplitude at source is the largest rate with 10 properly located events at thecorresponding amplitude. In the performed tests it seemed to be appropriate, to build a mean value, if fora parameter combination 8 or 9 events were properly located.
When AE-measurements are performed for monitoring cracks or failures only the amplitude at thereceiver is known. Therefore, for the interpretation of location quality, it is sometimes more useful to
use the amplitude at the receiver (Fig. 3). According to this diagram, similar numbers as above can beextracted with the amplitude of the recorded event instead of the amplitude at source.
A very good impression of the quality of the location calculations is given by the location plot y vs. x of
the located hits (Fig. 4) for all sequences performed in one test.

Fig. 2: Number of properly located hits versus amplitude at source and rate of pulses.

121Fig. 3: Number of properly located hits versus amplitude of event and rate of pulses.

Fig. 4: y versus x of located events at one test with the source at x=2 and y=26.

122

There are three additional numbers, which are considered for the evaluation:• Total number of events (located and not located ones) in the test.
• Total number of located events in the test
• Total number of properly located events in the test
Optimization of the acquisition parameters using the test above
There are two important acquisition parameters, which determine how the hits are built from theincoming signal. The threshold level determines the sensitivity of the arrangement. The time span forwhich the channel is blocked by one hit is determined by the rearm time together with the thresholdlevel.
To find a good combination of both parameters, the test described above was performed, for somecombination of the two parameters. The pulse amplitudes of the test ranged from 100 to 130dB in stepsof 6 dB and the rate of the pulses range from 2 to 128Hz increased from one step to the next by a factorof two. Therefore, 42 sequences á 10 pulses were used in one test.
The piezoelectric source was coupled near to an artificial notch, which is the expected failure region.
The most important location for noise was the nozzle for the pressurization. The piezoelectric source
was coupled to this region for another test. The results of some of the tests are listed in Table 1.
Table 1: Tests for optimization of the acquisition parameters.
Investigatedacquisition
parameters

Min. amplitude
for proper
location [dB]Max. rate for proper Max rate for proper
location [Hz]location [Hz]Amplitude at source: Ampl. of event:

Number Number Numberofofof
eventseventslocatedlocatedeventsin
failureregionThres Rearm-hold time[dB] [ms]source receiver

112dB

130dB 45 dB

63 dB

27.7 0.41

106

39

32

16

32

16

261

2050

261

27.7 1.18

106

39

64

16

32

12

266

1187

266

27.7 3.021

106

39

48

16

32

16

269

543

269

27.7 5.99

106

39

32

16

32

12

249

393

249

27.7 11.98

106

39

32

12

32

12

252

362

252

27.7 24.013 106

39

32

16

32

12

254

368

254

29.6 3.021

112

45

64

16

48

16

244

437

245

31.2 3.021

112

45

64

16

64

16

263

458

263

33.7 3.021

112

45

64

16

64

16

242

380

242

36.4 3.021

118

51

64/118Db 32

64/51dB 54

212

393

212

27.7 3.021

Test with source at noise location (near nozzle)

0

520

154

The results show a slight decrease of the maximal rates for proper location when the rearm time isincreased. An exception is the rearm time of 0.41ms, for which this rates are slightly decreased. Whenlooking at the number of events it can be seen that the number of events can be decreased from 2050 to
543 by increasing the rearm time to 3.021ms, without reducing the number of properly located events. A
123

further increase of the rearm time reduces the number of events further, but the number of located events
decreases also slightly and the maximum rates for proper location decrease also slightly. Increasing thethreshold levels leads, as expected, to a reduction of minimal amplitude for proper location, but also to
an increase of the maximal rate for proper location. Within these tests, it was not possible to decrease thethreshold to a level at which a serious decrease of the maximal rate for proper location occurs.
An additional test, with the source at the expected noise location, shows that events from that location
are not mapped into the failure region.
CONCLUSION
A test for the quality of AE arrangements, which focus on the location calculation, was developed. The
most important problems considering repeatability and comparability of the procedure are thepiezoelectric source and the coupling of the source to the vessel. The preparation of the surface, where
the source is coupled, is also important. In the case of the tests described here, a reference source wasused, and it was coupled to the vessel in the same way as the sensors were coupled. The problem, thatthe user must perform the test in relatively short time when preparing experiments, was solved by fully
automating the procedure, by controlling a digital signal generator with PC-software, and gaining theresulting indicators in a simple way from standard AE-diagrams. A limit for the automation of theprocedure is the limited dynamic range of the signal generator, which can be solved by inserting
attenuators manually into the cable. The maximum amplitude of the signal generator is another limit.
Indicators were developed, which quantify the weakest properly locatable event, and the maximum rateof locatable AE events, depending on the amplitude. With these indicators, it is possible to adjust thearrangement, acquisition and the location parameters and to test proper filters. The adjustment of theacquisition parameters with this test was shown.
REFERENCES[1] Rauscher F., Schwarz M.: Schallemissionsmessung an Druckbehältern mit fehlerhafter Längsnaht
bei zyklischer Belastung sowie bei der Druckprobe. Bericht Nr. 5, Technische Universität Wien: Instutut
für Apparate und Anlagenbau (1995)[2] Rauscher F.: Acoustic emission of vessels with partially penetrated longitudinal seams. International
Conference Acoustic Emission 1999 at Brno University of Technology, 15.-17.June 1999.
[3] Bayray M.: Acoustic Emission (AE) due to cyclic pressurisation of vessels with partially penetrated
longitudinal seams. European Conference on Acoustic Emission Testing 24-26 May 2000, Senlis
France.
[4] Nondestructive Testing Handbook, Vol. 5: Acoustic Emission Testing. American Society For
Nondestructive Testing, 1987.

124

MONITORING OF WELD'S DEFECTS EVOLUTION SUBMIT TO STATICAND DYNAMIC LOADING THANKS TO THE ACOUSTIC EMISSIONMETHOD
C. HERVE, R. PENSEC* and A. LAKSIMI*,CETIM, Senlis, France, *UTC, Centre de Recherche de Royallieu, Compiegne, France

ABSTRACT
Acoustic emission is a method especially suitable for the pressure vessels monitoring. The purpose ofthis study is to determine some patterns of flaw evolution via an original experimental approach. Staticand fatigue cycles have been combined in order to duplicate accurately the loading undergone by apressure vessel in service. Moreover, many parameters have been studied: different materials, several
kinds of flaws, different forms of flaw. The research allows us to understand microscopic evolutions ofsome defects, to determine the efficiency of the acoustic emission method applied to the flaw detection
according to the evolution state, and to evaluate the harmfulness of weld defects from their acoustic
emission during the successive proof test simulation.
INTRODUCTION
The regulation requires that the pressure vessels be subjected before any use to a proof test andperiodically every ten years thereafter. In general, this test consists of a hydrostatic pressure test. If thiskind of test allows one to check the ability of a vessel to withstand its maximum pressure service, it does
not allow to detect the presence of all the growing defects. Moreover, the Pressure Equipment Directive
97/23/CE [1] and the work of the TC54 (in particular the draft standard prEN 13445) introduce thepossibility to replace the hydrostatic test by another proof test, hydraulic or pneumatic. All this explains
the will of manufacturers and industrialists to lay out a method able to evaluate the vessel integrity
during the different proof tests and also to guarantee the safety of such a test especially when the test is apneumatic one.
The final objective of the research program is to replace the first proof test (hydrostatic or otherwise) or
to propose a method of vessel re-qualification by some in-service test, pneumatic or hydraulic,monitored by acoustic emission (AE). The first part of this program deals with tension tests on
specimens to study the behavior of welding defects subjected to a cycle of loading representative ofwhat a pressure vessel can bear during its life, from the point of view of their mechanical behavior andof their AE.
In the present case, to study:1) the fatigue behavior of one defect, a long and shallow notch;2) the abilities of detecting by AE the defect during the pressure vessel lifetime;3) the evaluation of the harmfulness of a defect by the AE, and4) the estimation of the life duration of a structure from the AE recorded during a proof test or
following tests.
EXPERIMENTAL SET UP
Specimens and defects features
125

The research relates to large-sized tensile specimens drawn from a 6-mm thickness sheet of a carbonmanganese steel, (C: 0.17%; Si: 0.02%; Mn: 0.7% and YS = 336 MPa; TS = 469 MPa). A weld seamhas been produced in the center of the sample by a TIG technique, and at its foot, a long and shallow
notch has been made by EDM.
The AE equipment
Four sensors, manufactured by CETIM and resonant at 180 kHz, are connected to an AMSY4 AE
acquisition system from Vallen Systeme. They form a linear mesh around the weld seams, the two
external sensors eliminating the noise coming from outside. The location of the events is very sharp andthe signals resulting from the weld area, with or without defect, can be located easily.
One of the main difficulties encountered during the AE survey of fatigue tests comes from the noise
generated by the hydraulic system of the fatigue machines since significant mechanical vibrations canmask the AE generated in the center of the test-piece by saturating the sensors. To avoid this problem, asystem of vibration insulation has been designed and set between the piston rods and the hydraulicwedge grips. This system allowed the detection threshold decrease from 55 to 34 dB.
Cycle of loading representative of a pressure vessels life
In order to simulate more precisely the flaw evolution during the life of a pressure vessel, a combinationof proof and fatigue tests has been applied to the specimens (Fig. 1).
Loadproof test 1

proof test 3

proof test 2
Fatigue 1

Fatigue 2

Fatigue 3
RuptureTime

Fig. 1 Combination of cycles of tests and tests in fatigue applied to the specimens.
During the cycle of loading representative of a proof test, all the loads are compared to Pc, the designpressure. The maximum of load reaches 1.5Pc, which is equivalent to 90% of the yield stress of the basematerial. The fatigue cycle is composed of sinusoidal oscillations at a 5-Hz frequency; the maximum of
the load corresponding to Pc. The ratio between the minimal and maximal loads is 0.2. The number ofcycles applied between two successive tests corresponds to a several daily uses of the vessel.
DEFECT BEHAVIOR DURING FATIGUE LOADING ONLY
Under only fatigue cycling, the life duration of the specimens is expected from 86000 to 93000 cycles.
The AE resulting from the defective zone is often masked during the fatigue cycles by a generalemission of the specimen related to the resonance of the fatigue test machine. The vibration insulation
system was unable to filter the significant vibration, and the signals of amplitude lower than 55 dB are
not detected. Some of the specimens tested show an amplification of the emission recorded around thedefective area to the two-thirds of the cycle. The rate of events and the average energy of the signals canincrease then temporarily on a few thousands of cycles in the middle of the cycle. However, for eachspecimen, a final increase of the AE, in a number of events and in energy, is observed in the lastthousands of cycles (Fig. 2).
126

Number ofcyclesNumber ofcyclesNumber of
eventsEnergyTime (s)Time (s)Fig. 2. Acoustic emission recorded on the level of the defective zone during cycling in fatigue;(a) Amplitude of the events. (b) Rate of true energy.
Correlation between the mechanical behavior and the recorded acoustic emission
The mechanical behavior of the defect is composed of three successive stages: the initiation, the stablepropagation and the final unstable propagation. The phenomenon of initiation features some ridges
regularly laid out on the defect’s initial boundary, which is synonymous with several starting points. The
boundary of the defect quickly becomes uniform and propagates in the form of a succession of small
striation and micro-cracks. Not very visible in the first half of the fatigue cycle, the striation becomesincreasingly clear as the speed of the crack propagation increases. During the last stages of the stablecracking, the face presents a tangle of striation and dimples, synonymous with the appearance of a static
tearing. Finally, the fatigue crack propagation becomes unstable, and a static fracture of the remaining
ligament happens.
The stable propagation at low speed is undetectable by AE under the test conditions. Only the last stageof the stable propagation is clearly identifiable. It results in a rate of events and a rate of energy much
more significant, the average amplitude of the signals remaining moderate. The small dimples thus do
not generate events of strong amplitude. Schematically, the propagation speed of the crack, the rate ofevents and/or the rate of energy vary in time in a similar way and show in particular a strong increaseduring the last thousands cycles. The increase of the acoustic emission observed on some specimens at
the two-thirds of the fatigue cycle is not related to a specific mark on the face of the crack.
DEFECT DETECTION BY AE DURING THE LIFE OF A PRESSURE VESSELDefect detection during the first proof test
Some previous studies [2,3] have shown that the long and shallow crack, submitted to a proof test,
generates few AE and that the majority of the emission appears mainly in the second part of the loading
cycle. However, different situations happen in the present case: Figure 3a shows a crack that generates asignificant AE, but whose detection is paradoxically late since few insignificant events are observed
until the load overtakes Pc. The detection of the defect is certain only during the last load rise towards
1.5Pc. Figure 3b shows on the other hand a specimen far less emissive on the whole of the cycle, andwhose events are not clearly indicative of the presence of a defect in the specimen.
In comparison with the behavior of a sound weld seam, the presence of a long and shallow crack in thevicinity of the weld is detected mainly by the amplitude of some of the recorded signals and by the
temporal density of the AE appearance. The aspect of the Kaiser effect and the recurrence of theemission are not in the present case some criteria, which could allow us to detect the presence of thecrack. Emission during holds is sometimes detected, but in a random way, which does not make itpossible to be a relevant criterion.
127

Amplitude (dB)

(a)

(a)

Amplitude (dB)Time (s)(b)

Time (s)Figure 3: AE amplitude recorded during the first cycle of loading applied to two specimens
containing a long and shallow crack.
Detection of the defect from one proof test to another
Several specimens containing a long and shallow crack at the foot of their weld seam are submitted to a
succession of proof and dynamic loading as described in Fig. 2.
In this situation, the fracture topography shows the following features (Fig. 4):! The first proof test is not visible.
! The post-proof test behavior is typically like overloads and generates plastic deformation at the crack
tip more significant in comparison with each previous fatigue cycle. The proof tests can be visually
located on the fracture topography by a ribbon of smooth and bright surface. The width of thisribbon increases with the propagation of the crack since, the deeper is the crack, the bigger is theplastic deformation created ahead of the crack for a same load. Some ribbons are also accompaniedby small dimples.
! Between the marks of the proof tests, the fatigue surface described in the first paragraph is observed.
The width of these areas increases in time, whereas the number of cycles between the tests remains
constant, thus representing the increase of the propagation speed of the crack in time.
The AE recorded during the second and the third successive proof tests undergone by the same specimen
is described in Fig. 5 below (the first proof test of this specimen is related in the Fig. 3a).
Fatigue propagation

Third proof test

Initial defect geometry

Fig. 4 Schematic fracture topography of a specimen which has undergone a succession of proof
tests and fatigue loading.
The maximum of load reached during the fatigue cycling is equivalent to Pc, whereas it reaches 1.5Pc
during the different proof tests. The AE increases from a proof test to another since it is connectedwithout any ambiguity to the volume of plastic deformation created during the overload. An increase, inquantity more than in quality, of the AE recorded from one test to the following is clearly correlated tothe presence of a defect. However, as shown in the following paragraph, every defect is not announced

128

Amplitude (dB)(a)

Amplitude (dB)(b)

Time (s)Time (s)Fig. 5 Evolution of the acoustic emission (event amplitude) recorded during the second and thethird proof tests undergone by a specimen (the first one corresponds to the Fig. 3a).
systematically by an increase in its AE from one test to another. That means that the comparison of the
AE recorded from one test to another does not always make it possible to detect a growing defect.
During the second proof test, some AE can be recorded exclusively between Pc and 1.5Pc, which meansthat the Kaiser effect can be observed whereas the defect propagates itself significantly during theprevious fatigue loading (Fig. 5a). Some emission on the two holds at 1.5Pc exist and, even if only twoevents appear clearly in the middle of these two stages, the emission observed on a hold is a best
criterion for defect detection than the Kaiser effect can be. The final static fracture of the specimen
happens during the third proof test at 1.1Pc. Among the many tests performed, this is the only one where
the Kaiser effect has not been observed (Fig. 5b).
The quantity of AE recorded during a test is thus in direct relationship to the level of damage undergone
by the specimen and depends on the stage of evolution of the defect.
ESTIMATION OF A DEFECT HAMFULNESS BY ACOUSTIC EMISSION
Three specimens, containing the same long and shallow crack are subjected to the same cycle of loadingof Fig. 2. Although the defects are similar, the life duration of the third specimen is quite higher than theother two (+30%). Figure 6 represents below the number of events found during the different proof tests
for each specimen.

Nb of Events
100%

50%

proof test 1(initial)

proof test 2

proof test 3

1 2 3
Time

Fig. 6 Comparison of the number of events emitted in the defective area by three specimens during
the three proof tests.
129During the first proof test, the acoustic emission of the three specimens is relatively similar. After the
first fatigue loading, the AE recorded during the second proof test is quite different among the three
specimens. The specimen 1 is very emissive, and generates four times more events than during the first
test. The specimen 2 shows a significant but limited growth in its AE. For the third specimen, the AE
recorded during this second test is less than during the first one.
Compared to the evolution of the recorded emission from the first to the second, specimen 1 will breakduring the second fatigue test. The specimen 2 will complete its second fatigue load but will breakduring the third proof test and only the specimen 3 will bear the third proof test and will undergo a goodpart of a third fatigue test before breaking.
It is not thus possible to deduce the fatigue life-time of a specimen according to the AE, which it emits
during the first proof test. The quantity of AE (in number of events or in energy) recorded during thesecond test appears to correlate to the life-time of the specimens if it is compared with the emission of
the first test. The harmfulness of the defects is absolutely not quantifiable by the AE recorded at the timeof the first test. Only the comparison of the acoustic emissions recorded during tests 1 and 2 gives a realindication of the tendency to the growth of a defect and thus of its harmfulness. Some defects being
propagated significantly in fatigue cannot be detected during a proof test.
CONCLUSION
By combining two kinds of loading, the experimental setup makes it possible to understand the realevolution of the defect during the life of a pressure vessel. The tests show that the AE emitted is directly
correlated to the evolution stage of the defects. There is then a real relation between AE and the damageof the structures. The first test does not make it possible to have an idea of the harmfulness of thedefects. On the other hand, the AE recorded during the second test is directly in correlation to thenumber of cycles borne by the structures up to the fracture. Furthermore, it is the comparison of theappearance of the AE between the two tests, which makes it possible to give an idea of the tendency to
the growth of the defects and thus of their harmfulness.
REFERENCES[1] European directive 97/23/CE, "Parliament and Council’s directive related to the adjustment of thedifferent European States’ regulations, concerning the pressure vessels", 1997.
[2] R. PENSEC, M. CHERFAOUI, C. HERVE and A. LAKSIMI, “Monitoring of defects evolution inpressure vessels. Application to the Improvement of the AE detection criteria”, AES99, p. 187, Brno,
1999.
[3] R. PENSEC, M. CHERFAOUI, C. HERVE and A. LAKSIMI, “Discrimination of flaws evolution
patterns during pressure vessels static and dynamic cycles of loading thanks to the acoustic emission
method”, QNDE 99, Montreal, 1999.

130

ACOUSTIC EMISSION (AE) DUE TO CYCLIC PRESSURISATION OF
VESSELS WITH PARTIALLY PENETRATED LONGITUDINAL SEAMS
Mulu BAYRAYVienna University of Technology, Vienna, Austria
ABSTRACT
Experiments have been conducted at the Institute of Pressure Vessel and Plant Technology on a series ofvessels with partially penetrated longitudinal seams. A pressure test and subsequent cyclicpressurisation were carried out on the vessels. AE statistical analysis of the pressure tests was reported
in the previous EWGAE meeting. In this paper the AE signal due to the cyclic pressurisation of thevessels will be discussed. The aim of the paper is to present the experimental results obtained ininvestigating the crack growth using the AE data. Possible sources of noise during data acquisition wereidentified and ways of filtering the noise implemented. The AE data that is expected to be from the crack
growth was then analysed. Results of the data analyses indicate that the AE intensity (e.g. cumulative
counts) increases rapidly before burst. The AE data prior to burst are in agreement with crack growthmeasurement using strain gauges. It is also observed that the AE events occur mostly during theincreasing pressure phase, well before reaching the maximum pressure.
INTRODUCTION
Experiments have been conducted at the Institute of Pressure Vessel and Plant Technology on a series ofvessels with partially penetrated longitudinal seams [1]. AE statistical data analysis of the pressure test
part of the experiments was reported in [2]. This current paper deals with AE data analysis due to cyclicpressurisation on vessels of series BC (16 vessels BC02-BC18 excluding BC07).
The objective of this paper is to present the AE data analyses during the cyclic pressurisation; especially,
to investigate possible sources of noise and establish ways of filtering noise, to investigate the AE events
due to fatigue crack growth and to investigate the possibility of monitoring the fatigue crack growth.
The experimental procedure will be briefly described in the first section. The second section deals withthe presentation of results and discussions. Finally concluding remarks will be forwarded.
EXPERIMENTAL PROCEDURE
The description of the experimental set-up, test vessels and procedure are discussed in [1-2]. The
experimental procedure will be briefly discussed here for convenience. The vessels with outer diameter
351 mm, overall length 600 mm, and thickness 3.5 mm were made of St52-3 DIN 17100 base material.
The vessels were manufactured with intentional lack of penetration on the longitudinal weld. The lack ofpenetration was on the inside (root) of the one-sided weld and was about 240 mm long. The external131

weld excess of the longitudinal weld was ground off. This region was the expected failure area during
the loading of the vessels under internal pressure.
The general procedure of the experiment was to conduct a pressure test followed by cyclic pressurisation
until failure. The pressure test was carried out as near as possible to the burst pressure and wasmonitored using strain as break-off criteria (Pressure at break-off, Pbo , was at 0.2% mean strain over 20
mm gauge length for BC02 and 0.5% for the rest). Then the vessel was cycled, sinusoidal at frequencyof 1 Hz, for the first 10,000 cycles with Pbo /2.45 as maximum pressure. For vessels surviving 10,000
cycles, the maximum pressure was increased and cycling continued up to the next 10,000 cycles. This
procedure was repeated until failure. AE and strain gauge measurements were carried out during boththe pressure test and cyclic pressurisation.
Five sensors (four 150 kHz resonant and one 900 kHz wideband) with Vallen Systeme AMS3
instrumentation were used to acquire the AE data. Preamplifier gain of 34 dB and threshold of 27 dB
with 6 dB crest factor was utilised during the data acquisition.
EXPERIMENTAL RESULTS AND DISCUSSION
Calibration
The auto-calibration option of the AE instrumentation was utilised to calibrate the sensors. Difference insensitivity among sensors was obtained by comparing two sensors and their response when one is used
as a calibration input and the other as a receiver, and vice versa. An average and the maximum
difference in sensitivity in the array of sensors were used as comparison criteria. Comparing thecalibration data of all the vessels, the variations were not far from each other. Also, the averagesensitivity of the sensors during the tests were in the range of the ASTM recommendation of ±4 dB [3].
Thus, data collected during the respective cyclic pressurisation can be assumed as acquired at similarsensor conditions.
Signal – Noise Discrimination
The possible sources of noise identified were due to electrical interference (EMI), pressurisation noise at
inlet/outlet, friction at supporting brackets, and noise from circumferential welds (juggle joints). A large
amount of data was acquired during the experiment, so that investigation and classification of the sourceof noise was difficult. Thus, we utilised methods of filtering and extracting the useful AE (due to thedamage/failure). The filtering methods were: (1) Frequency filter: data was already acquired with highpass filter of 100 kHz integral with the preamplifier. (2) Amplitude filter: amplitude filter of 40 dB wasused to suppress low-amplitude hits, which cannot be properly located. (3) Duration filter: hits having
less than 30 µs were filtered out to suppress any short duration electrical interference. (4) Location filter:
after location of events using the above filters a filter which considers only events from the expected
failing region was utilised. The filter was between -5 < X < 5 and 10 < Y < 40 (cm), which describes the
expected failing region. The data extracted using the noise filtering methods outlined above wasconsidered for further analyses.

132

Table 1 below compares number of events extracted as useful data and total number of events acquired
employing the first three filtering methods described above. The amount of useful data varies from one
vessel to another, but was less than 8 % of the total amount of data acquired.
Table 1 Extracted events and total number of events acquired.
VesselTotal no. of events

Events located inside(located and not located)

expected region

%
total

BC03

12,687

118

1.0

BC16

27,040

448

1.7

BC08

25,056

709

2.8

BC11

16,571

1,204

7.3

Cumulative Counts versus Number of Cycles
In order to examine the history of the AE hits, cumulative counts and cumulative energy of all first hit
channels were investigated (AE data discussed in the coming sections will be based on all first hit
channels of the four 150 kHz sensors). In most cases, the cumulative counts and cumulative energy were
found to have similar trend. In further discussions, only the cumulative counts will be considered.
However, similar discussions could be made on the cumulative energy data.
1.0E+07
1.0E+06
1.0E+05

BC03
BC06
BC09
BC13
BC15
BC16
BC18

CC

1.0E+04
1.0E+03
1.0E+02
1.0E+01
1.0E+00
0

4000

8000

12000

16000

20000

24000

N (Cycles)

Fig. 1 Cumulative counts versus number of cycles: Group I.
Figures 1 to 3 show the AE activity in terms of cumulative counts (CC in logarithmic scale) throughout
the life of the vessels (N). The plots are shown for the vessels in group for simplicity of viewing. The
plots have in general three distinctive phases. The first phase, at the beginning of the fatigue cycles, ischaracterised by few events and few counts. The second phase, mid and major portion of the fatigue
cycles, is characterised by low or no AE activity in almost all vessels. The third phase, few cycles before
failure, is characterised by high rate of AE activity and hence the cumulative counts rose within

133

1.0E+07
1.0E+06
1.0E+05
BC08
BC05
BC02
BC12

CC

1.0E+04
1.0E+03
1.0E+02
1.0E+01
1.0E+00
0

4000

8000

12000

16000

20000

24000

N (Cycles)

Fig. 2 Cumulative counts versus number of cycles: Group II.
1.0E+07

1.0E+06

1.0E+05
BC17
BC14
BC11
BC04
BC10

CC

1.0E+04

1.0E+03

1.0E+02

1.0E+01

1.0E+00
0

4000

8000

12000

16000

20000

24000

N (Cycles)

Fig. 3 Cumulative counts versus number of cycles: Group III.
relatively few numbers of cycles. The sudden rise of AE cumulative counts was observed to happen
after about 85 – 95 % of the total cyclic life of the vessel.
Number of Counts versus Amplitude
To look into the similarity and difference in characteristics of events in each vessel, the number ofcounts with the respective amplitude of hits were investigated. The plots of number of counts versus
amplitude for vessels BC06 and BC09 are shown in Fig. 4.
It can be noted that the plot for BC06 shows a good linear relation between the number of counts and theamplitude. A trend line, including its regression function and coefficient of determination (R2 ), is shownin Fig. 4a. Such a trend is observed in most vessels although the scatter was variable form one vessel toanother. A linear relationship between the number of counts and the amplitude (in dB scale) means an
exponentially decaying signal. The plot for BC09 (also for BC08) shows a number of events with highnumber of counts ranging from low to high amplitudes (Fig. 4b). This means no relation between the134number of counts and the amplitude of the hits. This is expected from leak-type AE events, which arecharacterised by continuous emission. Hits of such type, whether low amplitude or high amplitude, havehigh number of counts. The last few cycles of vessel BC09 were, therefore, leak-type hits. In fact, whennumerical filtering was used to suppress the last few cycles, linear tendency is obtained. The rms voltage
level was also high during the last few cycles, and it confirms the existence of leakage before burst.
14000

24000
22000

12000

20000

y = 313.24x - 13142
R2 = 0.8347

18000

10000
16000
14000
CountsCounts

8000

6000

12000
10000
8000

4000
6000
4000

2000

2000
0

0
0

10

20

30

40

50

60

70

80

90

0

10

20

30

40

50

60

70

80

90

A(dB)

A (dB)a) Vessel BC06.
b) Vessel BC09.
Fig. 4 Number of counts versus amplitude.
Comparison between AE Cumulative Counts and Strain Gauge Data
The cyclic pressurisation was also monitored using strain gauges attached close to the failure region.
The strain gauge instrumentation was capable of recording data online. The AE cumulative counts datacompared with strain gauge measurement data show similar trend. The strain gauge data recorded highrate of increase at the same time when the AE recorded high rate of counts.
Occurrence of Events with Respect to Phase of Load Cycle
During the data acquisition, it was observed that most AE hits occur at lower pressure than at the
maximum pressure of the cyclic load. The cyclic pressurisation was sinusoidal type of loading (phase 0– 3600 ) alternating from minimum value (00 phase) just above 0 bar up to the maximum value (1800
phase) and back to minimum value (3600 phase), see insert of Fig. 5a. Utilising the cycle slope flag of
the AE system and the pressure (parametric input), the occurrence of the events with respect to the phaseof loading cycle was determined.
In most cases, the majority of events occurred during the phase where the load was increasing. Few
events occurred near the maximum pressure and during the phase where the load was decreasing (withexceptions outlined in the next section). The AE data can be analysed by looking at the phase, at which
the events were recorded as the number of cycles increases until burst. Figure 5 is such a plot for vessels
BC06 and BC08. On most of the vessels, the following can be observed. (1) The majority of the eventswere recorded in the range 30-900 phase in most of the vessels. This means that those events were135

recorded during the increasing pressure phase. After such events are observed, most vessels failed withina small number of cycles (400-3800 cycles). (2) In most of the vessels, the phase at which the eventswere recorded decreases as the number of cycles gets closer to burst. (3) In most vessels, a number of
events were recorded around the maximum pressure (1800 phase). (4) Events were also recorded
randomly on the increasing and decreasing phase of loading at the cycles close to final burst.

360
330
1.00
p/pmax300
270

Phase ( 0)

240

0.75
0.50
0.25
0.00
0

210

90

180

270

360

Phase (0)

180
150
120
90
60
30
0
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

24000

16000

18000

20000

22000

24000

N (Cycles)a) Vessel BC06.

360
330
300
270

Phase (0)

240
210
180
150
120
9 0
6 0
3 0
0
0

2 0 0 04 0 0 06 0 0 08 0 0 0

10000

12000

14000

N (Cycles)b) Vessel BC08.

Fig. 5 Distribution of event occurrence with phase and number of cycles.
Exceptions to the above observation:(a) Hits with low amplitude and low number of counts were recorded in vessels BC06, BC11, BC14 andBC18. Those hits can be identified on the amplitude versus phase and counts versus phase plots. The
occurrence of the events was irregular and the source of the events might be noise.
(b) Hits with varying amplitude and number of counts were recorded at early cycles in vessels BC08
(see Fig. 5) and BC10. The events were recorded near the maximum pressure (1800 phase), and in thedecreasing pressure phase towards the minimum (300-3600 phase). The rms voltage level during thistime was higher than the normal background noise level. The source of the events was not identified.
136

(c) Even though in most vessels events near burst were recorded throughout the load cycle, vessels
BC08 and BC09 (see Fig. 4b) recorded abnormally many events. The hits were with high number ofcounts irrespective of the amplitude. Those events were due to leak just before burst.
CONCLUSION
The signal– noise filtering method has extracted useful AE data due to crack growth. The exponentiallydecaying type of hits observed and confirmation of crack growth through strain gage measurement
imply that the majority of the extracted data was from crack growth. However, the percentage of usefuldata as compared to the total amount of data recorded during the experiments was too small.
Improvement in the percentage of useful data is expected by optimising the acquisition parameters.
Studies in progress of improving the location of events on AE experiments of such small vessels isreported in [4].
The AE cumulative counts data showed that the growth of the crack could be monitored in most of thevessels. The cumulative counts increased at higher rates during crack growth, which could be seen on
the plot of cumulative counts versus number of cycles. However, the number of cycles from the crack
growth phase before failure was variable from one vessel to another.
Most of the AE events just prior to failure were found to be at the increasing phase in the cycle but
lower than the maximum pressure. Though events were also recorded near the maximum pressure, theevents at low pressure show that load gating may result in missing data related to crack growth. Further
study would be necessary to investigate this observation and to look at utilising it as means ofmonitoring the crack growth.
Based on the AE data analysed, it seems that in the vessels considered here, no crack growth wasobserved until 10,000 cycles.
REFERENCES[1] Project Report, “ Pressure Vessels: The determination of the relation between the fatigue strengthand hydro test pressure,” Vienna University of Technology, Institute of Pressure Vessels and Plant
Technology, unpublished.
[2] Rauscher, F., “Acoustic emission of vessels with partially penetrated longitudinal seams,”
Proceeding of the EWGAE conference AE’99, Brno Czech Republic, June, 1999.
[3] ASTM Standard, ASTM E 976.
[4] Rauscher, F., “Optimisation of acquisition parameters for acoustic emission measurements on smallpressure vessels,” Proceedings of the 24th European Conference on Acoustic Emission TestingEWGAE 2000, Senlis, France, May, 2000.

137

INSPECTION OF LPG VESSELS WITH AE EXAMINATION
P. TSCHELIESNIG and G. SCHAURITSCH
TÜV Austria, Wien, Austria
ABSTRACT
Since 1992 the required main inspection can be replaced by a pneumatic loading monitored withacoustic emission (AE). The data evaluation, which started with ASTM Standards, has to be changed,
because new results showed that the specific kind of defects in juggle joints and all disturb noise makean active evaluation method inappropriate. An evaluation method based on the different AE
characteristics, combined in a Cluster Evaluation Factor (CEF) and implemented in the on-line
monitoring, gives us and in specific the test inspector the opportunity to detect the criticality of a vessel
very early and stop the test without any problem for the environment.
INTRODUCTION
It’s obvious, that today gas is beside crude oil the most important basis for the production of energy.
Because the establishment of the necessary network of transportation lines for natural gas is expensive
the common alternative is the use of liquefied petroleum gas (LPG). This LPG is stored at the end-user,
often private houses or medium and small enterprises, in small storage tanks up to 15 m3. Since thesestorage tanks (pressure vessels) have to be inspected before they go into service and periodically during
their lifetime, we found defective storage tanks, which lead, due to defects in the welds, to the escape of
LPG even during the normal service period. These defects occurred in the circumferential welds (juggle
joints) caused by stress corrosion cracking (SCC). The Technical Inspection Hessen (BRD) investigatedthese phenomena and discovered that the reason for it has been the filling of these vessels with methanol
during storage before they went into service. Because the TÜV Austria has experience with AE-testingof these types of vessels since 1992 and has tested and investigated similar defects, which occurred in
France in 1995/96, it was invited to help devise a proper testing method for these specific defects.
BASEMENT AND DEMANDS ON THE TECHNIQUE
Based on the experience with the specific type of defects we have had the following cracks in the jugglejoints, the heat-affected zone and even in the remaining overlapping part of the material.

Fig. 1: Specific defects in the juggle-joint.
138

Due to technical and economical reasons, a planar location system is inapplicable, especially for those
vessels, which are buried. These buried vessels are only accessible within the “dome” region, which aresituated the man-hole (e.g. Germany, Switzerland and Austria) or only through connection-lines (e.g.
France). On the other hand, the application of a one-sensor technique was improper based on the
shadowing effect and the high attenuation of the buried vessels, because one sensor could not possibly
inspect an area of up to 30 m2. Based on the high noise level coming from the environment and thepressurisation device, the TÜV Austria used for vessels up to 5 m3 a two-channel system with a linear
correlation technique.
The loading during the tests were performed up to 1.1 times of the design pressure with the stock
product (LPG) itself. During this measurement the activity of the AE was controlled, but also theintensity was evaluated.
Because AE can only detect those defects, which become active during the stimulation, it was clear thatthe defects #3 and 4 (see Fig. 1) cannot be detected by AE. However, these cracks were not dangerous
for the vessels, because these parts of the vessel will not be stressed during the test or the normal
service. The most dangerous defects for the remaining lifetime of the structure are the cracks #1 and 2.
EXPERIENCES WITH THE AE TESTSFrom 1997 till 1999, 39 vessels from these production lots, where these specific types of defects could
be expected, were tested in Germany. Of those 54 % was flagged by AE and has to be re-tested by other
NDT-methods. During this test-period one of the vessels became leaky at 1.61 MPa. These specificresults will be discussed later.
In Austria we tested during the same time period 75 vessels of the same series and 16 % of those vessels
with the most significant defects were taken out of service and under-went re-tests by other NDTmethods. In this case no cracks were found, but unacceptable welding defects (e.g. lacks of fusion) weredetected.
The difference between the vessels in Germany and Austria has been the drying of the vessels inGermany. The formic acid, produced from methanol and water, appeared in the vessels in a higher
concentration in German vessels. In Austrian vessels, the acid was diluted due to the higher amount ofresidual water. But in both test series we found the same production defects and those initiated the SCC
in combination with the specific geometry of the juggle-joints.
AE-TEST RESULTS
The most interesting example was an LPG tank, which has a storage capacity of 2.3 ton, with thediameter of 1.6 m. The material was StE 355 with a wall thickness of 5.6 mm. The tank was situatedunderground and protected with epoxy-resin against corrosion.
The loading was performed by the evaporation of the liquefied gas from the working pressure of 850
kPa and was controlled with AE. At 1.5 MPa, the safety relief valve started to open and was blocked.
After this the pressurisation was continued. At 1.58 MPa the test-inspector detected a high AE activity
139

and stopped the test. A crack (type 1 defect) was unstable critically at this stage and de-pressurisation
was too slow, the tank became leaky. Most of the gas could be pumped back in the service truck, but thearea around the vessel has to be blocked till the gas was diluted under the explosion barrier.
A post-test AE-data evaluation indicated that the crack propagation, which was initiated at a very low
pressure, could be detected at 1.35 MPa according the AE activity. But this didn’t show a large increaseof the rate. Much clear increase of the AE intensity occurred. See Fig. 2. A few events with a highamplitude and energy, which consequently also resulted in a rise for the ring-down counts (with event
duration of over 100 ms).

Fig. 2: AE-graphs starting from 1.2 MPa.

A more detailed evaluation of the data starting from the blocking of the safety relief valve showed thattill 1.58 MPa only a smooth increase of the hit- and count-rate was found (Fig. 3). A few high energy
events with consequently also high amplitudes led to a rapid increase in the energy and also count rate
above 1.58 MPa. At 1.61 MPa the crack became critical and the pressure relief followed couldn’t stop
the crack propagation.
After the test, the tank was dug out and we found a 40-cm long crack of type 1 caused by stresscorrosion cracking in the circumferential weld in the 6 o’clock position. The welding showed a massive
pre-damage and the residual wall-thickness of 1 mm fractured spontaneously by the loading.

140Fig. 3: AE-graphs for the last period (1.50 MPa till 1.61 MPa).

Such a big crack, which penetrated through more than 90 % of wall before the loading, showed in the
AE behaviour no increase in the AE activity rate until the crack started to propagate. This is caused inthis specific case by the juggle-joint geometry. This kind of welding produces a lot of interference noisedue to remaining slag and other metallic particles in the over-lapping part of the weld, while any
extension of the weld in circumferential direction of the weld is extremely restricted. Because the cracks
are open from the beginning, no AE from crack opening movement could be detected.
From 1.5 MPa till 1.58 MPa, a few but very energetic signals could be detected. These were produced
by crack propagation. This crack can be detected with AE and shall lead to a safe “stop” of the test. To
take this into account, a specific factor, which gives us a possibility to classify a tank or even a specificarea quantitatively in the post analysis, has to be obtained. This “Cluster Evaluation Factor (CEF)”
refers to the specific characteristics of the AE-signals for the pre-selected cluster produced by acorrelation between the two sensors used. Due to the small sensor-to-sensor distance, a definite location
is impossible, but this time correlation gives us the possibility to filter out the noise and to weight thefactor for a specific area. Under this presupposition, that is, for this type of vessels the defects arealways in the same region, we can recalculate the energy of the event to the place of its origin or takethis into account for the threshold of this factor. This evaluation factor is calculated according to thefollowing formula:
CEF =

1 z
∑f
z n=1 p n
141

where fp is weighting-factor for the different AE characteristics, pn is used AE characteristic (e.g.
nmaximum amplitude, true energy) and z is number of used signal characteristics
This formula gives us a quantitative expression about the specific cluster of the test object. This factor
will be implemented into the testing procedure as an alert-value and/or a stop-criteria and will be used
on-line during the pressurisation.
In the example shown (Fig. 4), the CEF demonstrates the criticality of the vessel at 1.16 MPa, which
gives enough time to stop the loading.

Fig. 4: Example of CEF.

Beside a clear test procedure, the previous feasibility check and a clear value for the CEF, the test
inspector needs not to weight the different AE signatures of the vessel and gains therefore the necessary
reaction-time to stop the pneumatic loading in time. This is an absolute requirement for this kind oftests.
CONCLUSION
Since 1992 the mandatory inspection (hydrotest and visual inside inspection) can be replaced by apneumatic test monitored with acoustic emission. The data evaluation started at the beginning has to bedone according the ASTM E 569-82 “Standard Practice for Acoustic Emission Monitoring of Structures
During Controlled Stimulation”. This requirement is still valid for the accreditation and bring also a142

sufficient result and pre-warning time, if only stable crack propagation appears. If you also have
unstable, critical crack propagation, it is absolute necessary to add to this standard a specific test
modification.
This test modification has to be adapted on the problems of juggle-joints as circumferential welds. These
welds have specific defects, caused by stress corrosion cracking, but the AE behaviour is also distortedby the hindered elongation and the noise. Even though the structure produces enough AE for the prewarning, the number of AE events is small and the AE test-crew must be highly alert during the pressure
test.
For the on-line monitoring, one needs a pre-evaluated factor, which gives a simple quantitative value ofthe safety of the structure. The TÜV Austria used a specific cluster evaluation factor, which gives us theopportunity to include all necessary AE characteristics into a quantitative guideline to stop or continue
the test.
REFERENCES
1. P. Tscheliesnig, G. Schauritsch, G. Krenn; „Die Anwendung der Schallemissionsprüfung bei derwiederkehrenden Untersuchung von Flüssiggasbehältern“, TÜV Österreich (former Wien), presented at
the 9. Kolloquium „Schallemission“ of the DGZfP in Zittau, 1992.
2. P. Tscheliesnig, G. Schauritsch; „Erfahrung und Weiterentwicklung bei der Prüfung von kleinen
Lagerbehältern mittels der Schallemissionsprüfung“, TÜV Österreich, presented at the 11. Kolloquium„Schallemission“ of the DGZfP in Jena, 1997.
3. J.M. Capreau (Institute de Soudure), E. Eberhard (Butagaz), G. Schauritsch, P. Tscheliesnig (TÜVAustria), „Testing LPG Tanks By Acoustic Emission“, presented at the 23rd European Conference on
Acoustic Emission Testing in Vienna, 1998.
4. P. Tscheliesnig (TÜV Austria), J. Liska (Skoda JS), “The testing of LPG vessels with Acoustic
Emission Examination“, presented at the International Conference of Acoustic Emission in Brno (CS),
1999.

143

INSPECTION OF PRESSURE VESSELS USED IN REFRIGERATION AND AIR
CONDITIONNING SYSTEMS
Alain SKRABER, Fan ZHANG, Mohammed CHERFAOUI and Laurent LEGIN*
CETIM, Senlis, France and *TRANE
ABSTRACTThe survey consists in developing a monitored method, which uses the acoustic emission technique,allowing the overall quality of the structure to be assessed during testing while warranting thereliability of such a test when conducted pneumatically. The results given in this paper refer to tensile
test runs on specimens and in-laboratory pressurization tests on refrigeration capacities. These tests
have proven the fitness of acoustic emissions to detect growing defects on tanks made of a material forwhich the production processes (chemical pickling, shot-blasting, etc.) allow the surface oxide layer
resulting from rolling to be eliminated. A space-time criterion allowing the discrimination of the signals
representative of changes in a crack during the testing cycle has been developed.
THE INDUSTRIAL PROBLEM
In France, the statutory hydraulic test that serves for qualification purposes is advantageous in that itallows one to ensure that the vessel will withstand the maximum operating pressure. But it is costly as it
involves the use of the whole plant and careful, lengthy cleaning operations even though it provides only
a few pieces of information on the structural condition. The major drawbacks of the hydraulic test
without any particular control are to make the defects vary without detecting them and to adversely
affect the capacity performance owing to a residual humidity. The effects may alter the vessel life spanand lead to extra costs.
Some mechanical industries especially those dealing with refrigeration equipment have been very muchinterested in the development of a method allowing the overall quality of the structure to be assessed
during testing while warranting the reliability of such a test when conducted pneumatically.
Establishing a monitoring method would allow industrialists:• to cope with the impossibility of using cryogenic fluids R11 and R113,• to assess the overall quality of the structure,• to eliminate any corrosion hazards,• to reduce the lockout time during statutory re-testing with the possibility of making routine checking
less frequent,• to gain the experience required to adapt themselves to ever changing European standards and rules.
OBJECTIVESThe survey consists in developing a monitoring method, which uses the acoustic emission technique andhas two objectives, namely to assess the overall quality of the vessels while ensuring the reliability ofpneumatic pressure tests.
The monitoring method would allow one to decide whether the vessel pressurization should becontinued or stopped from the moment when any growing defect of a hazardous nature has been reliably
detected and located in real time.
144

The results given only concern a portion of the project that consists in proving the method capability tomeet this two-fold objective and in defining its limits and performances.
APPROACH
At the end of various field test runs, it has first been proven that integrating an acoustic emission control
system into a production line capable to adapt itself to environmental perturbations and production
requirements was feasible.
A series of tensile tests on welded specimens has established a database representative of the behavior of
the various welding defects. Tests aimed at adapting criteria obtained during the tests conducted onwelded specimens have been carried out on pressure vessels of different size, made from different
materials and of which the surface condition varied. It has, however, been impossible to directlytranspose the criteria determined from welded specimen tests owing to the specific behavior of thevarious material surface conditions. The behavior differences will be explained by complementary tests
conducted on «model» capacities and on specimens. These tests are described in the test result
presentation.
The portion of the project that consists in defining a testing procedure and the associated software for
automatic release of safety alarms is on the way. The results given in this paper refer to complementarytensile test runs on specimens and in-laboratory pressurization tests on capacities, which have allowed
the method performances and limits to be evaluated.
TESTING CONDITIONSGeneral characteristics
The testing conditions described in Table 1 have been defined for in-laboratory tests conducted on
cylindrical "model" capacities, upon which the analysis results described below are based. The tablesummarizes the main characteristics of tested capacities and of defects obtained by electro-discharge,
and the test pressures (Pe = 1.5 Ps) reached.
Table 1: Testing conditions
Geometry(mm)Nbofcapacities

dia.

thck.

lg.

400

6

1*600

7

168
168
600

3
4
6

1*280
1*185
2*260

12
22
3

length(mm)Nbofdefects
percapacity

60

100

1 or 25080
30

10; 30; 50
50
50

1
1
6

Testpressure

Defects:electro-erosion

Testing cycle
The testing cycle represented on Fig. 1 consists of successive rises and plateaus. The vessel is subjected
to pressure variations throughout the testing cycle twice before linear pressure loading takes place anduntil a burst occurs.
Sensor layout
In most cases, the device used on all the tanks includes eight sensors, two of which are located on the145

P (% Pe)

100
80
60

40
20

T (mn)

Fig. 1: Testing cycle

2

2
7

6

3

1

6

8

5

4

1

2

3

1

Fig. 2: Sensor layout on a developed view of the tank.
vessel bottom. The other six are set in two rows of three sensors each equally spaced so that the notches
fall within an equilateral triangle as seen on Fig. 2.
Instrumentation
The acquisition chain used is a VALLEN multi-channel system for characterization and localization of
acoustic emission sources. The sensors are of the 180 kHz resonant type. Preamplifiers with a gain of 34
dB and a high-pass filtering at 20 kHz are located between the sensors and the acquisition system.
The acquisition system ensures the following functions in real time:- signal localization- threshold acquisition of impulse signal shape parameters (amplitude, energy, number of bursts,
number of alternations, duration, rising time, frequency),- periodical acquisition of continuous emission and of tensile test effort
TEST RESULTS
Correlation between specimen testing and capacity pressurization tests
Specimen testing has been initiated so as to be able to understand the problems arising from thetransposition of criteria obtained through welded specimen tests to capacity pressurization tests.
146

Specimen testing with (and without) any rectification of their surface prove that the oxide layer which
covers some capacities delivers many acoustic emission signals while it is cracking (Figs. 3 and 4).
The tensile test on specimens and the capacity pressurization test have both shown that the start of oxide
layer cracking was fundamentally different:-

tensile tests on specimens show that specific phenomena develops only when the yield strength
has been exceeded (see Fig. 4: only a very small burst emission located on the specimen usable
portion: between 2 and 11 mm in the resilient domain);-during a capacity pressurization test, the oxide layer cracking occurs much before the yieldstrength is reached (see Fig. 5).
This could explain the difficulties met with the transposition of criteria determined from welded
specimen testing.
Capacity testing - Signal analysis
The analysis methods developed from various data processing operations (classifying, discrimination,neuron networks, etc.) do not allow growing defect signals to be discriminated from the many othersignals generated during testing. As all these methods use the same data: shape parameters (duration,
amplitude…), one can deduce that the oxide layer cracking signals and defect variation signals cannot be
distinguished by applying analysis methods based upon conventional shape parameters.
On the contrary, as regards the tanks made of a material for which the production processes (chemical
pickling, shot-blasting, etc.) allow the surface oxide layer resulting from rolling to be eliminated, inlaboratory capacity tests have proven the fitness of acoustic emissions to detect growing defects.
Figure 6 shows the start of signals representative of growing defects on surface oxide-free capacities. A
defect regarded as non noxious (bursting outside of a defect) cannot be detect when the pressure is lower
than the test pressure as the stress level reached does not suffice to make it vary. On the contrary, any
noxious defect (burst occurring on the defect and reduction in the burst pressure) generates acoustic
emission signals when the pressure is lower than the test pressure. The feasibility of the technique used
to assess the overall quality of the vessel is thus proven by the variation in emission start vs. defect size
curve. The start of emission of signals located on the defect increases with the defect noxiousness. We
also see that the burst pressure always exceeds the test pressure even with the deepest defects. One cantherefore deduce that the pressure equipment used in refrigeration systems is amply oversized.
A data analysis shows that for the type of capacity tested, applying a space-time criterion allows one todiscriminate the signals representative of changes in a crack during the testing cycle defined under theconditions described above. The space-time criterion is reached when the threshold corresponding to anumber of signals located within a small space (cluster) and of which the occurrence takes place in asmall period of time is exceeded. In some cases, the so-called "multi-criteria" combination of the spacetime criterion with another criterion based upon a shape parameter allows the diagnostic to be
sharpened. For the type of capacities considered, adding this additional criterion allows one to eliminate"excessive false alarms" insofar as the space-time criterion suffices to detect all the tested growing
defects.
Let us also specify that the above results have been achieved on capacities showing man-made defects ofthe electro-discharge type, which are certainly easy to make and calibrate but of which the applicability
is questionable.
147

Sensor position

Sensor position

Stress (MPa)Stress (MPa)

2
2

1

1

Time (s)Time (s)Fig. 3: Difference of burst emission between an oxidized specimen and an oxide-free specimen.

Material

Cracked
oxide
layer

Oxide
layer
Material

Before loading

After loading
Fig. 4: Oxide layer cracking.

FUTURE PROSPECTS
By using the acoustic emission, the procedure proposed by CETIM would allow conventional hydraulictests to be replaced by pneumatic tests depending on the surface condition of the material used. The
method performance in assessing the vessel overall quality during a pressurization test could be ofservice in new applications. The next step could consist in transferring the method to industry byintegrating the CETIM system into the measuring sequence and the performance of validation tests(robustness test).

148

Fig. 5: Events localized on the tank during testing cycle.
EA earliness (Po : pressure of first EA on defectNon noxious defectNoxious defect

3,5
3
2,5
Po/
Pe 2
1,5
1
0,5
0

Theoretical Pr

Actual Pr

Pt
Ps

0
50
100
Defect surface: length / depth (mm?)

Where

150

Pr : burst pressure
Pt : test pressure
Ps : operating pressure
Po : 1st emission pressure on defectFig. 6: Start of occurrence of signals representative of growing defects on capacities of which
the material production mode allows the oxide to be removed.
149

THE NEW RUSSIAN STANDARDS IN THE FIELD OF ACOUSTIC EMISSION
Valery I. IVANOV and Igor E. VLASOVOrgenergoneft, Moscow, Samara, Russia

Abstract
Three AE standards are created in Russia in recent years: 1) Regulation of organising and carrying out
of AE testing of pressure vessels, apparatus, boilers and industrial pipelines, 2) Requirements toacoustic emission instrumentation used for monitoring of hazardous production facilities, 3)
Requirements to acoustic emission sensors used for monitoring of hazardous production facilities. Thesestandards have been approved by the main state agency of supervision over industrial safety in Russia Gosgortechnadzor of Russia. These are used for testing hazardous production facilities.
Introduction
Any evolution meets the crisis. Crisis generates problems but at the same time it stimulates new
approach, which in Russia includes burst Acoustic Emission NDT Method (AEM) application inindustry. Setting standards is a problem that we would like to discuss here.
AEM provides suitable classification of defects in an object under testing. AEM is increasingly operated
in industry and widely used for safety maintenance of hazardous facilities. AEM operation is approved
by "Regulation of Set-up and Safety Maintenance of Pressure Vessels" [1]. In USA AEM application
for industrial facilities is regulated by ASTM, ASME and other technical societies. There are standardsof AEM application in several other industrial countries.
During 1996-99 we have developed three basic Standards for Russian industry;- Regulation of organising and carrying out of AE testing of pressure vessels, apparatus, boilers
and industrial pipelines. RD 03-131-97 [2].
- Requirements to an acoustic emission instrumentation used for monitoring of hazardous
production facilities. RD 03-299-99 [3].
- Requirements to an acoustic emission sensors used for monitoring of hazardous production
facilities. RD 03-299-99 [4].
All these documents have been approved by the main state agency of supervision over industrial safety
in Russia - Gosgortechnadzor of Russia.
The First Standard«Regulation of AE Testing» states principles and procedures of AEM application and lists the facilities
that can be examined by AEM. Special attention is drawn to preparation of AE work activities. It is
recommended to perform the great amount of preliminaries before AE. It includes familiarity with orevaluation of material AE and wave-guide characteristics and that of careful choice of AE sourceclassification system and rejection criterion. The entire recommended criterion is indicated in non
mandatory Enclosure.
“Regulation of AE Testing" is a procedural and technical–organizational norm document, whichcontains orders, regulation and information. These provide AE testing of hazard facilities of oil150

chemical, gas, power and other industries. “Regulation of AE testing" consists of nine basic issues andeight Enclosures. The main issues are: General Provisions; Requirements to Organizational
Management, Requirements to Executors and Procedure of Preliminaries to AE, Requirements to Outfit
and Equipment, Testing Procedure, Data Accumulation, Processing and Analysis; Estimated Results;
Record-Keeping on Testing Results; Safety Guidelines and Responsibility for "Regulation" Violation.
Enclosures: AE sources classification system and criteria of examined subject status condition;
Enumeration of reference documentation and literary sources, Requirements to Examination Report's
contents, Forms of records (protocol) and report of examination results, Standard load diagram, Terms
and definitions.
General Provisions contain documents purpose and range of its application. There is information about
AE examination, its characteristics, and advantages and disadvantages (limitations). Division "Plan of
AEM application" contains recommendation on how to use AEM in combination with othernondestructive testing (NDT) methods and its independent application. The original division is"Requirements to Organizational Management, requirements to executors and procedure of
preliminaries to AE". It specifies scope of client's and contractor's work activities during decision
making on carrying out of AE testing of the object and after it. Specification of major preliminary effort
to AE examination should help to carry out the inspection in accordance with necessary requirements
and provide the qualitative results.
Organizations to carry out the AE testing should meet several requirements to provide high performancelevel. It should be certified by Gosgortekhnadzor. The inspection personnel shall be properly trained andlicensed/certified. The Issue "Outfit and Equipment Requirements" states the conventional requirements
encountered in the majority of similar documents. The issue "Examination Procedure" specifies
technology of the inspection, including such stages as installation of AE sensors, performance check ofequipment and channels calibration (set-up), loading of the object, location of AE sources, noise
analysis.
"The Regulations" specify that if there is no possibility of reducing the noises to required value, the AE
examination should be stopped. The most important part of "The Regulation" is "The Estimatedexamination results". During AE examination, AE sources are registered, identified, classified and AE
sources that exceeded threshold level are evaluated. Executor fixes admissible class of AE sources while
preliminaries to AE inspection. So, “The Regulations” define 2-stage approach to estimated examination
results:
1. Classification of AE sources
2. Identification of the source as acceptable or not
Classification of AE sources should be carried out on single or few parameters of signal: cumulative
counting, amount of impulses, amplitude, power, counting rate, activity, AE sources concentration andtime. Detected and identified AE sources are divided into four classes (categories):
I class - passive source;
II class - active source;
III class - critical active source;
IV class - catastrophic active source.
I class source: source registration for the purpose of its impact analysis of future development. II classsource: registration and observation over development in process of examination, keep records and makenotes on recommendation of for carrying out additional inspection by other methods. III class source:
registration, observation over development, taking actions on preparation the possible load rejection.
When IV class source would be detected, the following activities should be performed: immediate151

unload to 0 or to the value when AE source class would come down to II or I. After unload, the object
should be examined and the other methods of testing should be used if necessary. AE source should be
identified and future-testing safety should be stated.
If the results of AE examination indicate that the estimation of the object’s technical status was
acceptable or no other AE sources were registered, application of additional nondestructive testing
would not be required. If AE examination result interpretation was not clear, additional kinds ofnondestructive methods should be used.
Executor should choose system of AE sources classification and criteria estimation of testing results andsubmit it to client’s approval. Parameters of AE sources classification system and criteria of testing
results estimation should be chosen from classification systems described in the Attachment especiallyfor each object. That is why AE testing safety performance and future safety of the object under testing
depend on Executor’s qualification and experience.
“Regulations” state instructions on record-keeping of testing results. This document contains
requirements to Examination Report’s contents. In addition, there are forms of protocol and testing
results and conclusion. Examination Results Report should contain the information, which would allow
to estimate the object and confirm qualification level of organization and specialists carrying out theexamination. Based on this, it will be possible to estimate the reliability of results.
Requirements to AE Instrumentation
Standard “Requirements to AE instrumentation” contains the following items: methods of classificationof AE examination, parameters and technical characteristics of AE equipment, requirements toparameters and technical characteristics and methods of its determination. Technical characteristics of
AE instrumentation should be determined for each AE system (device) during certification andcalibration.
Methods of AE examination are divided into devices and systems depending on its complexity.
AE instrumentation is divided into four classes following information volume received during AE
examination. Since the dynamic range of AE signals may achieve 100 dB or more during AE testing, it
is advisable that dynamic range of the equipment would be the same. Range of equipment working
frequencies should be of 10 kHz – 1.0 MHz. Irregularity of frequency – amplitude characteristics should
not exceed ±3 dB. Error of AE signal amplitude measurement should not exceed ±2 dB. Input range ofthreshold regulation: 20 - 80 dB (in reference to 1 µV of channel input), discrimination 1 dB. Regulationof all others equipment parameters together with exact parameter values are proposed in the Report.
Requirements to AE Sensors
AE signal interpretation, estimation of testing results and decision making to a great extent depend onvalues of AE signal parameters. Characteristics of AE sensors (AES) influence greatly the parameters ofregistered signal. The knowledge of AES characteristics should be obligatory. AES is a complex electroacoustic system, which converts acoustic signal to electrical one. In Standard “Requirements to AE
Sensors” the basic parameters are chosen and methods of its measurement are given. There are fourparameters of AES: pulse characteristics: sensitivity (or transudation factor), impulse characteristics,
fundamental frequency and energy characteristics, which reflects such characteristics as impulse signal
energy transmission. All the above-mentioned parameters are real and are measured during calibration,and they are applied in most of criteria for AE sources classification.
152

Sensitivity of AES – “s” is determined as ratio of “um“ – Maximum value of AES impulse
characteristics - to “u1“ - acoustic signal maximum value (particles displacement of standard block orobject under testing surface) of standard source directly under AES or close to it: “s = um / u1”.
Standard contains AES classification based on different parameters, including sensitivity, appliedfrequency range, frequency. AES are divided into four classes based upon sensitivity: I class sensors
possess sensitivity less than 50.106 V/m; II class sensitivity range (50-100).106 V/m; III class sensor
sensitivity range (100-200).106 V/m; IV class sensors possess sensitivity exceeding 200.106 V/m.
Various methods of parameter determination have been worked out for AES calibration, including
analytic approaches and experimental principles of parameter values measurement. The following items
have been assumed as a basis of experimental methods of parameter determination: AES reaction on
dynamic displacement, standard surfaces of calibration block (object) on which AES was installed.
Different stimulation sources of AE signal have been described, advantages and disadvantages of one or
another calibration signal have been estimated. Methods of AES parameter measurement are similar tomethods applied in existing standards (ASTM E 1106).
Various systems of measuring and parameter estimation of AES have been described; advantages anddisadvantages of these systems have been fixed. Requirements to the following calibration means havebeen settled: calibration signal source, calibration blocks and devices, which are used for calibration.
During development of the Standards, needs of discussing principal issues became evident. At present,
physical aspects of AE are more or less clear. That is, AE signal generation, its propagation,transformation and so on. The present authors believe the AE physics have been already created. Now it
is time to work out the basis of AE examination. These are the fundamental problems of worldview.
Besides there are scientific, technical and organizational questions which we would like to discuss.
Brief list of AE testing problems (just as for any other nondestructive testing and technical diagnostics)
includes such items as personnel, Standards and equipment. In any case these problems exist all over theworld. If we would not pay attention to borders we can say that these are the problems of the united AE
society:- precise program of method’s development and cooperation on coordinated programs- involving new (young) people in AE field- interchange of ideas and personnel.
The important issue is what factors would influence AE examination results. These results might bemore disastrous because AEM is closer to disaster than any other method. The question is “for how
closer?”Development of techniques and standards in AE field requires the international efforts. Many teams
should work on one program. To start with, for example, program which will allow receiving from eachteam for each examination information about examination’s reliability and about possibility of thesedata comparison. It should be appreciated to make an agreement (under condition of confidentiality) oninterchange of examination results and to work out a system of receiving and submitting the data. It isnecessary to have a group of teams to carry out analysis and analysis system. At the moment the weakest
point of AEM is not the lack of standards, but an operator. Certainly, standards require important
revision and amendments.

153

References
1. Regulation of Set-up and Safety Maintenance of Pressure Vessels. PB 10-115-96. Gosgortechnadzorof Russia. 1996.
2. Regulation of organising and carrying out of acoustic emission testing of vessels, apparatus, boilers
and technological pipelines. RD 03-131-97. Gosgortechnadzor of Russia. 1997.
3. Requirements to acoustic emission instrumentation used for monitoring of hazardous production
facilities. RD 03-299-99. Gosgortechnadzor of Russia. 1999.
4. Requirements to acoustic emission sensors used for monitoring of hazardous production facilities.
RD 03-300-99. Gosgortechnadzor of Russia. 1999.

154

USING ACOUSTIC EMISSION TO MONITOR METAL DUSTING
Fridiric FERRER, Eric ANDRES, Jean GOUDIAKAS and Claude BRUN*
Elf Atochem, Saint-Avold Cedex, France, *Elf Atochem, Lacq, France

ABSTRACT
Metal dusting is a catastrophic form of carburization, which can lead to rapid wastage as the affected
metal disintegrates into a mixture of powdery carbon and metal particles. The phenomenon occurs ingaseous atmosphere, which is strongly carburizing in the temperature range of approximately 400˚C to850˚C with a carbon activity greater than one. The corrosion behavior of electropolished alloy 800H
tubes was investigated in H2-85%CO gas mixture at 650˚C. The metal dusting is preceded by internal
carburization, whereby the chromium is tied up. The remaining Fe-Ni matrix then reacts to form unstableintermediate carbide M3C, which decomposes to metal particles and carbon. Acoustic emission (AE)sensors were coupled with the tested material during the experiments. Thus, corrosion evolution and pit
distribution were monitored on-line and correlated with acoustic parameters. The introduction of organic
sulfides in the process gas produced a significant decrease in the corrosion phenomena.
1. INTRODUCTION
In highly carburizing atmospheres (e.g. H 2/CO/CO2/H2O), between 400˚C and 850˚C, materials such ascarbon and stainless steels, as well as nickel-base alloys, can be affected by a damaging phenomenon
called "metal dusting". In fact, a part of the material is rapidly turned into dust, which is composed ofparticles of metal and graphite. This degradation process can be very detrimental in environments suchas those encountered in thermal cracking or ammonia plants.
The typical feature of a material damaged by metal dusting depends on the nature of the material. In allcases, the corrosion products are composed of the previously described dust, but for carbon steels or
low-alloy steels, the corrosion develops uniformly, while for stainless steels or nickel-base alloys, it
grows by pitting.
The mechanism of the phenomenon has recently been described in several papers [1,2]. It has been
demonstrated that the carbon activity of the atmosphere must be greater than one. In classicalcarburizing gas mixtures, this activity is controlled by the following chemical reactions:
CO + H 2↔
H2O + C
(1)2CO↔
CO2 + C(2)
For plain carbon or low-alloy steels, five states of development can be distinguished. Firstly, there is anover-saturation of the metal with carbon. Secondly, cementite M 3C forms at the surface. Thirdly,graphite is deposited on the cementite surface. Fourthly, the cementite decomposes to metal plus carbon(M 3C ↔ 3M + C). Finally, there is further graphite deposition on the fine metal particles formed in theprevious state.

155

For high-alloy steels or nickel-base alloys, this mechanism is complicated by the existence of aprotective chromia layer. In this case, metal dusting may start at some defects on the scale, which causes
local attack and pitting [1]. The protection against this pernicious corrosion phenomenon requires either
the choice of a resistant material or the use of inhibitors. This paper deals with the latter solution. In
particular, some organic sulfides, known for their ability to decrease the amount of coke formation on
metal surfaces during cracking processes [3], could also have an inhibiting effect on metal dusting [4].
The DMDS is one of these organic sulfides. However, the problem with high-temperature corrosion is
the instantaneous determination of corrosion rate.
For that purpose, a method for on-line monitoring of corrosion evolution is needed. Recent work has
demonstrated that AE could be a powerful tool for studying low or high temperature corrosion
phenomena [5-8]. Accordingly, this technique has been chosen.
2. EXPERIMENTAL
The experimental device used in this study (Fig. 1) consists of a furnace, in which a metal sample tube isinserted and a controlled gas mixture feeding system. An AE acquisition system is composed of waveguides coupled to the metal sample, two wideband sensors (Vallen SE900) linked to preamplifiers(Vallen AEP4) and to a Vallen AMSY4 AE system. A hygrometer is used to follow H2O evolution due
to the chemical reaction (1). AE calibration is done before and after each test. By using the Hsu-Nielsen
acoustic source, we determined the waveguide attenuation to be about 15 dB.
Metal dusting tests were conducted during periods of ten days each. The principal experimental
conditions chosen are brought together in Table 1. The chemical composition of the tested alloy is given
in Table 2.

Fig. 1 Schematic representation of the experimental device.
3. RESULTS AND DISCUSSION
3.1 Test without DMDS injection
As a reference, the first test was conducted in a CO/H 2 atmosphere free of DMDS. After ten days in the
hot carburizing gas mixture, the 800H tube was removed from the furnace and examined. Under a thick156

Fig. 2 Metal dusting pits generated during the test performed without DMDS (3.75x).

coke layer, a distribution of numerous metal dusting pits was observed (Fig. 2). These pits were about 1
mm in diameter and 500 µm in depth. Further investigations by SEM and X-ray analysis allowed us to
identify clearly the nature of the corrosion products: a dust composed of particles of metal and graphite.
The evolution of AE activity recorded during this test shows three periods (Fig. 3). During the firstthirty hours, no AE could be detected: this corresponds to the incubation period of the phenomenon.
Then, especially for channel 2, AE activity increased almost linearly. As long as the atmosphere wasproducing high carbon activity, the metal dusting was activated. When the gas flux was changed to N2
flux, AE activity became weaker, or practically non-existent.
Acoustic parameters of the signals recorded during the emitting period are found in Table 3. Their values,
relatively dispersed, do not show significant evolution with respect to time. Despite the use of waveguides, which have an attenuation of ~15 dB, the measured amplitudes are high enough to prove that thesound-generating mechanism is powerful.
Figures 4 and 5 show respectively the number of visually observed metal dusting pits and the number of
localized AE events according to the X position on the tube (see Fig. 1). The pit distribution ischaracterized by two distinct populations (Fig. 4). The first one, between X = 100 and 350, is lessimportant than the second one, between X = 350 and 600. The fact that there are two populations is dueto the temperature profile of the furnace. Because of the latter, the optimal temperature for metal dustingis located in two critical zones of the tube, which explains the two pit populations.

157

158

Figure 5 shows that the distribution of AE sources also has two populations. The second one is very
similar to the second population in the pit distribution. On the other hand, the first population of
localized AE events is almost non-existent. After the post-test AE calibration, we found that this lack
could be attributed to an unfortunate loosening of the coupling of the sensor 2. The accuracy of the AE
location of the damaged areas is then confirmed.
3.2 Tests with DMDS injection
Other tests were conducted in a CO/H2 gas mixture with different quantities of added DMDS. After eachof these tests, coke deposition, pit distribution and AE were examined. The results are summarized inTable 4, in comparison with the reference test. The DMDS injection indeed inhibits the metal dusting
phenomenon. Moreover, AE monitoring corroborates perfectly these results. As no AE signals arerecorded when coke deposition is observed, it can be concluded that the emitting mechanism is
associated with the formation of pits and not with coking. A decrease in the AE acquisition thresholdwould perhaps allow for the detection of this carbon deposition.
Remark : During the test number 1, the relative humidity measured at the furnace exit varied in a similar
way as AE activity but with a lower sensitivity.

3.3 Interpretation
In AE studies of high temperature corrosion phenomena, the breaking of oxide scales is often assumed tobe directly responsible for the ultrasonic wave generation. In the case of metal dusting, and especially forhigh-alloy steels or nickel-base alloys, this idea can be justified by the fact that damage takes place atscale flaws. Moreover, the metal dusting mechanism itself, i.e., the sudden decomposition of acarbon-over-saturated metal in the combination of metal and graphite must play a role in the AE activity.
An extensive analysis of AE signal characteristics would help to distinguish between the two
mechanisms.
Concerning the inhibiting effect of DMDS on metal dusting, investigation is planned to yield an accurateinterpretation. In fact, following thermal decomposition of DMDS, sulfide compounds would fix on anddeactivate the sites of the metal, which catalyze the phenomenon.
4. CONCLUSIONS
Experimental conditions were determined in order to develop a rapid metal dusting on 800H alloy. Afterten days of testing, very significant damage, characterized by the presence of numerous pits, wasobserved.
The AE technique was tested as a possible monitoring technique. The AE signals are linked to the metal
dusting pits formation, which generates relatively high energy ultrasonic waves in the material. The pit
159formation can be detected following a passive acoustic period, which reveals the existence of an
incubation time.
The use of organic sulfide DMDS was proposed to inhibit this serious corrosion phenomenon. In thepresented experimental conditions, injection of small quantities of DMDS was sufficient to prevent themetal from being damaged or, at any rate, to retard the damage.
This work will be completed and followed by the examination of the preventive and arrestive properties
of DMDS injection; in particular, for the assessment of minimum required dosage. For this application,
acoustic emission monitoring will play an essential role.
References[1] H.J. Grabke, C.B. Bracho-Troconis and E.M. Moller-Lorentz. "Metal dusting of low alloyed steels".
Werkstoffe und Korrosion, 45, (1994), 89.
[2] R.J. Gommans and T.L. Huurdeman. "DSM's experience with metal dusting in waste heat boilers".
Paper 3b, Presented at the AlChE Ammonia Safety Symposium, Vancouver, BC, Canada, October 3-6,
1994, 20 p.
[3] Cher Dip Tan and R. Terry K. Baker. "The impact of selected sulfides o coke formation on various
metal and alloy surfaces". Published at the Department of Chemistry, Northeastern University, Boston,
Massachusetts 02115, 1999, 14 p.
[4] H.J. Grabke and E.M. Moller-Lorentz. "Effect of sulfur on the stability of cementite". Steel research,
66 (1995) No. 6, 254-258.
[5] H. Mazille, R. Rothea, The use of acoustic emission for the study and monitoring of localized
corrosion phenomena, Modelling aqueous corrosion, edited by K.R. Trethewey, P.R. Roberge.
Netherlarids: Kluwer Academic Publishers, 1994, pp. 103-127.
[6] F. Ferrer, H. Idrissi, H. Mazille, P. Fleischmann, P. Labeeuw. On the potential of acoustic emission
for the characterization and understanding of mechanical damaging during abrasion-corrosion processes,
Wear, 231 (1999), 108-115.
[7] M. Schulte and M. Schütze. The role of scale stresses in the sulfidation of steels. Oxidation ofMetals, 51, Nos. 1 and 2, (1999), 55-77.
[8] A.S. Khanna and coworkers, Application of Acoustic Emission Technique in High Temperature
Oxidation Studies. Key Eng. Mater., 20-28, no. 4, (1988), 3443-3451.

160

USE OF ACOUSTIC EMISSION TO DETECT LOCALISED CORROSION
PHILOSOPHY OF INDUSTRIAL USE, ILLUSTRATED WITH REAL
EXAMPLES
Alain PROUST and Jean-Claude LENAIN
EURO PHYSICAL ACOUSTICS SA, SUCY EN BRIE CEDEX, FRANCE
ABSTRACT
After several years of research in laboratory, the ability to detect active corrosion by acoustic emission
technique has been successfully proven. More recently, the industrial feasibility has been established in
the field, according to the requirements of two international chemical companies, on many cases ofdifferent pressure components and storage tanks. The goal was to make a diagnostic on the propagationof the localised corrosion, like pitting or stress corrosion cracking, on components during the process,on a restricted area in service components. This diagnostic has to be done immediately after themeasurement, by a simple system and procedure, applied by a non-expert in acoustic emission. This needarises from unpredicted failures of industrial installations due to rapid propagation of corrosion damage,after a long period of initiation. The development of units, with multiple process, leads to complex
problem upon the sensitivity of corrosion for an existing vessel exposed at various environments.
This paper presents several situations where CORPAC® technology can be applied, not only to detect andlocate damage from corrosion, but also to: characterise the corrosion (evaluation), help to finalise thebest choice of material, understand the part of the process cycle which produces, activates the corrosion,validate repairs, evaluate the efficiency of corrosion protection. Examples from different materials(stainless steel, carbon steel,…) on reactors, columns, piping, tanks are given.
INTRODUCTION
Localised corrosion cracking is an important damaging phenomenon in the chemical, petrochemical, andnuclear industries. Unpredicted failures occur in service, which have economic, safety, health andenvironmental consequences. Stress corrosion cracking, pitting and crevice corrosion are not easily
detected with traditional non-destructive techniques. Furthermore, these damages are mostly detectedduring shut-down inspection and the periodicity of the inspection are not adapted to such a phenomenon
which can take years to initiate and exhibit a significant speed of propagation. Material of the industrial
devices are chosen to resist to corrosion, nevertheless localised corrosion take place due to unpredicted
localised condition or transient state of process. Most of the time, the cause of localised corrosion is notclearly identified and at the opposite of general corrosion the kinetics are not easily predicted. When asubcritical corrosion damage is found, the inspection staff has to decided if they stop the production or ifthey can operate under survey because the propagation rate is low or more fortunately if the corrosion is
stopped.
The detection of the propagation steps of localised corrosion has been first a beautiful dream of scientist
before being demonstrated in laboratory scale. The AE signature of the "voice" of corrosion has beendrawn from scientific studies including a European BRITE EURAM contract before to be transposed inindustrial uses. The most difficult steps had been to extract automatically known signal fromenvironmental noise of process.
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This technology called CORPAC® has been achieved by building a database of SCC, pitting and crevice
signal in a vessels configuration at several chemical plants.
ACOUSTIC EMISSION APPLIED TO THE SURVEY OF CORROSION PROCESS
Acoustic emission (AE) is the result of sudden energy release within a material, which appears as elastic
wave. This technique is widely used as a non-destructive testing technique for fitness for service
evaluation in industrial field. AE is also a powerful tool to characterise and understand damage initiationand propagation. Most of all microscopic mechanisms has been studied and correlated with AE signals.
Many authors have characterised the AE signature of many damage phenomena as localised plasticstrain, twinning, ductile and brittle fracture for metallic alloys. Localised corrosion monitoring is more
recently under the interest of researcher. It has been proved that many type of localised corrosion
process can be detected by AE not only on laboratory scale [1] but also on industrial field conditions[2,5]. If AE sources from mechanical damage propagation are well accepted and understood, themechanisms that generate elastic wave from corrosion process are sometimes not so clear. To better
understand the effect of each corrosion process it has been necessary to identify recognised all parasitic
signals coming from environmental effect like boiling, crystallisation, particle impacts…
Stress corrosion cracking (SCC)
This phenomenon produces discontinuous “brittle” fracture surfaces in ductile material. SCC
mechanisms have been deeply investigated and it has been found that AE signals are producing from bothbrittle crack propagation and plastic blunting of crack tip. In the case of transgranular crack propagation,
the AE signal production is mainly a mechanical effect resulting from discontinuous crack propagation
steps [3,4]. AE signals have been also detected and clearly identified from intergranular SCC crack
propagation for instance in alloy 600 [6]. In this last case, it is not proved that this mechanism is purely
discontinuous, but it gives raise to EA hits even in aluminium alloys.
Pitting corrosion and crevice corrosion
A lot of mechanisms can be put forward to explain AE generation during pitting process, but many ofthem are not really realistic like metallic ion dissolution [5,7]. There is mainly 4 hypothesis which weremaintained to explain elastic wave production: salt crystallisation inside pits, hydrogen evolution due tocathodic reaction, localised plastic strain at pit tip, spalling of corrosion products.
In the case of material type stainless steel the most probable mechanism is the formation and hydrogen
evolution in the pit tip. For low carbon steel, the expansion of corrosion products can be the main
phenomenon responsible of AE detection.
ADVANTAGES AND LIMITATIONS OF THE TECHNOLOGY
Acoustic emission is the only non-invasive technique that can detect the initiation and propagation of
SCC, pitting and crevice corrosion in-service and on-line. According to lab scale, small scale and full sizestudies one hour of monitoring is statistically enough to detect real case of corrosion encountered inindustrial installation. A database is available for a wide range of stainless steels and carbon steels regard162to the SCC, pitting and crevice damage detection. The AE sensors are mounted on the external surface ofthe vessel wall and can withstand up to 170°C in standard conditions that cover most of the chemical
process. A solution for higher temperature (maximum operation around 600°C) is under development
and has given very good results for a SCC process at 250°C. Depending on the in service process noise
installation corrosion damage can be detected from half a meter up 10 meters away from the sensor. A
small access to the surface is needed and only local removal insulation has to be made before checking.
The know-how, database and self-diagnostic algorithms are incorporated into a portable system requiringno expertise on the part of the user. This is crucial point because the tools have to be used with aperiodicity related to corrosion risk and the industrial consequences defined by the risk based inspection
policy of the plant.
The CORPAC ® technology is able to detect only propagation corrosion process, which allows
monitoring corroding plant as function of process conditions. The main limitation of this technology is alocalised corrosion not detected if the corrosion process is stopped. For example, it is well known that
propagation of SCC on austenitic stainless steel is not significant below 40°C in chloride environment. It
has been verified with CORPAC® on a full size tank, with a continuous decreasing temperature rate, thatAE activity from SCC is not more detectable below 50°C in a hot chloride environment. That is the
major reason why AE has to be applied in service conditions. Also, this technique cannot determine if
the corrosion is initiated from the inside or the outside.
CASE HISTORY
Several case history are proposed to illustrate some uses of CORPAC®.
Case 1 : Risk-based decision and characterisation of corrosion evolution (Fig. 1)
After several months of operation [8], internal visual inspection reveals significant pitting corrosion in a
19-25 HMo vessel and pitting and SCC in a 316 Ti vessel. These two brawn new different stainless steel
vessels contain an organic product at 100°C. This product is supposed to be a non corrosive media
stored under nitrogen flow. The corrosion study concludes that the corrosion could be due to thepresence of few PPM of water. The production has to reduce as much as possible the content of waterand the inspection staff has to find a way to confirm that corrosion has stopped. Periodic visualinspection of grinding area is practically difficult to achieve. Internal wall has to be first cleaned withsteam introducing water not easily eliminated. Then, CORPAC® is used to assess the corrosion study
conclusion. In summer 1996, the first CORPAC® evaluation showed that very few AE is detectedconfirming that corrosion has stopped when water content is drastically reduced. Furthermore acomplete opposite diagnostic has been established six months later showing a strong corrosion activity at
the liquid/gas interface.
This last conclusion has been explained later by an incident of production. One month before theexamination, a dryer column has failed letting pass in the product a small amount of water. In 1998, thecorrosion diagnostic was showing a strongly reduced rate of corrosion due to complete consumption ofwater. The CORPAC® analysis as confirm the corrosion expertise and has given tools to maintain acorroded structure in service. The non-conclusive internal inspection has been cancelled and replaced bylow cost (no shut down) periodic survey with CORPAC®.
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Fig. 1

Fig. 2

Case 2 : Damage area characterisation (Fig. 2)
A dye-penetrant inspection has detected that an 18-m high column presents some trough wall cracks.
The micrographic examination shows clearly transgranular stress corrosion cracks in the austenitic
stainless steel material. The first solution should be to replace the complete column. CORPAC ®
examinations have been performed on the complete wall of the column under service condition. SCC
activity has been found only on a 2-m high section of the column corresponding probably to the level
where the temperature is the most susceptible to promote SCC. This diagnostic has been partiallyconfirmed on few 2-m squares with a pulsed eddy current technique. In this case, eddy current was able
to detect only trough wall cracks or cracks near the surface. Because CORPAC® analysis demonstrates
that defects are only propagated in a restricted area, the major part of this column has been conserved
and the defective part cut and replaced by duplex stainless steel to better resist to SCC in these specificconditions.
Case 3 : internal coating and repair validation (Fig. 2)
CORPAC ® has been performed on carbon steel railroad tank car and reactor with several types ofvitreous or organic coatings (glass, ebonite...). At the occasion of a regular hydraulic proof test, a residual
activity of corrosion was found before increasing the pressure. This unexpected phenomenon was due tosome external repairs, which produce micro-cracks in the vitreous coating. The AE activity was due togeneral corrosion localised in the coating breakage area produced by industrial water.
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In order to better understand and confirm this type of results, some experiments were performed on an
old railroad tank car filled with water, which have been taken out of service. CORPAC® technology wasable to detect corrosion activity not only where the coating was broken but also where electricalmeasurement did detect nothing.
The visual inspection reveals in this specific area the colour of the resins has changed. The permeation ofindustrial water trough this non-effectively broken coating was sufficient to produce significant AE
activity. 60 litres of hydrochloric acid were poured into the tank and within few min., AE activity ofdetected corrosion increases drastically showing that the corrosion rate is clearly correlated to AE
measurements.
Case 4 : Help in material choice.
Introducing coupons on real industrial installation is common practice to finalise a material choice forcorrosion resistance purpose. One of the major problems is to open the vessel periodically to withdrawn
coupons for observation. A CORPAC® unit was used to determine the corrosion initiation of several
coupons on a by pass pipe. The only two things to take in consideration are that the coupons have to be
in a same small area where the signature of each coupons can be mixed or also that the initial vessel is no
subject to corrosion. The recorded activity is closely correlated to the damage propagation rate.
The development of units, with multiple process, leads to complex problem upon the sensitivity ofcorrosion for an existing vessel exposed at various environments.
CONCLUSIONSThe propagation of localised corrosion such as pitting crevice and stress corrosion cracking is detectableindustrially by Acoustic Emission without any variation of internal or external loading. The CORPAC®
technology is rapid and easy to use and consists in “listening the process conditions. A patternrecognition algorithm is used to separate damage signature from process noise. The diagnostic of
CORPAC technology reveals the existence of active or non-active localised corrosion. Useable by a nonexpert in acoustic emission, it gives a rapid diagnostic on area, which a risk of localised corrosion issignificant. The areas to check are mainly defined by the inspection staff or sometime by internal
corrosion expert, according to several criteria. Most of the time the examination is decided by a risk
based inspection plan but also according the site experience. As corrosion indications are found,
CORPAC ® allows to monitor and characterise their evolution and gives tools to understand the transientphase of process which gives rise to damage. As localised corrosion can be monitored, corroded structure
can be kept in service with reasonable evidence of fitness for service and at least semi-quantitative
information on corrosion rate.
REFERENCES[1] PROUST A., Etude par la technique d’EA de la corrosion sous contrainte de trois nuances d’acierinoxydable (austénitique, ferritique et auténoferritique) en milieu chloruré chaud. Thèse GEMPPM,
INSA Lyon, 1992165

[2] LENAIN, J.C., Evaluation de l’état de corrosion des fonds de bacs de stockage par Emission
Acoustique, 3ème colloque Européen, Corrosion dans les usines chimiques et parachimiques. Lyon 14-15
Oct 1997.
[3] PROUST, A., FLEISCHMANN, P., MAZILLE, H., ROTHÉA, R., Acoustic Emission monitoringof stress corrosion cracking on ferritic, austenitic and duplex stainless steels. Corrosion déformation
Interaction. Les Renardières Septembre 1992.
[4] YUYAMA, S., KISHI, T., HISAMATSU, Y., AE analysis during corrosion, stress corrosion
cracking and corrosion fatigue process. J. Acoustic Emission, 1983, 2(1/2), 71-93.
[5] POLLOCK, A., Acoustic Emission capabilities and applications in monitoring corrosion, Corrosion
monitoring in industrial plants using non destructive testing and electrochemical methods, E. G.C. Moranet P Labine, Philadelphia, PA: ASTM, Special technical publication 908, 1986, P 30-42.
[6] CASSAGNE, T., PROUST, A., MAZILLE, H., Initial results on stress corrosion cracking
monitoring of alloy 600 in high temperature water using acoustic emission, ninth international conference
on environmental degradation of material in nuclear power system-water reactors, Newport Beach,
California, August 1-5 1999.
[7] MAZILLE, H; ROTHÉA, R., TRONEL, C., An acoustic emission technique for monitoring pitting
corrosion of stainless steels.
[8] SOMMEN, J., RENAUD, L., L’Emission Acoustique : une aide au diagnostic efficace en cas dematériel corrodé. CEFRACOR 3éme colloque Européen Lyon, 14-15 Octobre 1997.

166

INSPECTION OF FLAT-BOTTOMED STORAGE TANKS BY ACOUSTICALMETHODS; CLASSIFICATION OF CORROSION RELATED SIGNALS
P. TSCHELIESNIG, G. LACKNER, M. GORI*, H. VALLEN§ and B. HERRMANN¶
TÜV, Vienna, Austria, *CESI, Segrate, Italy, §Vallen Systeme, Icking, Germany,¶
Dow Germany, Stade, Germany

ABSTRACT
Acoustic emission monitoring is applied during on-line inspection of flat-bottomed storage tanks to locate
leakage in the bottom plates. Since location algorithms only handle signal arrival times, acoustic
emission waveforms still have a relevant diagnostic potential. With a deeper knowledge of the links
between the acoustic emission and the defects in a tank, this potential could be exploited in order tocomplement the location ability, and allow a more comprehensive evaluation of the tank. To investigate
improved tank assessment methods and strategies, an EC funded project was started in 1997. This
presentation highlights the feature analysis and pattern recognition applied to corrosion signals, gatheredfrom tank plates submitted to corrosive fluids in the lab. The classifiers based on such database are now
allowing a preliminary evaluation of acoustic emission signals from real tanks.
INTRODUCTION
The maintenance of flat-bottomed storage tanks, which suffer from corrosion, involves both economicand environmental matters. Early warning of degradation by corrosion should prevent leakage in the tankbottom. Actual leaks should be detected as early as possible, to avoid soil pollution or the sudden stopof the component. However, time-consuming and expensive off-line inspections, which require emptying
and cleaning of a tank, would be unwarranted if the bottom condition is found good. Hence, on-line
acoustic emission (AE) monitoring is performed as an interim test; so that, if the tank appears neither
leaky nor seriously affected from corrosion, there can be enough confidence to delay the off-line
inspection. The detection of several sources, when a tank is seriously affected from corrosion or theplant environment is very noisy, can make this decision rather difficult. In this case, additional evaluation
methods would be needed to complement AE source location. Since arrival times only are analysed
during location, AE waveforms still have a relevant diagnostic potential that could be exploited for thispurpose. Moreover, a significant improvement would be reached if such evaluation methods can be based
more on objective criteria and less on testing experience. Eventually, such improvements may further
increase the reliability of the AE monitoring as demonstrated in the ECNDT '98 paper of Van de Loo andHerrmann [1], where in some cases the predictions of the tank bottom were not in full agreement withthe actual conditions observed by off-line inspections.
The above considerations thus suggested a better understanding of the relationship between AE and tankdamage. With the conviction that such knowledge can improve the diagnostic potential and reliability of
AE monitoring, the EC funded project SMT4-CT97-2177 "Inspection of flat-bottomed storage tanks byacoustical methods" was started in October 1997.

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PROJECT DESCRIPTION
The project team comprises TÜV (coordination and testing), CESI (testing), Vallen Systeme (AE systemmanufacturer), and the industrial partners Shell - The Netherlands, and Dow - Germany, who support
the project by arranging tests in plants and providing follow-up information after off-line inspections.
This 3-years project includes the following main tasks:• Laboratory tests to collect signals from leakage and corrosion processes,• Evaluation of laboratory database using front-running and possibly automated analyses tools,• Performance of field tests on storage tanks,• Evaluation of field database using the methods developed after evaluation of laboratory database,• Definition of a tank assessment strategy.
The preliminary results, gained in the first two tasks, allowed the prosecution of the project after midterm assessment. This paper reviews the work done in such tasks, while field tests and data evaluation
are in progress for the completion of the project. The final task is also intended to open the mostsignificant results of this work to the European standardisation community.
PROJECT OBJECTIVES AND METHODS
The relationship between AE and potential tank defects, like other subjects of practical importance for atank evaluation, were considered for investigation and improvement by this project. For example, thediscrimination of the actual stage of degradation of a tank bottom is very important for tank users [2].
Also relevant are discrimination between actual leaks and heavy corrosion spots, reliable clustering andpositioning of AE sources, noise filtering. In fact, if a test is affected by noise from the tank structure or
weather factors, the processing methods should also allow a better selection between noise and signals
carrying the actual diagnostic information.
The focus on AE signal evaluation in this project has been emphasised by the use of feature analysis andpattern recognition methods. Reference laboratory tests were designed to provide a well-defined databaseto study the subjects, and develop improved methods for the assessment of real tanks.
In the frame of the project, the phenomena of leakage and corrosion were addressed, respectively, by
testing partners TÜV and CESI. The contribution of Vallen Systeme allowed a common instrumental
platform (AMSY4, VisualAE and VisualClass) at disposal of the testing partners, and has also granted
support and upgrade actions during the progress of the project. The results presented in the followings
have considered the analysis of the signals gathered by partner CESI from several tank plates submitted
to corrosion tests in the laboratory.
LABORATORY CORROSION TESTS
Through laboratory tests, a database of corrosion related signals was generated, made of AE waveforms
associated to selected combinations of plate surface and corrosion environment. Test plates (area lessthan 0.5 m2, thickness about 10 mm) were cut from a corroded tank bottom. Some plates (label A) were"as found" to preserve their scaled surface (i.e. old corrosion); other plates (label B) were "grit blasted"
to have a clean surface (i.e. new corrosion). The "old corrosion" and "new corrosion" surfaces wereadopted to study the discrimination of the stage of degradation.
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For the performance of the corrosion tests, four conditions were obtained by adding to plain water:
3wt%NaCl, 10%NaCl, acetic acid (pH=4), acetic acid (pH=4) and 3wt%NaCl, listed in order ofincreasing aggressive potential. The industrial partners suggested such conditions to simulate the actual(not accelerated) damage phenomena that may affect the tank bottom.
Corrosion was produced in a test pool (stainless steel) and AE monitoring was performed using different
sensors provided from the technical partners. Such sensors are mainly resonant in the range 20-30 kHz,with a lower but still significant sensitivity at higher frequencies.
The combination of the above plate surface and corrosion conditions enabled 8 different corrosion tests.
For the setting of signal classifiers, two additional tests were also considered, as two plates (45A and
45B), formerly tested under acetic acid, were submitted afterwards to the severer mix (acetic acid andNaCl) environment.
EVALUATION OF LABORATORY DATABASE - SIGNAL CLASSIFICATION
To attempt a classification, the most significant differences among corrosion signals corresponding to thedifferent plate-fluid combinations, have to be highlighted. This phase has to clarify two primary issues:
the laboratory database must show a separation potential and the analysis tool must recognise anddevelop such potential to a rational form.
For the study of corrosion signal classification, the 10 reference tests were considered. As 10 data sets,
each one identifying a corrosion test, were selected and fed separately to the program (Visual Class), a
corresponding number of reference corrosion classes were "a priori" defined to attempt the desired signal
classification. Each data set comprised 100-200 AE signals gathered from steady corrosion condition.
Feature extraction was performed in a frequency band of 20-320 kHz and a time window corresponding
to the whole recorded waveform (512 points at 1 MHz sampling rate).
The discrimination potential of the classifier can be evaluated according to the percentage of training
signals, classified in each expected ("a priori" defined) class. The result for one classifier (one selected
sensor) is presented in Fig. 1 where percentages, normalised to the actual number of signals in each dataset, are within 50-90%. Similar results were found after evaluation of AE signals from one more sensor.
CLASSIFICATION TRIALS USING LABORATORY CORROSION DATA
When the training session is deemed satisfactory, a classifier can be released for the classification of
unknown signals. To assess the reliability of such classifiers, trials were performed using signals from thelab database, that were not used in the training session. All the lab tests performed were considered and,as a general rule, the classification was in agreement to the expected plate-fluid combinations.
Among such trials, the most significant one is related to the test "45Ba.a." (a plate with clean surface,corrosion under acetic acid). Since the test lasted several days, many data sets could be selected for a
detailed description of the corrosion evolution after classification. The results are shown in Fig. 2: thetrial data sets fed to the classifier are identified with a date and a label increasing with the test time.

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100
90

classification (%)

80
70

11feb_c3

60

C343B_s
C344a

50

C344b
C345a

40
47b-c347a-c345b-c345a-c3
C345B
C345a
C344b

30
20
10
0
43A3%

43B3%

C344a
44A10

44B10%

45Aa.a.

class names

C343B_s45Ba.a.

45Amix

45Bmix

C345B
45a-c345b-c347a-c347b-c3

data sets

11feb_c3
47Amix47BmixFig. 1: "10-class" classifier based on corrosion signals. Distribution of training signals.
The results show that, at the onset of corrosion, signals mainly belong to the classes corresponding toless severe corrosion environment (3%NaCl and 10%NaCl). Afterwards, from the mid-phase of the test,
more signals fall in those classes corresponding to the expected corrosion environment (acetic acid). This
trend is better established as the test approaches its final stage (steady corrosion condition).
CLASSIFIER UPGRADE
In the above classifiers, no specific class was defined to account for noise. If noise signals happened
during a test, then the result of the classification would be confused.
Additional work was performed to provide signals representative of the following noise sources: bubbles(can arise from corrosion), water drops (may fall from tank roof), and rain (can be a very noisy source ina tank test). Bubbles and water drops were simulated in the laboratory. Rain signals were collected froma database recorded during a tank test. Then, one classifier was modified by adding these 3 "noise"classes to the existing 10 "corrosion" classes.
TRIALS ON REAL TANK DATA
Trials have been performed to study the performance of the above mentioned classifiers. The example
shown considers the same inspection, from which "rain" signals were extracted to upgrade the corrosion
classifier. This time the trial data set was selected during a phase of the monitoring, in which rain was notexpected to affect the test. Such data were analysed without pre-processing (Raw), and after
filtering/locating (F&L). Figure 3 shows the results for the "corrosion" and the "upgraded" classifiers. In
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80

classification (%)

70
60

13-mar50

16mar-I
16mar-II
17mar-I40

17mar-II
18mar-II
18mar-I
17Amar-II
17Amar-I
17mar-III
17mar-II

30
20
10
0

17mar-I
43A3%

43B3%

16mar-II
44A10

44B10%

45Aa.a.

class names

16mar-I
45Ba.a.

45Amix

45Bmix17mar-III
17Amar-I
17Amar-II
18mar-I
18mar-IIdata sets

13-mar
47Amix47BmixFig. 2: Classification trial for test "45Ba.a.". Distribution of signals unknown to the classifier.
Fig. 3a ("10 class" corrosion classifier), the classification shows that only classes 43A3%(NaCl) and
44A10%(NaCl) are populated. In this case, a test operator should conclude that this tank suffers fromsome "old corrosion" (plate label is A), though the severity of the process seems not very serious. In Fig.
3b (13-class upgraded classifier), the classification highlights an unexpected though significant
contribution from "rain-like" signals. In comparison to the performance of the 10-class corrosion
classifier, 10% and 40% of the signals previously located, respectively, in class 43A3%(NaCl) and
44A10%(NaCl), are now collected in the "rain" class. The result also suggests a less severe corrosion(less data in class 44A10%), as compared to the one suggested by the 10-class corrosion classifier.
CONCLUSIONSThis paper reported the evaluation of an AE database, made of signals gathered from corrosion
laboratory tests, performed for different plate/fluid combinations. Feature analysis and patternrecognition could evaluate the separation of such corrosion-related waveforms. Noise signals were also
considered for upgrading the corrosion classifier. The performance of such classifiers was assessed afterclassification trials. The results show that a suitably trained classifier, can sort out from a test databaseand classify, noise signals and signals relevant to corrosion of increasing severity.
On-line AE monitoring tests are currently performed in the SMT project. These tests are focusing tanks
scheduled for off-line inspections in a short time period. Eventually, the reliability of the processing andevaluation method based on signal classification will be assessed on the basis of follow-up information.

171

(A)
60

classification (%)

50
40
30

Raw
F&L

20
10
0
43A3%

43B3%

44A10%

44B10%

45Aaa

F&L
45Baa

class names

47Amix47BmixRaw
45Amix/aa

data names

45Bmix/aa(B)

60

classification (%)

50
40
30

Raw
F&L

20
10
0
43A3%

43B3%

44A10%

44B10%

45Aaa

45Baa

47Amixclass names

47BmixF&L
45Amix

45BmixRaw
BubbleDropsdata names

T18rainFig. 3: (A) Shell Tank T18 - Inspection October 98. Results from "corrosion" classifier;(B) Shell Tank T18 - Inspection October 98. Results from "upgraded" classifier.

172

REFERENCES[1] P.J. van de Loo, B. Herrmann, “How reliable is Acoustic Emission (AE) Tank Testing?" The
Quantified Results of an AE Usergroup Correlation Study!”, Proceedings of the 7th European
Conference on Non-Destructive Testing, Copenhagen, 26-29 May 1998 - vol.1 - pp. 597-604.
[2] D.A. Kronemeijer and P.J. van de Loo, "Acoustic Emission tank testing: How to discriminate
between the onset of corrosion and further stages of degradation?", Presented at the European
Conference on Acoustic Emission Testing, TÜV Austria, Wien, May 6-8, 1998.
ACKNOWLEDGEMENT
The authors place a special acknowledgement to the European Commission (Directorate General XII) for
supporting this SMT project. Many thanks also to the industrial partners Shell Global Solutions
International B.V. (The Netherlands) and Dow (Germany) for their contribution to the project work. To
Mr. Emilio Fontana for his efforts during the proposal of this project and to Mr. Fabrizio Cattaneo(CESI) who contributed to the work and results presented in this paper.

173

SCREENING OF TANK BOTTOM CORROSION WITH A SINGLE POINT
AE DETECTOR: AE-SIMPLE
P.J. VAN DE LOO and D.A. KRONEMEIJER,
Shell Global Solutions International B.V., CM Amsterdam, The Netherlands

ABSTRACT
Tank bottom corrosion assessment with acoustic emission(AE) is a well-established non-intrusive testing
technique. A drawback is that during the testing the weather conditions must be “ideal”. The
mobilisation of qualified contractors is sometimes in conflict with the required test conditions. The tankowners felt the need to resolve this issue. Shell Global Solution launched the idea of a simple single
point AE detector, to be applied by local inspectors at the best moment. A prototype instrument has been
built, called AE-simple. Filter criteria are applied similar to those of the TANKPAC test procedure. This
paper describes the reliability investigation of this tool. The aim is to discriminate “A-grade” tanks(very minor corrosion activities) from “non A-grades”. This because earlier investigations showed avery good correlation between A-grades and tanks not requiring repair. We found that AE-simple scored
85 % correct in the A-grade class, whereas 13 % were “misses”, scored in class B (minor corrosion
activities). The false call level is only 10%. This is judged as very promising, justifying further
development into a simple portable field instrument.
INTRODUCTION
In the world of tank maintenance, the idea to shift from time-based to condition-based maintenance getswidely accepted. Attention is focusing on non-intrusive techniques. As such acoustic emission (AE) is
now well established. A drawback of AE tank testing is that during testing the weather conditions mustbe “ideal”: no rain, weak wind, no operational activities. To plan the mobilisation of qualified
contractors and to arrange the test period with Operations sometimes conflicts with the required test
conditions. The tank owners felt the need to resolve this issue.
Shell Global Solutions launched the idea of a simple single point AE detector to be applied by local
inspectors at the moment, which suits best. Through such a low threshold test, which can easily berepeated, insight can be gained in the tank bottom condition. One may then decide to organise a full AE
test, or to postpone further inspection activities.
Main issue to investigate is the reliability of such a single point AE detector in comparison with a fullanalysis such as TANKPAC (1), where use is made of a large number of sensors, covering the fullcircumference of a tank. The reliability of that method has been investigated by an industrial user group(2). Main result was that tanks graded as “A” appeared not to need any repair action. “E-grades” wereindicative for serious damage, see Fig. 1.
APPROACH
The first point investigated is the AE feature to be selected to assign a grade. In earlier work we
identified the “duration” to be indicative for heavy corrosion spots with scale due to wall loss (3).
174

Although this observation is used for further detailed analysis (the “potential leak” data), for theassignment of an overall
Follow-up results versus AE-grades, normalised per AE-grade
population of 148 tanks (Shell, Dow-Stade, DSM, Q8, Total and PAL-data base)
100
90

Relative [%]

80
70
60
50
40
30
20
10
0
A

B

C

D

E

AE-grade
FU1/2: No or minor damage/ no repairs

FU3: Damage/ some repairs

FU4: Damage/ major repairs - new floor

Fig. 1. Follow-up results versus AE-grades normalised per AE-grade (2).
grade, the most important parameter is the number of registered hits.
These hits are registered during a monitoring period of one hour. This period is based on the statisticaloccurrence (fluctuations in time) of the corrosion activities. Because for a handhold device, used by anoperator, the favourable period should be as short as possible, we investigated the effect of shorter
monitoring periods. As an outcome, we adopted the empirically derived conclusion from TANKPAC,
that a monitoring period of one hour is optimum.
In the next chapter we will consider in full detail the variations in numbers of hits per channel as being
representative for an arbitrary selected monitoring point.
EXPERIMENTAL DATA
We started our investigation on a set of data obtained on 7 tanks of various diameters. The number ofhits per channel is given in Table 1. We observed significant variations. These can not be related to thelocated corrosion spots.

175

Within TANKPAC these numbers are weighted, depending on tank size, viscosity of the product,applied threshold, etc. to end up with a grade. How this will be built-in into an AE-simple unit is out of
the scope of this paper. We now only consider the assignment of a grade when based on the selection ofa randomly selected position on the tank wall, for which we will take any channel to be representative.
We will assume all other criteria unchanged.
Table 1. Number of hits per channel as registered on 7 tanks, various diameters, various overall grades asassigned by TANKPAC.
tankoverall grade
Ch-1
Ch-2
Ch-3
Ch-4
Ch-5
Ch-6
Ch-7
Ch-8
Ch-9
Ch-10
Ch-11
Ch-12
average

I
A
313
245
474
27
0
406

II
A
185
164
225
219
192
314

III
A
254
134
262
748
191
561

IV
B
1509
387
356
391
653
447
444
689
492

V
B
1425
1027
1312
950
1464
966
843
599
744

244

216

358

596

1036

VI
C
2806
2256
2042
818
1631
2209
1469
1342
1975
1934
3050
2615
2012

VII
D
5009
2548
1663
2285
6837
2956
7380
1681
5254
3903
2704
6249
4039

STATISTICAL BACKGROUND
On the basis of the numbers given in Table I, but also on the numerous data available from all tanks
tested with TANKPAC, it is justified to assume that the average number of hits per channel has aGaussian distribution. This means that we may apply the “normal” statistical rules for probability
intervals and standard deviations. The relevance of this is illustrated in Fig. 2.

σ
α1

µ1

σ

α2
µ2

A-gradeD-gradeFig. 2. Overlapping normal distributions of average number of hits per channel.
176

If the decision boundary is chosen as indicated in Figure 2, then α1 is indicative for the percentage of“false calls”: although belonging to the class of A-grades, they are assigned to be non-A. The percentage
is derived from the width of the distribution, expressed in the number of standard deviations (the factort). Some examples are given in Table 2. For example, if the selected decision boundary is µ1+1.44*σ, the“right exceedance chance” is 7.5%. α2 is indicative for the percentage of “misses: although belonging to
the class of D-grades, they are left of the decision boundary, thus assigned to the population of A-grades.
Table 2. Interval width, t-factor and single sided exceedance chance.
1
2
3interval width
factor t
single side exceedance

68%
1,0
16%

75%
1,15
12.5%

80%
1,28
10%

85%
1,44
7.5%

90%
1,64
5%

95%
1,96
2.5%

STATISTICAL EVALUATION OF THE 7 TANKS AND FURTHER GENERALISATIONIn Fig. 3, the average number of hits per tank is given, including the confidence intervals as given in
Table 2. The dotted horizontal line, indicated as “criterion”, is the decision boundary. By shifting thisline up and down, one can judge the effect on the false calls and misses. Where inspectors will actually
put the decision boundary will depend on a more detailed consequence analysis, which is out of thescope of this paper. More important to discuss here is generalisation of this approach.

Hits [#]

Normal distribution
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
-1000

tank D
95%
90%
85%
80%
75%
68%

tank 1235

tank 1222

tank 1214

tank 507

tank 503

tank 130

probability-intervals“non A” tankcriterion“A” tank

Fig. 3. Average number of hits and confidence intervals of the 7 tanks tested.
From the data of 46 graded tanks tested within Shell, we calculated the average number of hits perchannel, shown in Fig. 4. If we apply the decision boundary for A- and non-A-grades of 400 hits, as
indicated by TANKPAC, we observe that all assigned C-, D- and E-grades are indeed non-A-grades, theassigned B-grades show some false calls, whereas all A-grades are properly identified. This confirmsthat 400 hits is a good decision criterion.
To judge the scores for single point monitoring, we have to take the standard deviations into account.
Via linear regression of the available data, we found as best approximation:177

σ = 0.48 ∗ µ − 28
8500
8000
7500
7000
6500
6000
5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0

400
A-tankB-tankC-tankD-tankE-tankaverage hits per channel of verification tanks

Fig. 4. Average number of hits per channel for 46 storage tanks tested at various Shell sites andapplication of 400 hits as decision boundary for A-grades
It should be realised that the chance of a miss or false call does not only depend on the measured average
number of hits (µ) and standard deviation (σ), but also on the likelihood of the occurrence of a certain
grade! In other words:(probability of a good score) = (reliability of the technique) * (probability of occurrence)
Below we will illustrate this approach using the of distribution of scores as found by PAL when they
applied TANKPAC to more than 2000 tanks (Ref. 2. See Table 3). Tank owners know best their own
tank farm, thus the potential occurrence of good or bad tanks. If required they can fine-tune or adapt theapproach with their own figures.
Table 3. Distribution of grading scores obtained by PAL, when TANKPAC has been applied to all tanksof their customer database.
Assigned grade
statistical occurrence

A
34%

B
26%

C
25%

D
8%

E
7%

If we now combine the average number of hits and standard deviations per grade with the assumed mostprobable occurrence we can calculate the number of misses and false calls for any decision boundary. By
way of example we show the results for a decision boundary is 400 hits, applied to the 7 tanks of Table 1
and an average E-grade value derived from the set of the 46 tanks used earlier (see Fig. 5).
The procedure applied is: subtract the average number of hits for a certain grade from the decision
criterion. Divide that number by σ (which gives the factor t). This determines the width of the178distribution. From the statistical t-tables the single sided exceedance chance can be determined. When
this is multiplied with the chance of occurrence one gets the fraction of scores in the A class and thenon-A class. When this is normalised to 100% above and below the decision boundary, one can express
the scores in terms of percentages “misses” and “false calls”. We observe 85% good scores and no“serious” misses: 13 % are B-grades. Of the non-A graded tanks only 10% are false calls.
distribution of tanks, limit >400

100.0

100.0

90.0

90.0

80.0

80.0

70.0

70.0
percentages

percentages

distribution of tanks, limit < 400 hits

60.0
50.0
40.0

“misses”

30.0

60.0
50.0

“false calls”

40.0
30.0

20.0

20.0

10.0

10.0
0.0

0.0
A

B

C

D

A

E

B

C

D

E

tank gradetank gradeFig. 5. Distribution of tanks: misses and false calls.
CONCLUSION
To investigate the reliability of single point AE monitoring of tanks, we used the TANKPAC registeredsignals of any arbitrary single channel. When applying the average number of 400 hits as a decision
criterion for assigning A-grade tanks (No follow-up recommended) and non-A-grades (full analysis
recommended) we found that AE-simple scored 85 % correct in the A-grade class, whereas 13 % were“misses”, scored in class B (minor corrosion activities). The false call level is only 10%. This is judged
as very promising, justifying further development into a simple portable field instrument.
ACKNOWLEDGEMENT
We want to thank the student Arjan Schwartz for his contribution to the execution of the work. Also wewould like to thank Physical Acoustics Ltd. (PAL) for supplying us with the detailed information of thetested tanks.
REFERENCES
1. Physical Acoustics Group, “Acoustic Emission Testing of pressure systems and tanks”. Brochure
PAL, Over/Cambridge, UK.
2. P.J.van de Loo and B. Hermann, “How reliable is Acoustic Emission (AE) Tank Testing? The
quantified results of an AE Usergroup Correlation Study!” 7-th ECNDT, Copenhagen, May 26-29,
1998.
3. P.J. van de Loo and D.A. Kronemeijer, “Acoustic Emission Tank Testing: How to discriminate
between the onset of corrosion and further stages of degradation?” European Conference on Acoustic
Emission, Wien, May 6-8, 1998.
179

180

CASE HISTORIES FROM TEN YEARS OF TESTING STORAGE TANK FLOORSUSING ACOUSTIC EMISSION
Stephen N. GAUTREY, Phillip T. COLE, and H.J. SCHOORLEMMER*
Physical Acoustics Limited, UK and *Physical Acoustics BV, Netherlands.

ABSTRACTThe enormous cost of opening and cleaning a storage tank for inspection of the floor is wasted if it isfound to be in good condition. The system described here evaluates floor condition on-line bydetecting active corrosion from the resulting acoustic emission. Evaluation of the emission against an
experience-based database allows the floor condition to be established, and appropriate maintenancedecisions made. This paper is a small cross-section of case histories from the past 10 years, takenfrom a database of thousands of tests.
INTRODUCTION
During operation, the only part of a tank that affects integrity, which cannot be inspected from theoutside, is the floor. The enormous cost of opening and cleaning for inspection of the floor is wasted
if it is found to be in good condition. In addition, environmental problems may result from the needto safely dispose of the waste inside many crude oil tanks. Major oil and chemical companies haveworked with Physical Acoustics to develop a non-invasive method of evaluating floor condition,
without removing the product. This method, known commercially as TANKPAC™, is widelyreported in published papers, for example ref. 1 which looks at the reliability on 600 tank tests toJune 1996, and ref. 2 which reports the cost savings resulting from its use as a maintenance planningtool.
The method involves mounting sensors on the tank wall and detecting the emission that results from
active corrosion inside or underside the floor. Evaluation of the emission against an experience-based
database allows the floor condition to be established, and appropriate maintenance decisions made.
The system is not an NDT method but a maintenance planning tool, to tell when internal inspection
is needed.
Iron, in the presence of water and oxygen combine to form a hydrated iron oxide, commonly called
rust. This simple definition describes the one chemical reaction that probably costs industry more inlosses and maintenance costs than any other. The emission detected during a test results primarily
from the fracture and de-bonding of corrosion product as a result of the volumetric changes taking
place, see ref. 3. Although corrosion is the prime source of interest, a leak may generate noise thatmasks the corrosion activity, and this is of course of interest to the tank owner, who is often
unaware of the problem. This paper describes a selection of case histories from 10 years experience
monitoring storage tanks for floor corrosion.
CASE HISTORIES

180

All the case histories below show the results after processing to remove extraneous noise sources,
which are inevitable at the high sensitivities being used. Sometimes only a few percent of thecollected data is attributed to floor corrosion and shown in these plots.
A. Oil-Field Production Tank
This example is given first simply because it provides a good demonstration that the procedure does
detect active corrosion. Many tanks with highly acidic and corrosive contents, such as those used infield production, have epoxy-coated floors backed up by large internal sacrificial anodes. These
anodes provide protection when the coating has broken down, by corroding themselves; otherwise
the floor would rapidly hole through. The anodes in this case are 1 metre x 0.5 metre rectangular zinc
blocks. There are more than thirty in this tank, arranged in radial rings. Corrosion of zinc does notproduce heavy scale, so the signals are small, but clearly detectable where the floor coating has
locally failed and made them active. When the zinc has been consumed the activity becomes moredispersed and louder, as the floor corrodes away. The location system is so accurate that it almost
shows the shape and orientation of these anodes, a remarkable result (Fig. 1). The same system isused to locate and orient tank inspection "robots". Naturally, it is important to confirm the anodelocations from maintenance records in order to avoid incorrect interpretation.

Fig. 1: 54 m diameter oilfield production tank showing location of "active" anodes.
B. 48-m Naptha Tank Before and After Repair
This tank was selected for maintenance as a result of testing and receiving the grading of “E”, thehighest priority. Approximately 90 patches were used in the repair of the tank, which, with the181benefit of hindsight, should have had a new floor; following repair the tank was returned to service,
and a test carried out as a baseline. The "baseline" test showed that, despite extensive internal andmagnetic field leakage (MFL) inspection, not all the problem areas had been repaired, andconcentration of AE activity is shown, indicating potential future problem locations. (Fig. 2)

Fig. 2: Above: before repair; Below: after repair
C. 110-m GRP-lined Crude Oil Tank
The size of this tank and location of the damaged area some 40 metres from the nearest sensor makeit of special interest. No damage was seen to the GRP liner, but MFL indicated >60% metal loss
from underside in the active area, this was confirmed by plate removal. It was found that chlorides
were present in the sand, which had possibly come from the beach rather than the depot!
182

Fig. 3: GRP-lined Crude Oil Tank inspection.
D. Sulphur Tanks
A number of sulphur tanks have been tested, and the results have all been comparable, with 95-98%of the emission originating as a result of corrosion under the insulation on the tank wall. This makes
it almost impossible to evaluate floor condition without resorting to complex 3D location methods todifferentiate the two. Even then it is questionable if the floor result would be reliable. However, in allcases the wall was in exceptionally bad condition, threatening tank integrity, and in one case the tank
was shut down shortly after the test and found to be corroded >90% through over large areas, andpin-holed in several locations, the tank was scrapped.
Of particular interest was the way corrosion was concentrated at the insulation support rings.
Shown in the figure below is the vertical distribution of AE resulting from corrosion activity,
showing pronounced peaks at the support ring locations. (Fig. 4)
E. Hot Fuel-Oil Tanks
Temperature plays a large part in corrosion rate, so it comes as no surprise that warm tanks sufferfrom higher corrosion rates once any corrosion barrier has broken down and conditions are right.
Very hot tanks theoretically should not corrode, however a problem is found in the areas oftemperature gradient, with locally high rates. In this case underside corrosion was more than 50%
through floor around most of the annular ring, but over a very narrow band which was missed by theprevious "spot" internal inspection. Tanks in similar service have failed catastrophically in service.
(Fig. 5)183

Fig. 4: Vertical distribution of AE resulting from corrosion. Lower plot: Y-axis is height in metres,
X-axis is relative activity, peaks corresponding to severe corrosion at insulation support rings.

Fig. 5: Hot oil tank with severe annular corrosion.
184

F. Crude Oil Tanks
Crude oil tanks are usually large, and have indeterminate amounts of sludge in the bottom. The depth
and type of sludge can have a significant effect on the AE result and a number of tools are used totry and compensate for this, all based on experience and comparisons with follow-up inspection.
Sludge depth is possible to measure by dipping or sonar methods, but the type of sludge is more
relevant. Some tanks have had a measured 3 m of sludge that had little effect on the results,
transmitting sound easily. Others with "dense" sludge have a severe damping effect. Often of more
importance is that the sludge is sometimes the only thing keeping the product in the tank, and over
one hundred holes have been found in some floors of crude tanks after cleaning. Operators
sometimes try to "re-suspend" sludge prior to cleaning, this is when the floor will often start to leakas the "sealing" sludge is removed.
GRP linings are very common in some areas, and have been found to have minimal effect on the test
results. Very occasionally the lining de-bonds and breaks up, resulting in rapid corrosion and a highgrade AE indications compared with comparable service tanks.

Fig. 6: One of 50+ holed areas in a crude oil tank, (not leaking in service because the sludge sealed thefloor).
This also illustrates why "leak detection" is of limited use in professional maintenance planning.
Leak detection only tells if the tank is leaking at the time of the test. In tanks with sludge, the leaks
may stop and start on a random basis, over periods of seconds or minutes, or sometimes as afunction of fluid height. Cracked plate welds may leak at low fluid level but seal at high fluid level.
185

Examples have been seen where the weld was completely missing over up to half a metre, but thetank had been in service for 15 years.
G. Leaking Tanks
Leaking tanks fall into two categories; 1) those where the owner was not aware of a leak, and the leak
was found as a by-product of the floor condition assessment, and 2) those where a leak was known
or suspected, and confirmation or location was requested.
Assessment of floor condition is not possible in the presence of a significant leak, as the leak noise"drowns out" the corrosion activity. The "leak test" is a different procedure, when a significant leakis correctly located it is obvious, as the event rate is exceedingly high, with hundreds of events per
second from one position; in this case, a few seconds of data is sufficient. Leak location on largetanks is difficult because insufficient tanks in the field actually leak for operators to gain the broad
experience of the different situations that occur in the field. Laboratory tests can provide only very
limited experience. Recent software and instrumentation developments, currently being field-tested,promise to assist in this area.

Fig. 7: Gasoline tank; owner did not know of its leak, which was found during a routine test.

186

The gasoline tank above had an epoxy coated floor. Where this failed locally, the floor had pin-holed,
1 mm in diameter when taken out of service about a month after the test, but the rest of the tank wasin good condition.
In the following example, operations had calculated a 100 cubic metre per day loss from their naptha
storage farm, based on mass balance. Inspection felt it was a gauging error as there was no evidenceof leakage, but at the request of operations requested our testing. The engineer found the leaking tankusing a 5131-LF portable leak detector, then located the leak with the AE test system. The leak rate
was so high, and generating so much noise, that the system amplifiers had to be switched off toavoid signal saturation. Opening the tank revealed a 1 cm hole due to MIC, (micro-biologicallyinduced corrosion), and the floor was unsupported over a cavity "washed-out" by the leaking napthaof several cubic metres. Some 700 tons of naptha had gone into the ground since the leak was firstsuspected.

Fig. 8: A few seconds of data was sufficient to reveal the location of this 100 ton/day leak.
Other major leaks found in the field have been as a result of:• Corrosion, the most common reason.
• A pipe rubbing at the edge of a sump, wearing a hole through the corner.
• Weld cracking, or no weld present.

187

Small leaks may not be detectable, either because they generate no noise, or the noise is drowned out
by active floor corrosion. Multiple leaks in the same tank make location difficult or impossible,
depending upon their individual characteristics.
STATISTICS
These are reported overall in reference 1, and since that report on 598 tank tests, there has been no
statistical change in the distributions. Overall statistics don't tell the individual picture though, and a
few observations are worthwhile making:• Some sites have nearly all "bad" tanks. The method is not a substitute for maintenance and it
simply allows you to focus where it is needed most.
• Some companies have over-maintained their tanks, and they stand to save a lot of money by using
the method.
ª We can see there is no reason for tanks not to stay in service for very long periods, by combining
all best practices this should be possible. We see some sites with only internal corrosion, some with
only underside corrosion, but few have neither! Combining all best practices may achieve this.
CONCLUSIONS
AE now plays a major part in tank maintenance planning for many oil companies, bringing benefits
in safety, environment and cost. The TANKPAC™ procedure is very different from all
conventional AE testing procedures for tanks and vessels, as there is no change in stress required,
and the tank is simply monitored for a period of time. This makes the data interpretation andevaluation difficult, as there is not a parameter with which to correlate. The test is run at such a highsensitivity that proper noise recognition and elimination procedures are always required. The
limitations and restrictions on its application are well documented; the benefits far outweigh theseand have resulted in its rapidly increasing use world-wide.
REFERENCES
1. P.J. van de Loo and B. Herrmann, “How Reliable is Acoustic Emission (AE) Tank Testing? The
Quantified Results of an AE User-group Correlation Study”, 7th European Conference For NonDestructive Testing, Copenhagen, 1998.
2. S.D. Miller, J. O'Brien, D.L. Keck, “Implementation of Advanced NDT Technology Programs at
Saudi Aramco,” 7th European Conference For Non-Destructive Testing, Copenhagen, 1998.
3. Stephen N. Gautrey, Phillip T. Cole: “Acoustic Emission from Active Corrrosion underinsulation-A case history from a Sulphur Tank”, Presented at the 22nd European Acoustic Emission
Working group, Aberdeen, UK, 1996.

188

ACOUSTIC EMISSION DETECTION OF DAMAGE
IN REINFORCED CONCRETE CONDUIT
H. Warren SHEN, Subramanian IYER*, Mark A. FRIESEL, Ferdinand MOSTERT, Richard D.
FINLAYSON, Ronnie K. MILLER, Mark F. CARLOS and Sotirios J. VAHAVIOLOS
Physical Acoustics Corporation, Princeton Junction, NJ*
Metropolitan Water District, La Verne, CA
ABSTRACT
Acoustic emission has been used to determine condition in reinforced concrete conduit. Acoustic data
generated by PLB (pencil-lead break) were collected from both good PCCP (Prestressed Concrete
Cylinder Pipe) and damaged PCCP as digital waveform files. These files were analyzed using thenormalized frequency energy method. The results indicate that the energy in specific frequency
segments can indicate the condition of the conduit, and with an appropriate tuning process, may show
the type of damage; e.g., broken wire or delamination. A partial power function and a graphic alarm
have been implemented in a standard AE instrument. A portable handheld sensor-head support
structure has been designed and built to maintain a constant source-sensor distance spanning the widthof the desired inspection region for a standard 1.2-m diameter PCCP.
INTRODUCTION
Prestressed concrete conduit is used for large volume residential and industrial water transport. The
conduit is constructed of layered mortar and concrete, and has pre-tensioned wire windings embedded
for added strength. Continual load and corrosion may cause the wire and concrete to fail, and may result
in contamination and blockage of the conduit, and surface subsidence. A technique and instrument wasdeveloped which can reliably detect damaged conduit during periodic internal inspection.
In December 1998, a preliminary study using acoustic methods to monitor PCCP was carried out, andpromising results were obtained (1). The current work includes more detailed analysis using thisexisting data and new data collected in October 1999. Real-time results are presented later in the paper.
ANALYSIS ON EXISTING DATA
Previously (1), it was concluded that frequency distribution is a potential approach for monitoring
PCCP, as the high frequency response is significantly reduced when damage is present. Figures 1 and 2
compare the frequency spectra obtained from a good section of a pipe (Fig. 1) and a damaged section ofa pipe (Fig. 2) using a PAC 30 kHz AE transducers. The spectra from the good pipe show significant
energy around 40 kHz, with most energy between 10 and 30 kHz. The spectra from the damaged pipe
have much lower energy near 40 kHz when compared to the energy in the 10 to 30 kHz range.
PolyModal

Frequency Envelop Feature

PolyModal(2) is a waveform toolbox software, which combines classical wave-mode analysis anddigital signal processing methods with AE applications and test data. By using the Frequency Envelop
Feature, up to 8 AE frequency based envelop features can be analyzed. Basically it breaks the spectrum
down into (up to) 8 segments, where the area under the curve in each segment (partial power) is reported
as a percentage of total area.
189

PLB Performed at 10 O'clock
Position from inside of the GoodPipe.

AE Sensor Located at 2 O’clock
Position from inside of the Good
Pipe.

Fig. 1 - AE spectrum (above) obtained from a good prestressed concrete pipe (bottom).

Stress Wire Damage
Located at 12 O’clockoutside of the Pipe.

PLB
Performed at
10 O'clock
Position frominside of the

AE Sensor Locatedat 2 O’clock Position
from inside of theDamaged Pipe.

Fig. 2 - AE spectrum (above) obtained from a damaged prestressed concrete pipe (bottom).
190

Table 1 is the partial energy presentation of the data from good pipe and from damaged pipe. The totalfrequency range is selected as 0 to 80 kHz, and each segment spans 10 kHz.
Selection of Frequency Ranges for Partial Power Setup
It can be observed that four critical segments, namely, 0-10 kHz, 10-20 kHz, 30-40 kHz, and 40-50 kHz
can be used to differentiate good pipe from damaged pipe by comparing partial powers. However, whenmore than one segment is used the differentiation can be enhanced. Table 1 shows that partial power
segments 0-10 kHz, 10-20 kHz, 30-40 kHz, and 40-50 kHz give very good differentiation between PLB
signals traveling through a good area and damaged area of concrete pipe. For example, in the 0-10 kHz
segment, the signals of the damaged pipe ranged within 13% to 18% of the total energy while those of
the good pipe ranged within 5 % to 10% of the total energy. In lower frequency ranges (0-10 kHz and
10-20 kHz segments) the signals show lower fractional energy in good pipe and higher fractional energy
in damaged pipe. On the other hand, in higher frequency ranges (30-40 kHz and 40-50 kHz segments)
the signals show higher partial power in good pipe and lower partial power in damaged pipe. The
Mistras 2001 system (3) used for real-time monitoring can take up to 4 frequency segments for partial
power (fractional energy) calculation, so the four frequency segments: 0-10 kHz, 10-20 kHz, 30-40,
kHz, and 40-50 kHz were selected. It has also been determined that since the 0.5-mm lead carries more
power and gives better differentiation than the standard 0.3 mm, the 0.5 mm pencil lead will be used.
Table 1
Table Showing the Differentiation between Damaged Pipe and Good Pipe Using the Partial Power Approach.
Signals were generated with the 0.5 mm PLB procedure from position 1 (10 o'clock) and received at
position 5 (2 o'clock), and later from 5 to 1. R3I sensor was used.

Part1
0-10
(%)

Part2
10-20
(%)

Part3
20-30
(%)

Part4
30-40
(%)

Part5
40-50
(%)

Part6
50-60
(%)

Part7
60-70
(%)

Part8
70-80
(%)

Total

Transverse Damaged 1-5

14,55
17,96
18,28

22,13
20,77
21,77

17,23
18,51
16,56

13,16
13,64
12,89

13,81
15,05
13,44

8,67
7,53
9,08

4,98
2,72
4,38

5,48
3,82
3,60

100
100
100

Transverse Damaged 5-1

13,92
13,66
14,63

19,49
21,47
20,40

16,48
17,98
15,72

12,86
12,63
13,61

13,69
13,50
13,67

9,43
9,52
8,88

7,62
5,58
6,11

6,51
5,65
6,98

100
100
100

Transverse Good 1-5

8,10
9,91
8,02

19,57
17,83
17,58

17,74
17,02
17,49

16,08
15,27
15,71

14,84
16,86
16,61

9,34
9,45
9,78

7,28
7,71
7,96

7,06
5,94
6,85

100
100
100

Transverse Good 5-1

6,94
5,91
5,89

16,83
17,21
19,70

21,39
22,91
20,95

15,82
16,74
17,39

16,33
17,40
17,50

10,70
11,03
8,47

6,59
4,74
5,34

5,40
4,08
4,76

100
100
100

Partial Power (kHz)(%)

EXPERIMENTAL PROCEDURE FOR NEW DATA
A field trip to the Metropolitan Water District, La Verne, CA was made in October 1999 to verify anddemonstrate the instrument. It was the first time that a real-time partial power function was set up andactivated to evaluate PCCP. The test configuration is shown in Fig. 3. Here, only signals generated at
position 1 and received by sensor 2, and those generated at position 5 and received by sensor 1, are191

discussed. The experimental procedure basically repeated what was performed as discussed earlier inthis report. However, in addition to testing the real-time partial power function, two kinds of damage,
namely, broken wire and delamination, were studied separately to see if the AE signals demonstrate agood classification on these two damage types.
The real-time partial power calculation was activated for the field experiment. This system function
automatically calculates the partial powers in up to four preset frequency segments. By comparing thepartial- power percentages of each segment, the condition of the pipe can be determined. With anappropriate adjustment of the partial-power settings, a classification of the damage type, e.g. broken
wire or delamination, is achievable.
3

4

2

5

1
Ch. 2

Ch. 1

Fig. 3 - Illustration showing the cross section of a PCCP conduit with the 2 sensor locations (Ch 1 and Ch 2)
and the 5 signal source locations. In the current study, only data from PLB signals generated at Locations 1
and 5 are described.

RESULTS
Table 2 presents the results from the real-time partial power calculation of the PLB signals traveling ingood pipe, pipe with broken wire damage, and pipe with delamination damage. It can be seen that thepartial power in segment 3 (Part3: 30-40 kHz) and in segment 4 (Part4: 40-50 kHz) can differentiate
between the good pipe and damaged pipe (either broken wire or delamination). Segment 3 (Part3: 30-40
kHz) can differentiate between delamination and broken wire but needs further adjustment (see below).
Since this is the first time that the real-time partial power calculation was applied, the results shown in
Table 2 can be considered promising.
ADJUSTING THE PARTIAL POWER FUNCTION
The following steps describe how we adjust the system for the best PCCP evaluation and monitoring
results. The adjustment should be carried out when monitoring different conditions, e.g. pipe material,
pipe dimensions, buried pipe or underwater pipe.
192

1. Obtain the pipe diameter to calculate the distance between the PLB source and the receiving sensor.
2. Find the right threshold so the system can be triggered by a PLB signal even if it has traveled throughbadly damaged area. The threshold is set as low as possible to be sensitive, but it cannot be crossed bybackground noise. The procedure of finding the right threshold is through experimental trials.
3. Set the sampling rate and hit length to determine the time period of a waveform section being
recorded and processed. For a 2-MHz sampling rate the time period being recorded and processed is
500 µs. This should be determined if overlapping is acceptable or not.
4. Different sensors will respond in a different dominant frequency range, and the partial power
frequency range must be adjusted accordingly.
5. In the Mistras system up to 4 frequency segments can be set up for partial power calculation. A
standard procedure for identifying the best frequency bands is currently under development. With the
PAC 30 kHz sensors, the total dominant frequency range can be set at 0 to 80 kHz. The 4 frequency
segments are set at 0-20, 20-40, 40-60, and 60-80 kHz ranges for a general evaluation. After a firstround trial there will be two segments giving good differentiation between the good and damaged
conduit, e.g. 20-40 and 40-60 kHz ranges as was found here. The 4 frequency segments are thenadjusted to 20-30, 30-40, 40-50, and 50-60 kHz ranges for a second trial. After identifying the damaged
pipe, it may still be desirable to know what type of damage has occurred. Different damage types may
show up more clearly in different frequency bands.
6. After the partial power parameters are determined, the graphic alarm is used to serve as an automatic
warning system. The graphic alarm has a general threshold setting, and the user needs to enter athreshold. Whenever a signal generates partial power beyond the level (above or below), the alarm willbe triggered.
CONCLUSION
Partial power application for pipe damage evaluation and monitoring is a simple pattern recognition
system. The four frequency segments can allow fast calculation and real-time classification. The partial
power calculation in the Mistras 2001 can support up to four segments of frequency ranges. It has
shown promise for differentiating between good and damaged pipes as well as different kinds ofdamage. For an in-situ real-time monitoring application, more than four segments might be required.
REFERENCES(1) Friesel, Mark A., Shen, H. W., and Mostert, F., 1999, "Feasibility Study of Monitoring Prestressed
Concrete Cylinder Pipe Using AE Techniques," Final Report, Physical Acoustics Corporation,
Princeton Junction, NJ.
(2) PolyModal Wave Toolbox: User's Manual, 1997, Physical Acoustics Corporation, Princeton
Junction, NJ.
(3) Mistras 2001: User's Manual, 1997, Physical Acoustics Corporation, Princeton Junction, NJ.

193

Table2
DataShowing the PLB (0.5 mm)Results Analyzed by the Real-TimeMistras Partial Power Method.
R3ISensor was Used forthe Test.

Partial Power (kHz)

Part1
0-10
(%)

Part2
10-20
(%)

Part3
30-40
(%)

Part4
40-50
(%)

Total of
Part1 to Part4(%)

TransverseGood 1-5

0
0
0

59
59
55

9
8
10

5
5
5

73
72
70

0
0
0
0

53
57
53
64

8
8
9
5

3
3
3
3

64
68
65
72

0
0
0
0
0
0

5
6
1
6
5
7

22
22
18
24
25
25

56
57
63
54
54
52

83
85
82
84
84
84

0
0
0
0
0

26
25
26
25
25

20
22
20
22
22

21
19
19
19
18

67
66
65
66
65

1
4
1
0
1

27
24
23
17
22

21
18
19
14
18

34
37
39
42
39

83
83
82
73
80

0
0
0
0
0

17
24
20
24
18

22
10
11
12
27

6
12
10
8
5

45
46
41
44
50

TransverseGood 5-1

Transverse1-5
Broken Wire

Transverse5-1
Broken Wire

Transverse1-5
Delamination

Transverse5-1
Delamination

194

Meeting Calendar
44th Meeting of AEWG
June or July, 2001; Montreal, Canada: Program Chair: Robert Hay(BobHay@tisec.com)
Expect announcement shortly

E-GLEA II
10-14 September, 2001; Laboratorio Ondas Elástica, Ensayos No Destructivos y
Estructurales, COMISION NACIONAL DE ENERGIA ATOMICA, Buenos
Aires, Argentina: see for datail at http://www.cnea.gov.ar/cac/endye/glea.htm
Proceedings of GLEA-I is available. See the web site.

25th EWGAE Meeting (EWGAE 2002)
The second week September, 2002; Prague, Czech Republic: Program Chair: P.
Mazal (mazal@cms.fme.vutbr.cz)

15th International AE Symposium (IAES-15)
Date not fixed, but in 2002 at Tokushima, Japan; Symposium to be Organized by
K. Yoshida (yoshida@me.tokushima-u.ac.jp)

195

COMPARISON OF ARTIFICIAL ACOUSTIC EMISSION SOURCES AS
CALIBRATION SOURCES FOR TOOL WEAR MONITORING IN SINGLEPOINT MACHINING
A. PRATEEPASEN, Y. H. J. AU and B. E. JONES
Brunel University, Uxbridge, Middlesex, UK
ABSTRACTTwo artificial acoustic emission (AE) sources, an air jet and a pulsed laser, were evaluated in reference
to their suitability as a calibration source for single-point machining and tool wear monitoring. The air
jet source was found to have a more similar rms AE-spectrum to that obtained from machining than thepulsed laser source. The rms value of the AE signal (AErms) produced by the air jet source wasobserved to be linearly proportional to the air pressure applied and sensitive to the torque used totighten the insert onto the tool holder. When the applied torque was greater than 1.2 Nm, the AErms
remained constant. Thus, once the tightening torque is above this threshold, the AErms value obtained
from a sensor can be converted into an air pressure value. In this way, providing a set-up is calibratedusing the air jet source under a defined condition, results obtained from different set-ups, having been
identically calibrated, can be compared, thus facilitating a transfer and sharing of knowledge.
INTRODUCTION
Research into the use of acoustic emission (AE) for tool wear monitoring [1-10] has established that
there exists a definite relation between AE and tool wear. Attempts were made to model the AE process
in machining, but despite the fact that general trends could be predicted satisfactorily, the absolutevalues of AE produced in apparently identical machining processes could still differ markedly from one
set-up to another.
The root cause of the problem is that the components that make up the AE transmission andmeasurement system as well as the interfaces between the components are highly variable. For singlepoint machining, typically, the components comprise an insert, a tool-holder and a sensor whereas theinterfaces refer to those that occur between the tool insert and the tool-holder; and between the toolholder and the sensor. Changes in either the components or the interfaces can produce a very different
AE response. A striking example is the coupling between the insert and the tool-holder where, as will
be reported in this paper, an increase in the clamping torque on the insert results in a significant drop in
the root-mean-square value of the AE signal (AErms). Consequently, AE results obtained from different
research centres are not easy to compare making knowledge transfer at best difficult, if not impossible.
To achieve transferability of results and hence knowledge, some form of AE calibration is necessary.
The process of calibration involves a measurement procedure carried out under specified conditions. Its
objective is to establish the relationship between the value of a quantity as indicated by a measuring
instrument and the corresponding value from a reference standard. When the result of the measurement
can be ultimately related to a stated reference, such as a national or international standard, through an
unbroken chain of comparisons all having stated uncertainties, then the measurement is said to be
traceable to the standard.

196

It is important to note that the calibration of a sensor, as is conventionally done, in order to determine
the AE at the sensing element of the sensor is not of much practical value. This is because one is often
only interested in the character of AE at its source, for example, at the cutting edge in machining. Whatis immensely more useful is the calibration of the whole AE system with the location of the AE source
known and the point of the sensor attachment decided. Understandably, once the layout of the sourceand sensor is changed, the system has to be calibrated again.
In this paper, two artificial AE sources, an air jet source and a pulsed laser source, were studied to assess
their suitability as an AE calibration source for the single-point machining process. The effects on the
AE were investigated of the clamping torque applied to the tool insert and a calibration procedure wassuggested.
ARTIFICIAL AE SOURCES
Based on the wave shapes, artificial AE sources can be classified into three different categories [11] as:
1. Noise – produced from, for example, helium gas jet impact, fracture of silicon carbine particles,
stress corrosion cracking and phase transformation in AU-47.5% Cd;
2. Continuous waves - generated by exciting piezoelectric, electro-magnetic and electro-static devices;
3. Impulses – arising from sparks, breakage of glass capillary, breakage of pencil lead, dropping of asteel ball on a hard surface to produce an impact, point-contact resistive heating and laser pulse
heating.
Berlinskey [12] used two artificial sources, a dropping ball and a pulsed laser, in the study of martensitic
transformation in Fe-30Ni. McBride [13] used a helium gas jet to calibrate the AE system for measuringcrack propagation in the vicinity of a notch. Evans [14] tested the diffuse field theory with a conicalpiezoelectric AE transmitter and sensor.
The American Society for Testing and Materials (ASTM) issued a standard guide E976-94 fordetermining the reproducibility and checking for degradation of AE sensors [15]. It recommended three
artificial AE sources: an electrically driven ultrasonic sensor, a gas jet and an impulsive source producedby breaking pencil lead. The standard guideline E1106-86 [16] used a step point-force by breaking a
glass capillary against a very large steel block.
To qualify as an AE calibration source in tool wear monitoring, the source should possess similar
characteristics to the AE sources produced in machining, in addition to the also important characteristicof reproducibility. Here, similarity suggests that the comparing sources have rms AE-spectra thatclosely resemble each other in appearance.
The pulsed laser has been frequently used as an artificial AE source in the past two decades [17-20] for anumber of reasons. Firstly, the laser source is broad-band and highly reproducible because the pulse
parameters can be clearly defined and tightly controlled. Secondly, the energy of a laser pulse is readily
quantifiable once the electrical parameters that drive the laser are known. Thirdly, laser can be delivered
to remote locations via optical fibres. However, a pulsed laser is not without its drawbacks: it isexpensive, requires stringent safety consideration and produces low power, hence weak AE, when, bynecessity, operated within the thermo-elastic range so as not to cause damage to the impinged surface.
In many respects, an air jet source is similar to the helium jet source. The advantages of the air jet
source are that it is non-contact, inexpensive, relatively safe, portable and readily available in a machine
shop. The disadvantage is that the behaviour of an air jet in respect of the AE produced is affected by ahost of environmental factors such as temperature and humidity.

197

SIMILARITY COEFFICIENT
An n-point rms discrete AE-spectrum can be thought of as a vector u defining a point in the ndimensional vector space. By analogy with vectors in the three-dimensional space, the length squared ofu is the inner product of u with itself. Thus, the length of u can be computed fromu = u.u =

n

∑u .
2
k

(1)

k =1

This length is the same as the AErms of the signal from which the n-point discrete spectrum is derived.
The vector u can be normalised by dividing its elements by the length of the vector. A normalised
vector, denoted by u , has a unit length.
Given two normalised vectors, u and v , in the n-dimensional space, the included angle θ between themis related to the inner product of u and v as(2)cos θ = u.v .
If the two vectors are identical, then cosθ = 1, whereas if they are orthogonal to each other, meaning thatthe projection of one vector on the other is zero, then cosθ = 0. Since the value of cosθ suggests thedegree of similarity between the two vectors, it is named the similarity coefficient.
AE COMPARISON OF AIR JET, LASER AND MACHINING
Three sets of tests were conducted to compare the shapes of the rms AE-spectra obtained from singlepoint machining, the air jet and the pulsed laser. The repeatability of rms AE-spectra from the air jet andpulsed laser sources was also assessed.
Machining tests
Machining tests were performed with the cutting process variables changing as follows:• Surface cutting speeds from 80 to 150 m/min;• Feed rates from 0.1 to 0.4 mm/rev; and• Depths of cut from 0.3 to 1.0 mm.
The work-piece was made from EN24T (0.35-0.45 % carbon) and measured 63.5-mm diameter by 150
mm length. Tool inserts of type GC 4035 DCMT 11 T3 04 UF and a tool shank of type SDJCL 1616H
11 (Sandvik Coromant) were used. Details of the insert geometry are: cutting edge length 11mm, insert
thickness 3.97mm, insert shape 55°, rake angle 0°, clearance angle 7° and nose radius 0.4 mm.
A broad band AE sensor (125 kHz – 2 MHz) was mounted at the end of the tool holder with silicone
rubber compound. A Hewlett Packard HP 89410A Vector Signal Analyser was used to produce a 401line rms AE-spectrum with frequency from 0 to 1 MHz averaged over 70 consecutive spectra.
Air Jet Tests
As shown in the block diagram of Fig. 1, air from an air supply passed through an air filter, a precision regulator, a precisionpressure gauge, an on/off valve and a nozzle sequentially, emerging as an air jet. The air jet was directed normally at

the top rake surface of the insert, 3 mm from the nose tip and equally distant from the leading andtrailing edges of the insert. The insert was clamped to the tool-holder with a clamping torque of 2 Nmand the tool holder was, in turn, held in a fixture.
198

Air
SupplyAirfilterPrecision
Regulator

Precision
Pressure
Gauge

On/offvalve

AirNozzleAir JetFig. 1. Block diagram of the air jet equipment.
Both the stand-off distance from, and the location of the point of impact on, the rake face were
controlled by micrometers. The measuring instruments and their settings were the same as those for themachining tests. Two resolutions of the frequency spectrum were used, namely 401 and 3201 lines. The
schematic diagram of the AE signal propagation path is shown in Fig. 2.
AE
input

InsertInsert/tool
holdercoupling

ToolholderToolholder/transducer
coupling

SensorAE output

Fig. 2. Schematic diagram showing the signal propagation path of AE in tool wear monitoring.
The tests were performed with two different sizes of nozzle diameters: 1.0 mm and 1.4 mm. The standoff distance was varied from 2 to 16 mm, in increments of 2 mm. The air jet pressure was varied
between 1 and 5 bars, in increments of 1 bar.
Pulsed Laser Test
A pulsed Nd:YAG laser system was used as the laser source. The energy of the laser was such chosenthat it was insufficient to cause damage to the insert. The energy level of the laser was measured with alaser power meter, which registered a value of 3 mJ when the tip of the optical fibre was 2 mm awayfrom the measuring matt black surface. The procedure and the set up of the measuring system were thesame as those for the air jet tests excepting the spectrum resolution, which was 3201 lines.
SIMILARITY OF ARTIFICIAL AND MACHINING AE SOURCES
All rms AE-spectra from the machining tests have similar appearance with the average spectrum asshown in Fig. 3.
Figures 4 and 5 show the typical AE time signals of the air jet and the pulsed laser. The air jet waveform
is continuous whereas the pulsed laser is of burst type. Figures 6 and 7 show the RMS AE-spectra forthe two different artificial sources. It is evident that both the air jet and pulsed laser sources produced
sufficient frequency bandwidth, 100 kHz –500 kHz, for tool wear monitoring purposes but the energy
level of the pulsed-laser source is much lower.
Using the machining RMS AE-spectrum as the reference, its extent of similarity compared to the air-jet
source and the pulsed-laser source, expressed in terms of the similarity coefficients as defined in
Equation (2), are 0.8653 and 0.5604 respectively. This result is to be expected as is apparent from therms AE-spectra of Figs. 3, 6 and 7.

199

700
654.6073 mV @ 115 kHz
600

AErms (mV)

500

400

300

200

100

0
0

1

2

3

4
5
6
Frequency (Hz)

7

8

9

10
x 10

5

Fig. 3. RMS AE-spectrum from machining EN24T with a GC 4035 insert.
1

2

0.8

1.5
1
Amplitude (Volt)

Amplitude (Volt)

0.6
0.4
0.2
0

0.5
0
-0.5

-0.2
-1
-0.4
-1.5

-0.6

-2

-0.8
-1

-2.5
0

0.2

0.4

0.6

0.8
Time (Sec)

1

1.2

1.4
x 10

0

0.2

Fig. 4. Time domain of the air jet.
700

0.4

0.6

-3

0.8
Time (Sec)

1

1.2

1.4
x 10

-3

Fig. 5. Time domain of the laser.
120684.7mV @ 135 kHz

104.8 mV @ 147.5 kHz

600

100

500

AErms (mV)

AErms (mV)

80

400

300

60

40

200
20

100

0

0

1

2

3

4
5
6
Frequency (Hz)

7

8

9

0

10

0

5

x 10

Fig. 6. Power spectrum of the air jet.

1

2

3

4
5
6
Frequency (Hz)

7

8

9

10
x 10

5

Fig. 7. Power spectrum of the laser pulsed.

200

AE AND AIR-JET PRESSURE AT DIFFERENT STAND-OFF DISTANCES

4500

5000

4000

4500

3500

4000
3500

3000
2mm
4mm
6mm
8mm

2500
2000

AErms (mV)

AErms (mV)Using Equation (1), the AErms value of the AE signal was calculated. For the air jet tests, therelationship was established between the AErms and the air-jet pressure at a stand-off distance from 2 to
16 mm, with bore diameters at the nozzle of 1 mm and 1.4 mm. The shapes of the rms AE-spectra at thetwo bore diameters were similar but the peak magnitude was higher for the bore diameter of 1.4 mm.
On the other hand, the 1-mm diameter nozzle produced spectra that had lower variability. Using the 1mm diameter nozzle, the relation between AErms and the air-jet pressure for different stand-off
distances is as shown in Fig. 8. The variability of the AErms, defined as the ±1 standard deviation
divided by the mean, was ±2.62 %.

1500

3000

10mm
12mm
14mm
16mm

2500
2000
1500

1000

1000

500

500

0

0
1

2

3

4

5

1

Pressure (Bars)

2

3

4

5

Pressure (Bars)

Fig. 8. AErms of the air-jet at pressure, 1-5bars, at stand-off distances (a) 2-8 mm, and (b) 10-16mm.
The condition at the stand-off distance of 2 mm and pressure of 2 bars was chosen to show thevariability of the measurements. These were the lowest values amongst the set of stand-off distancesand pressures tested.
Peak amplitude on the rms AE-spectrum with 401-point resolution= ±5.05 %Peak amplitude on the rms AE-spectrum with 3201-point resolution= ±5.84 %AErms from the rms AE-spectrum with 401-point resolution= ±1.14 %AErms from the rms AE-spectrum with 3201-point resolution= ±1.27 %For the pulsed laser tests, the variability of the measurements at the stand-off distance of 2mm and laser
energy of 3 mJ are:Peak amplitude on the rms AE-spectrum with 3201-point resolution= ±2.02%
AErms from the rms AE-spectrum with 3201-point resolution= ±1.92 %It is observed from these results that both artificial sources have similar variability.
AE, AIR JET PRESSURE AND INSERT CLAMPING TORQUE
Air jet tests were conducted to study the effects of different sensor location and of different insert
clamping torque on the AErms. Similar to the air-jet tests in Section 4.2, the air jet was positioned
vertically above the top rake face of the insert 2 mm inwards from both the leading and trailing edges of
the insert, at a stand-off distance of 5 mm. Three pairs of AE sensors were mounted with the first ofeach pair on the tool holder and the second on the tool post, all held in position using a silicone rubbercompound. These were all PAC sensors and the pairs were: WD and WD with response bandwidth of
100 kHz-1 MHz, UT1000 and UT1000 with response bandwidth of 60 kHz-1 MHz, R30 (100-400 kHz)201

and R15 (50-200kHz). The outputs of these sensors were amplified by 60dB and band-pass filteredfrom 20 kHz to 1MHz. The Hewlett Packard HP89410A Vector Signal Analyser was used to produce
an RMS AE-spectrum with 401-point resolution averaged over 70 successive spectra. The insert was
tightened to three levels of torque, namely 0.4 Nm, 1.2 Nm and 2.0 Nm. The air-jet pressure was varied
between 3 and 8 bars in 1-bar increments.
Results showed that the AErms were linearly proportional to the air-jet pressure applied for all levels ofclamping torque. It was also observed that the AErms was the highest at the torque value of 0.4 Nm,therefore suggesting that the AErms was sensitive to the torque applied. The graph for the clamping
torque of 2.0 Nm is as shown in Fig. 9.
2500

2500

2000

AErms (mV)

2000

AErms (mV)

3000

UT1000(P)UT1000(H)R15(P)
R30(H)
WD(P)
WD(H)

1500

WD (P)WD (H)
R15 (P)
R30(H)
UT1000(P)UT1000(H)

1500

1000

1000

500
500

0

0
0

2

4

6

8

0.4 0.6 0.8 1.0 1.2 1.4 1.6 2.0 2.4 2.8 3.2

10Torque (Nm.)

Pressure (bars)

Fig. 9. AErms related to air jet pressure
for different sensors.

Fig. 10. AErms related to clamping
torque at constant pressure of 5

To study the relation between clamping torque and AErms, the air-jet pressure was fixed at 5 bars whilst
the clamping torque was changed from 0.4 Nm to 3.2 Nm using an adjustable torque wrench. The
results, as in Fig. 10, show that the AErms decreases as the torque increases from 0.4 to1.2 Nm and thenremains constant from 1.2 Nm to 3.2 Nm. The ratios of AErms between the different pairs of sensors,
one on the tool holder and the other on the tool post, were calculated for each value of clamping torqueand they are as shown in the table below:
Sensor pair
WD/WD
UT1000/UT1000
R30/R15

Mean of ratios
1.049
0.630
2.223

Standard Deviation of ratios
0.031
0.038
0.076

Variability (%)
2.984
6.093
3.411

CONCLUSION
Compared to the pulsed laser, the air jet is more suitable as an artificial calibration source for measuringsystems used for machining study and tool wear monitoring. This is because the air jet source has an
RMS AE- spectrum more similar to that observed in machining than the pulsed laser, is relative safe touse, is less expensive and is more readily available in a workshop.
For a fixed stand-off distance, the AErms of the air-jet increases linearly with the air-jet pressure. The
clamping torque applied to the insert can affect the AErms if the torque value is low; but when theclamping torque exceeds 1.2 Nm, the AErms remains constant. A safe clamping torque for the tool
holder used in this research is around 2 Nm beyond which there is the risk of damaging the hexagonalhead of the tightening screw.
202

In summary, a calibration procedure may be suggested as follows. With the insert clamping torqueabove 1.2 Nm, the AErms value obtained from a sensor can be converted into an air pressure value using
the calibration graphs such as Figures 8 and 9. In this way, providing a set-up is calibrated using the air
jet source under a prescribed condition, results obtained from different set-ups that have been calibrated
in the same manner can be compared.
ACKNOWLEDGEMENTS
The authors wish to acknowledge support from the INTErSECT Faraday Partnership, the Engineeringand Physical Sciences Research Council and the Royal Thai Government.
REFERENCES(1) E.N. Diei and D.A. Dornfeld, “A model of tool fracture generated acoustic emission duringmachining”, Trans.ASME, Journal of Engineering for Industry, 109 (3) (1989) 229-237.
(2) E. Kannatey-Asibu, Jr. and D.A. Dornfeld, “Quantitative relationships for acoustic emission fromorthogonal metal cutting”, Trans.ASM, Journal of Engineering for Industry, 103 (3) 1981 330-340.
(3) L. Dan and J. Mathew, “Tool wear and failure monitoring techniques for turning-a review”, 1st
J.Mach. Toos Manufact, 30 (4) (1990) 579-598.
(4) R. Teti and D.A. Dornfeld, “Modelling and experimental analysis of acoustic emission from metal
cutting”, Trans. ASME, Journal of Engineering for Industry, 111(3) (1989) 229-237.
(5) M.S. Lan and D.A. Dornfeld, "In-process tool fracture detection", Journal of Engineering Materials
and Technology, 106 (2) (1984) 111-118.
(6) T. Blum and I. Inasaki, “A study of acoustic emission from the orthogonal cutting process”, Trans.
ASME, J. Engineering for industry, 112 (1990) 203-211.
(7) A.E. Diniz, J.J. Liu and D.A. Dornfeld, “Correlating tool life, tool wear and surface roughness bymonitoring acoustic emission in finish turning”, Wear, 152 (1992) 395-407.
(8) J.J. Liu and D.A. Dornfeld, “Modelling and analysis of acoustic emission in diamond turning”,
Journal of Manufacturing Science and Engineering, 118 (1996) 199-206.
(9) T.A. Carolan, S.R. Kidd, D.P. Hand, S.J. Wilcox, P. Wilkinson, J.S. Barton, J.D.C. Jones and R.L.
Reuben, “Acoustic emission monitoring of tool wear during the face milling of steels and aluminium
alloys using a fibre optic sensor”, Part 1: energy analysis, Proc Instn Mech Engrs, 211(1997) 299-309.
(10) K. Iwata and T. Moriwaki, “An application of acoustic emission measurement to in-process sensingof tool wear”, C.I.R.P. Annals, 26 (1977) 21-26.
(11) N.N. Hsu and F.R. Breckenridge, “Characterisation and calibration of acoustic emission sensors”,
Materials Evaluation, 39 (1981) 60-68.
(12) Y. Berlinsky, M. Rosen, J. Simmons and H.N.G. Wadley, “A calibration approach to acoustic
emission energy measurement”, Journal of Nondestructive Evaluation, 10 (1) (1991).
(13) S.L. McBride and T.S. Hutchison, “Helium gas jet spectral calibration of acoustic emission
transducers and system” Canadian journal of Physics, 56 (1978) 504-507.
(14) M. J. Evans, “The use of diffuse field measurements for acoustic emission”, PhD. Thesis, Imperial
College of Science, Technology and Medicine, 1997, 196 P.
(15) The American Society for Testing and Materials (ASTM): Standard guide for determining the
reproducibility of acoustic emission sensor response, E976-94, (1994) 374-379.
(16) ASTM: Standard method for primary calibration of acoustic emission sensors, E1106-86(reapproved 1992), 485-494.
(17) Y. Berlinsky, M. Rosen, J. Simmons and H.N.G. Wadley, “A calibration approach to acoustic
emission energy measurement”, Journal of Nondestructive Evaluation, 10(1) (1991) 1-5.

203

(18) A.M. Aindow, R.J. Dewhurst, D. A. Hutchins and S.B. Palmer, “Laser-generated ultrasonic pulses
at free metal surfaces”, J. Acoust.Soc.Am.69 (2) (1981) 449-455.
(19) C.B. Scruby, R.J. Dewhurst, D.A. Hutchins and S.B. Palmer, “Qualitative studies of thermallygenerated elastic waves in laser-irradiated metals”, J. Appl. Phys., 51(12) (1980) 6210-6216.
(20) S. N. Hopko and I. C Ume, “Laser generated ultrasound by material ablation using fiber optic
delivery”, Ultrasonics, 37 (1999) 1-7.

204

DEVELOPMENT OF AN EQUIPMENT TO MONITORING AND CONTROLTHE QUALITY OF RESISTANCE WELDING
J. CATTY
Technical Center for the Mechanical Engineering Industries (CETIM), Senlis Cedex, FRANCE

ABSTRACTThe resistance-welding process is widely used due to high productivity and economy. It is utilized
strongly in the manufacturing of automotive components and structures. The automotive sector isparticularly demanding in terms of quality standards, productivity levels and automation of productionlines. The process of electrical resistance welding, however, suffers from persistent problems ofinconsistent weld quality caused by variations in material composition, thickness and type of coatings,
electrode wear and electrical supply fluctuation. There is a need to develop robust and reliable
monitoring approaches either to predict or detect when the weld quality is beginning to decrease and/or
defective welds have been produced. The need is to have a relatively low cost system that will
accurately “verifies” the state of weld quality, assuring weld quality in real time.
The aim of the present project is to develop a low cost and robust prototype able to monitor and control
the quality of resistance spot welding in industrial environment. Previous results have shown that thetechnique of acoustic emission (AE) is potentially very interesting, but in some industrial cases it gaveless pertinent results. So, if the AE alone does not allow a reliable diagnosis in all configurations of
welding, a multi-parameter approach seems to be a promising way. Based on these considerations, theprototype developed in the present project integrates two different technologies based on the monitoringof the electrical resistance welding parameters and AE control. Some results are presented in this paper.
Craft Project was funded by The European Community and includes as partners,
Incompol (PT), David Valente Almeida (PT), Gametal (PT), TECNA (IT), SOLRES (SP), MSC
Technik (DE), Fleetguard (FR), EMT74 (FR), VPTech (FR), UTEC (PT), ISQ (PT), CETIM (FR).
INTRODUCTION – From the industrial approach to a new concept
One of the basic requirements of a resistance-welding quality standard is that a peel or chisel test must
be conducted at a frequency agreed with the customer. The resistance-weld quality control is usually
carried out by conventional inspection techniques, destructive (metallographic and mechanical testing)and/or non destructive (ultrasonic or X-ray). Meanwhile, the welding conditions are based on tolerance
parameters resulting from empirical/experimental results, some of which can be monitored. This
common industrial approach, similar in the different end-users around Europe, fails to assure quality
products and even leads to defective products. This situation has important implications on the
productivity and cost effectiveness requirements for manufacturers.
The expected project outcome represents an important strategic advantage to both end-users andequipment manufacturers through:• increase in the European industrial competitiveness through its capability to supply quality assured
products,• important savings in raw materials due to significant reductions in the number of destructive tests for
quality control• important contribution in the direction of the development of “intelligent welding machines”.
205

FIELD OF THE STUDY – The coated steels, the main part of the market
When the world of resistance welding is analyzed in terms of volume, it appears that the area of
automotive industry and subcontractors is by far the greatest user of this process. In this area, most of
the used materials are steels and coated steels. Aluminum alloys represent actually a small part of themarket, but seems a promising field of development. The tests performed in this project are mainly
based on coated steels (e.g., galvanized steels). Nevertheless, the concept of the monitoring system isable to be adapted to other welded materials.
METHODOLOGY
The initial concept of the system is to take advantage of the combination of two types of measures(acoustic emission and electrical parameters). In order to achieve this goal, the methodology needs todetermine:• The best measurement parameters (type of sensors, filtering and acquisition),• The most pertinent parameters, extracted and calculated from the signals from all the sensors.
The methodology adopted started with the development of a pre-prototype to be used in the spotwelding tests, flexible enough to support changes of sensors or pretreatment. Then, for each material andthickness, the experimental plan was the following:• Welding with more than 35 different conditions of forces and currents. For each combination ofcurrent and force, obtain 5 shear-stress measures and 3 nugget diameter and 4 more for backup. (Thewelds must be obtained with electrodes in a good condition);• For a selected ideal current and force, welds must be done from initial conditions (“goodelectrodes”) to deteriorated conditions in order to study the influence of electrode deterioration inspot weld quality and measured parameters.
The spot welds produced were evaluated in terms of spot diameter, penetration depth and mechanicalstrength, besides visual inspection.
Finally, a relationship between the recorded real-time parameters and the quality parameters (diameter,mechanical strength and explusion phenomenon) was searched to define a quality algorithm to be usedin the prototype software.
TYPICAL RESULT ON STEEL
Experimental conditions
Welding machines
The spot-welding tests used for the data processing have been performed in laboratory environment,with TECNA Machines. Theses machines, a welding press and a welding gun, are representative of
the most commonly used machines in the automotive industry (the welding gun for robots) and thesubcontractors of this industry.
The Prototype
The prototype developed in the frame of the project (see photo 1) is based on a Compact-PCI industrial
Computer (PXI system from National Instruments) equipped with an acquisition board. A specificinterface board was developed, that ensures the following functions: Adaptation of the signals; Powersupply of sensors if needed; Pre-treatment.
206

Photo 1: Preprototype.

The sensors specified in the definition of the prototype have been installed on the welding machine:• Current Sensor: a toroid coil.
• Voltage Sensor: clamps fixed as closed as possible to the weld.
• Acoustic Sensor: a specific sensor developed for this application. It needs to be insensitive to theelectromagnetic interference from the machine.
• Pressure or Force Sensor: a specific sensor was used for this application that enables us to measure
the real force applied to the parts.
Steps in the Data Processing
The aim of the data processing is to extract all the pertinent information from the real-time parameters,
calculated from the signals coming from the sensors. In order to reach this goal, the data processing is
divided into three steps:• Step 1: Correlation between the parameters: In order to minimize the number of parameters used in
the algorithm, we evaluate the correlation between all the parameters. When the correlation
between two parameters is near unity, it means that the two parameters bring exactly the same
information. Then, we can suppress one of the two parameters.
• Step 2: The prediction of the quality of the weld is divided into two objectives:• Prediction of the diameter of the (weld) point.
• Prediction of the mechanical strength.
• Step 3: Prediction of the expulsion phenomenon.
Application on an Electro-Zinc SteelCorrelation between the parameters
This step allows us to suppress some parameters. After this step, we obtain less than 10 informative
parameters.
207

Prediction of the diameter of the point
In order to find the relation between the real time parameters and the diameter or mechanical strengthof the point, we used a multiple linear regression algorithm. The result of the multiple linear regression
performed to find the diameter of the point is represented in Fig. 1. This is a correlation diagram: on the
X-axis, we found the predicted value; on Y-axis, the real (measured) values. The algorithm gives a
global good prediction. An ideal prediction should put all the points on the line. Some points are placedout of the confidence ellipse. The pertinent parameters (used in the prediction) are linked to thewelding stage.

Val. Prévues vs. Observées
Var. Dépendante : DIAM_MM
12

Ideal Prediction line
10

8

6

4

Val. Observées

20« good weld »
-2
-2

0

2

4

6

8

10

12

Régression
IC à 95%

Valeurs Prévues

Fig. 1: Result of the Multiple Linear Regression –– Diameter of the Point.
Prediction of the Mechanical Strength of the Weld
We use the same multiple linear regression algorithm to find the relation between the real timeparameters and the mechanical strength of the weld. The result of the process is represented in Fig. 2.
The pertinent parameters (used in the prediction) are also linked to the welding stage. The algorithm
gives a better prediction for the mechanical strength than the prediction for the diameter of the point.
Interpretation• This behavior has been observed in most of the cases studied (with different type of materials andthickness). It should mean that the real-time parameters are more linked to the mechanical strengththan to the diameter. Thereby, we can suppose that the information included in the parameters islinked to the physical phenomenon of welding.

208

Val. Prévues vs. Observées
Var. Dépendante : EFFORT_A
800

Ideal Prediction line
700

600

500

400

300

Val. Observées

200

100

« good weld »
0
0

100

200

300

400

500

600

700

800

Régression
IC à 95%

Valeurs Prévues

Fig. 2: Result of the Multiple Linear Regression - Mechanical strength of the point.
•

We observe that the correlation seems to be non-linear, and more accurate for the points with highmechanical strength value. Although it seems hard to establish a direct relation between this remark
and the process itself, we can assume that the resistance-welding process is often more reproducible
in its «good working area» than for the «bad conditions». Then, the accuracy of the prediction is acriterion of good welding condition.

Prediction of the Expulsion phenomenon
In order to control the quality of the resistance welding process, we need to detect the expulsion
phenomenon. The system should give an answer – yes (expulsion) or no (no expulsion) -. The answer
is only a binary value, 0 or 1. Then, the algorithm used is different from the one used to predict thediameter or mechanical strength. For this case, we use a discriminant analysis. This analysis is based on
the same real time parameters than the previous analysis. The final result of the classification is given
in Table 1.
Table 1: Final result of the discriminant analysis
Matrice de Classif. (incexp.sta)
Lignes: classifications observ.
Colonnes: classifications prévues
Total

G_1:0

G_2:1

Correct

p=.53924

p=.46076

G_1:0

96.24

205

8

G_2:1

96.70

6

176

%

96.46

211

184

209

The global rate of good classification is high (96 %). It means that 96 % of the welds without expulsion
are well classified (6 % of welds without expulsion are declared with expulsion). Moreover, 97 % of
the welds with expulsion are well classified (3 % of welds with expulsion are declared without
expulsion). The high rate of good classification is very promising, and industrially available.
CONSEQUENCE ON THE DEVELOPMENT OF THE PROTOTYPE
From the tests performed on different type of materials, we observe that the results are very promising,but we note some differences, in term of quality of prediction and also in term of type of pertinent
parameters used in the prediction.
From a general point of view, the quality of the prediction is better for the strength of the point than the
diameter of the point. We can clearly observe that the relation is more linear and the prediction moreaccurate for the mechanical strength parameter. It seems to be an important result that shows that theprediction is really linked to the welding phenomenon: we can easily acknowledge that the parameter«Mechanical Strength» is more representative of the weld nugget than the parameter «Diameter of thepoint».
We can make another remark concerning the prediction results: the prediction is always more accurate
for the welds with a correct diameter or mechanical strength. We observe that the points are very close
to each other at the top of the curve and very scattered at the bottom of the curve («bad points»). Froma theoretical point of view, it seems natural: it is well-known that good conditions of process (welding)are always more reproducible than bad conditions of process.
Concerning the pertinent parameters that allow the prediction, for each configuration, the relation isdifferent in term of parameter and coefficient. However, this study allows us to show that the pertinent
parameters that control or characterize the resistance-welding process are:• Electric parameters,• Load parameter and• Acoustic parameters.
The monitoring system needs to have the information from all these sensors.
All these results show that the mathematical analysis of the real-time parameters gives pertinent
information. The result of the prediction (diameter, mechanical strength or expulsion) is even higher
than a direct analysis based on only one parameter.
We need next to consider the industrial transposition of this type of classification: The final relationthat predicts the diameter, mechanical strength or expulsion requires several preliminary tests, becauseof the statistical nature of the data treatment. Actually, we don’t know if a universal relation exists,
independent of the nature of the welding machine or type of sheet, or shape, or thickness, but we arestill searching. Nevertheless, the results achieved in this study will be exploited in a first «industrial
prototype» that will be tested in industrial environment by industrial partners.
ACKNOWLEDGEMENT
This CRAFT project is funded by all the industrial partners and the European community.

210

A Study of Small HSDI Diesel Engine Fuel Injection Equipment Faults using
Acoustic Emission.
J.D. Gill*, R.L. Reuben*, J.A. Stee1*, M W Scaife~ and J Asquith~*Dept. of Mechanical and Chemical Engineering, Heriot-Watt University, Edinburgh.
~ Perkins Engines Co. Ltd., Peterburgh

SUMMARY
Acoustic emission measurements have been carried out on a Perkins T1004-4 high-speed, direct
injection diesel engine. This is a turbo-charged four-cylinder engine of approximately 1 litre percylinder. The AE sensor was attached to the injector body of cylinder four. By examining the raw AE
signal acquiredfrom this transducer during the injection and combustion period it has been possible todescribe the effectiveness offuel injection ofdiesel into the combustion chamber. These measurements
have also demonstrated the possibility ofapplication to otherfactors affecting the combustion process.

ABSTRACT
Cylinder pressure, needle lift pressure and high pressure injection line pressures (at both pump andinjector) have been acquired over the complete operating range of a Perkins TI000-4 turbo-charged,
four-cylinder high speed direct injection (HSDI) diesel engine. The engine is fitted with Perkins highswirl 'Fastram' piston crowns and Bosch KD fuel injectors operated by a Bosch EPVE rotary fuelpump.
The work comprises a detailed study into the effect injector discharge pressure has on the acoustic
emission (AE) response of the injector body. Two different fault conditions have been examined;
injector discharge pressure reduced from 258 bar to 165 bar in all cylinders and injector dischargepressure reduced to 100 bar in cylinder 4 (instrumented cylinder). These two faults have been
compared with the acoustic emission obtained under normal running conditions.
A comparison was also made with similar studies using vibration measurement, and it was found thatthe use of AE is more reliable at higher engine speeds and loads. This is due to the fact that theacceleration signal from the injectors suffers from severe interference caused by high amplitude, lowfrequency vibrations from structural resonances within the engine, specifically at high engine speed andload.

INTRODUCTION
Arguably the most influential component of the diesel engine is the fuel injection equipment (FIE);
even minor faults can cause a major loss of efficiency and an increase in engine emissions and noise.
The FIE requires the tightest of manufacturing tolerances and effective combustion is very dependent
on the successful operation of each of the component parts. With increased sophistication (e.g. higher
injection pressures, pulse injection and electronic control) being required to meet continuously
improving noise, exhaust smoke and gaseous emissions regulations, the FIE is becoming even more
susceptible to failure. This is mainly as a result of the ingress of particles into the system causing
excessive wear and, for this reason, it is no surprise that injection systems have been shown to be thelargest contributing factor to diesel engine failure [1]. Methods for successfully measuring theeffective operation of the FIE have invariably relied upon the use of intrusive transducers to measure a
related quantity, such as fuel injection pressure or cylinder pressure [2]. Some work has been
undertaken to examine the use of vibration monitoring of the injector body (3) with the aim of using its
response to diagnose the injector condition.
The largest problem with FIE is that small faults can easily manifest themselves without the end user
being able to attribute these directly to the injection system [4,5]. This can be because they are notimmediately noticeable or because the fault is so small that it does not necessarily show itself
immediately in terms of a significant loss of power or increase in emissions. This generally happens
with long-term wear and can allow damage to occur to other fimdamental parts of the engine.
Both the vibration, and the significantly higher fi;equency acoustic emission responses of the injector
are likely to contain valuable information pertaining to the overall condition of the injection system.
EXPERIMENTAL SET-UP
The test engine was a Perkins TI004-4 turbo-charged, four cylinder high speed direct injection (HSDI)
engine developing 135 bhp at 2500 RPM. The engine was mounted on a skid and attached to a Froude
eddy-current dynamometer to control engine load and speed. The engine was fitted with a BoschEPVE rotary fuel pump and KD fuel injectors and was fully instrumented with injector pressure, incylinder pressure and needle lift transducers on the cylinder being examined (cylinder four) as shownin Figure 1. These were complemented by an array of acoustic emission (AE) transducers andaccelerometers. All of the above transducers were synchronized with the angular position of the engine
using a one-pulse-per-degree shaft encoder. After acquisition, the signal was re-sampled with respect
to the shaft encoder signal to remove any effects of fluctuations in engine speed during the cycle. The
AE sensors used were a combination of Physical Acoustics types D9203 and R30I. The signals fromthe transducers were bandpass filtered between 0.1 and I MHz and the rms AE signals were averaged
at 25 IlS and sampled using a Microstar DAP 3000a/212 data acquisition board. The raw signals were
sampled at 5 MHz so that after re-sampling of the signal it was possible to obtain a minimum resolutionof2.5 MHz to prevent aliasing of the signal.
Kistler Injector
Discharge Pressure
SensorNeedle Lift
Transducer

Cylinder 4 Injector &Physical Acoustics
R30I AE Sensor(Removed for clarity)

Physical Acoustics
09203 AE Sensor

Kistler In-Cylinder
Pressure
Transducer

B&K4334
Single Axis
Accelerometer

Figure 1: Perkins TlOO4 Cylinder Head Set-up

Raw and rms acoustic emission measurements were obtained simultaneously with all othermeasurements at engine speeds varying between idle and the full rated speed of 2500 RPM. Five
different load settings were examined, calculated as a percentage of full load at that speed. These were

0%, 25%, 50%, 75% & 100% load. Three different injector discharge pressures were examined tosimulate damaged injector springs.
ACOUSTIC EMISSION MONITORING OF THE INJECTION PROCESS
Figure 2 shows a typical raw AE response from the injector body at 1500 RPM and 50% load for one
full four stroke engine cycle re-sampled with respect to crankshaft position.

1.5

RawAE
Needle Lift

lr1ector Pressure

~

~O.5

~
~O

.

~
~O.5
a.

.

E

f
Injection Cyi. 1

Injection Cyi. 3

"

r

-1

-1.5

Injection Cyl4njection Cyi. 2-2oo-------='90:------:-::,60:----:2=-7o--:-360=--~450:::--------:-7540:----:.=-30----;:;720
Crankshaft Position [Degrees ATOC 1 AringJ

Figure 2
Raw AE Re-sampled wrt Crankshaft Position at 2.5 MHz.
AE Sensor Attached to Injector Body of Cylinder 4

Figure 3
Cylinder 4 Injection and Combustion Period - Raw AE,
Injector Needle Lift and Injection Line Pressure

The injector discharge pressure was set at 165 bar, as opposed to the factory specification of 258 bar,
but is otherwise in good condition. The AE signal can be seen to display four distinct bursts related tothe injection and combustion period of each individual cylinder. The most significant burst is that due
to the injection and combustion in cylinder four, the injector on which the AE transducer was situated.
If the signal obtained from the instrumented injector is examined in more detail, Figure 3, it can be seenthat there is an increase in AE activity at approximately 356 0 after top dead centre (TDC) of the firingstroke of cylinder one. This is believed to be caused by the build-up of diesel pressure in the highpressure pipe-work. This is in contrast to the vibration signal that does not appear to show any signs of
the initial build-up of pressure immediately preceding the lifting of the injector needle.
EXAMINATION OF FAULT CONDITION
Following examination of the normal operating condition, the injector discharge pressure in the
instrumented cylinder (cylinder 4) was reduced to 100 bar to examine the effect this would have on the
AE response. Figure 4 demonstrates the effect reducing injector discharge pressure has on the load
speed curves for the engine. Much work has been carried out investigating, both mathematically andthrough visual examination, the effect injector pressure has on fuel-air mixing in the combustion
chamber. It is agreed that, for an engine of this size, (l litre/cylinder) impingement of the diesel spraywith the wall occurs at between 8.5 0 to 11 0 after initial injection for an engine under full load. The
majority of the air entrainment required for the mixture to reach the near stoichiometric condition
occurs in the wall jet. Therefore, reducing the injector discharge pressure, as well as increasing droplet
size, also increases the period before mixing of the fuel and air begins. The consequence of this is that

by the time the fuel is in a suitable state for ignition the peak cylinder pressure, and therefore
temperature, has begun to fall.
500'-~-~-~-~-~r_~N;:::"'"'=:"'-';:Run="=;",,=='il-Injector Pressure 165 bar-

Reduced Cyl.4 In;. PressLXe

450

'00

300

250

2~

//
1000

1200

1400

1600
1800
2000
Engine Speed [RPM)

2200

2400

2600

Figure 4: Load / Speed Curves for Perkins Tl004-4 Diesel Engine
Condition I: Nonnal Running (Injector Discharge Pressure set at 258 bar)
Condition 2: Injector Discharge Pressure Reduced to 165 bar in all Cylinders
Condition 3: Injector Discharge Pressure Reduced to 100 bar in Cylinder 4

Figure 5 shows cylinder four injection and combustion period for normal running and for cylinder fOUf
injector discharge pressure reduced to 100 bar. The initial point of injection is earlier in the cycle forthe reduced injector pressure and this is borne out in the AE response as the signal can be clearly seen
to rise earlier than for the normal running condition.
Maximum Injector
Needle Opening

1.5r-..:,,-~-~-"'---'---'---r;===;~==::::c:::::;]---+-III-,lL

Faull Condition-

NormalRuming

Detection of
Increase in FuelLine Pressure-1.5-2 '-:-=---::c=-----::38=o--::3=90~-,':::OO~-,4:::10~-::4=20--::4=30--,4=40........J
Angular Position [Degrees AIDC Firing Cyl11

Figure 5: Raw AE from Cylinder 4 Injector Body showing Cylinder 4 Injection & Combustion Period

However, the lower amplitude AE response from the injector body associated with the combustion
process is extended for the fault condition. It is believed that this is a result of the less efficient mixingof the fuel and air. This necessitates a longer period for the adequate entrainment of air with the larger
fuel droplets causing more erratic burning of the fuel.

As the speed increases, the period between fuel injection and combustion increases in tenns of crankangle as the fuel takes a certain time to mix and for ignition to commence. To compensate for this, thefuel pump control automatically advances the start of delivery. Figures 6 a-d show the point ofdetection of the increase in pressure in the fuel line and the point at which the injector needle becomesfully open for four different speed conditions across the range of loads. These were calculated by nnsprocessing of ten raw AE cycles for each condition with an averaging time of 40/1s and then calculating
the mean and standard deviation for each point. As can be seen, the point of fuel line pressure increase
and the maximum opening of the injector nozzle needle detected by the AE transducer is advanced with
reduced injector discharge pressure.

_1500 RPM

1000 RPM

363,,----~---~-----~_____,
x_ u__ uu --u1--------258 ba

363n==-:=::~~=:======~~_3-:~258:J,

362

362

_

--,,{- "---.. _···.. ·.... I165ba.. 1165b~

I-------- -----1--------

t_ nn .__ n _-------n---1 100 ba: 361

I-mmmm-1-------u1

mm n_nmI '00

"360

!

1"59

~

~--- --£-..-_--~---.I.258 b

1358---±--L-T>57--.:] '00';;;-------;c'oo:;;;-------J

356L:------;025,-------C50

~

-

>57

1=-- -I . . --~f-load (Percentage of fullollId]

Figure 6b: AE Detection 1500 RPM

Figure 6a: AE Detection 1000 RPM
2000 RPM

360r-r---~-----=.:.~-----~__,

n_~-.

~ 356 }-------------1------------- --n------n----------------1 1OO ,1~2500 RPM
360r-r---~---r-------~__,_·~"T165~,==,1356~------:O25-------;;5~0 ---------='100~

loed (?-erceriaOll: of flJIload]:E-----·-------_JC_nn

165b.rT~ r:=::::::::-I=:::=::::::::t:::=:::::::::=::=:::~~~'65b~
1
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35a

il356

100 bar

i

!364
.~

~Ii >52- ---.-------0£ 256 b
~-

---T-

_

. _ £ __---"[165t350---+-----f--------T'oo,..L-L---~25--------:5=--0---------,:,oo~Load (Percentage of tulload)

Figure 6c: AE Detection 2000 RPM

J350 I---··_···+-I---+1-,.. 1=~._-=--- _TL--~f _ _==='65=b=larf
1-1'00",1346'-:---~25------O50:-------,-'-,00,------1
load [Percertage of fulload)

Figure 6d: AE Detection 2500 RPM

Detection of Increase in Fuel Line Pressure (solid line)
Detection of Maximum Nozzle Needle Lift (dashed line)

SUMMARY AND CONCLUSIONSThe effect of reducing injector discharge pressure on the AE recorded on the injector body has been
quantified using measurements on a test bed engine. As has already been demonstrated by otherauthors, notably Gu and Ball [3], the injector vibration and stress waves in the injector body are caused
by a combination of mechanical and fluid-mechanical events. These events consist of impacts causedby the needle opening and closing and also by the high-pressure diesel flow through the pipe-work andnozzle. A number of these events are more easily detected using acoustic emission, notably the buildup of pressure in the high pressure pipe immediately prior to the injection process commencing.

Examination of the point of detection of the increase in fuel pressure and opening of the injector nozzle
using AE have been shown to be good descriptors of the injector discharge pressure. Using thismeasure it is possible to detect the nozzle opening pressure of the injector without the need for
expensive and intrusive sensors. The advantage of acoustic emission measurement over vibration
measurement in this type of application is the significantly higher frequency of signal detected. This
overcomes problems associated with engine resonances and low frequency vibrations caused by theoperating environment (e.g. mobile applications, such as vehicles).

FUTHERWORKWork is still being carried out into other fuel injector faults and the possibility of using a small array of
acoustic emission sensors to detect a greater number of engine faults. Acoustic emission has so far
been successfully applied to detect a variety of valve faults, gasket leakage and it has also been possible
to reconstruct the cylinder pressure trace with an accuracy of ±5%.
The largest problem to be overcome in terms of using acoustic emission to detect faults on small diesel
engines is the complexity of the AE signal at high speeds and loads. As can be seen in Figure 1 thesignal acquired by a sensor mounted on one cylinder does not just relate to that cylinder. To overcome
this problem will require some form of filtering or another technique which is currently being
examined. This technique uses the transfer function, calculated using a piezo-pulser as a source, of theindividual machine to eliminate unwanted components ofthe signal.

ACKNOWLEDGEMENTS
The authors gratefully acknowledge the funding provided by the EC under its BRITE-EURAM
programme, Project number BE96 3491.
REFERENCES
[1]

[2]
[3]
[4]
[5]

Roylance, B. J. and Zadeh, A. The Development of Experimental Methods to Measure
Film Deposit Formation on Diesel Engine Pistons, Experimental Methods in Engine
Research and Development '91, IMechE, London, 1991, pp. 67-74.
Shoji, T. Effect of Cycle-to-Cycle Variations in Spray Characteristcs on Hydrocarbon
Emissions in a DI Diesel Engine. JSME: Series B, 1997,40(2),312-319.
Gu, F. and Ball, A.D. Vibration Based Fault Diagnosis in Diesel Fuel Injection System.
Diesel Fuel Injection Systems. IMecheE, London, 1995, pp. 89-98.
Lilly, L. C. R. Diesel Engine Reference Book, 1984, Butterworth & Co. Ltd.
Zahdeh, A. R. Henein, N. A. and Bryzik, W. Diesel Engine Cold Starting: PC BasedComprehensive Heat Release Model: Part 1 - Single Cycle Analysis. ASME: Journal ofEngineeringfor Gas Turbines and Power, 113,464-473.

UNSUPERVISED PATTERN RECOGNITION
OF ACOUSTIC EMISSION FROM FULL SCALE TESTING
OF A WIND TURBINE BLADE
Dimitrios KOUROUSSIS, Athanassios ANASTASSOPOULOS,
Pantelis VIONIS* and Vassilios KOLOVOS*
Envirocoustics S.A., Athens, Greece*
Centre for Renewable Energy Sources, Pikermi, Greece

ABSTRACTIn the present paper, acoustic emission (AE) data obtained during a static test of a 12-m FRP windturbine blade was analyzed and classified into classes using various unsupervised pattern recognition(UPR) techniques. Class criticality was revealed by means of correlation of each class’s activity withthe applied load. Application of location techniques pinpointed the specific areas of activity of eachclass, on the blade. Using the UPR results, a supervised pattern recognition (SPR) method was trained,
based on back propagation neural network, and applied to AE data obtained during a subsequentbiaxial fatigue loading of the same blade. Similar classes appeared at the same regions of the blade, butwere triggered at different phases of the loading cycle. The application of UPR on AE data proved to bea promising tool towards the characterization of damage evolution with monotonic or fatigue loading.
INTRODUCTION
Acoustic emission (AE) monitoring during full scale testing of FRP wind turbine (W/T) blades is arelatively new application. The difficulty in such tests arises mainly from the potentiality of different AE
sources, such as delamination, matrix cracks, fiber breakage, etc., which, sometimes, occur concurrently
and are expected due to the nature of FRP materials [1]. The separability of such material-failure
mechanisms by means of UPR analysis of AE data has been demonstrated in the past [2,3]. However,the sophisticated structural design of modern W/T blades, containing reinforcement spars, internal cells
and skin materials with different stacking sequences, introduces additional complexity in theunderstanding and characterization of the detected flaws. More specifically, particular sections of W/T
blades (e.g. the internal spar ends) exhibit high AE activity, even at low load levels, due to local stressconcentrations, but the corresponding mechanisms do not necessarily signify critical damage.
Nevertheless, the same or another area of the blade might experience a different, more critical damage
mechanism at higher loads, which has to be segregated and evaluated separately. Discrimination of thevarious damage mechanisms on FRP materials by means of conventional AE analysis imposes several
restrictions and difficulties, as it is usually based on the location of the AE sources followed by tedious
analysis of the characteristics of the located AE events. The analysis usually involves two-dimensionalfeature correlation (i.e. correlating two AE features at a time), as it is practically impossible to visualize
the whole feature set. This approach does not always yield a reliable identification of the signatures ofthe various damage mechanisms. Even more so, it is not always possible to isolate a particularmechanism and monitor its activity in time, with increasing load, in order to assess its criticality.
The applicability of conventional AE analysis on full-scale testing of W/T blades was assessed by
Sutherland et al. [4]. Further application of the AE technique proved to be successful in detecting andlocating structural flaws on the blade, at very early stages and before they had become visible [5,6].
However, the necessity to discriminate and analyze the detected flaws by means of their AE features still
exists and becomes even more apparent in the case of full-scale fatigue testing of a W/T blade [7]. The
217

purpose of this work is to separately investigate and compare the various classes of AE data, revealed by
UPR techniques, which appear during static loading. Furthermore, application of neural networks forsupervised classification of data obtained during fatigue loading of the same blade, offers a better insight
into the nature of the existing flaws. Successful recognition of the different FRP damage mechanisms bymeans of their AE signatures could, ultimately, lead to the automation of the classification procedure for
future W/T blade tests.
EXPERIMENTAL SET-UP
For the present work, two different AE tests are considered. The first was a static test performed while
the blade was previously unloaded. Conventional AE analysis has already been performed and has
successfully pinpointed “weak” areas of the blade [5,6]. The second AE test refers to the monitoring of
23 cycles of biaxial fatigue loading at 0.1 Hz, which took place after the blade had undertaken several
millions of fatigue cycles in the laboratory, and a visible crack had developed on the trailing edge of theblade between sensors 6 and 10 (see Fig. 1). All tests took place at the Centre for Renewable Energy
Sources (CRES) Wind Turbine Blades Testing Laboratory. The blade was loaded on a single point bymeans of a hydraulic actuator (two in the case of fatigue testing), and AE data were recorded by a
Physical Acoustics Corporation (PAC), ten-channel “SPARTAN 2000” AE system and the associated,
PC-based data acquisition software. Ten 60 kHz resonant frequency PAC R6I AE sensors, equippedwith a 40dB internal preamplifier, were mounted on the compression (suction) side of the blade. The
sensor positioning and the corresponding AE channel numbers are presented in Fig. 1. Duringacquisition, basic AE signal features were recorded and stored in file by the AE system software (PAC“SP2-LOC”), including channel number, hit arrival time, rise time, counts, energy, duration, amplitude,
average frequency, average signal level and counts-to-peak. During the fatigue test, loads in both flap
and edge directions were measured by the load cells between the blade and the actuators.
In the case of the static test, one hydraulic actuator was mounted underneath the blade and was exerting
force perpendicular to the blade’s surface plane, so that the blade’s pressure side was under tension, as
in real conditions. The load was applied at a flapwise position, 7 m from the blade’s root and at 25%
chord station (Fig. 1), resulting in a triangular distribution of the bending moment along the blade’s axis,
aiming to simulate the maximum operational load.

62.5

F

9

8

7

6

5

4

3

2

125

125

10
1

50

62.5

c/4

(cm)

125

125

100

700

AE sensor position

Fi g . : 1AE sen s o r sitio
p o n a n d c helannunmer
b s Lo. a d a pica
p ltio n pino t .
In the fatigue test, two hydraulic actuators were used, at the same axial position as in the static test, one
exerting force in the flapwise direction and the other in the edgewise direction. Both actuators wereapplying periodic force to the blade at the same frequency (0.1 Hz) and the resulting force had edgewise
and flapwise components, with 90° phase shift, causing the blade to move elliptically.

218

PATTERN RECOGNITION ANALYSIS
Unsupervised pattern recognition (UPR) was applied on the data of the first static test using thecommercially available “NOESIS” pattern recognition and neural networks software for AE
applications. A “first-hit” analysis was followed; i.e., only the first hit of each AE event was used. The
first step before application of UPR was to select a representative set of AE features based on which thedata would be segregated into classes, exhibiting similar characteristics. For that purpose, featurecorrelation analysis [8,9] was performed
for all the available AE features. The
degree of correlation among features,presented in the dendrogram of Fig. 2,
demonstrates very strong correlation forCounts and Duration. Subsequently, UPR
was performed with a reduced set of AE
features, comprising Rise Time, Counts toPeak, Energy, Duration, Amplitude and
Average Frequency. Furthermore, allfeatures were normalized individually to arange from 0 to 1, in order to avoid
biasing the classification towards thefeature exhibiting the highest physicaldimensions.
Initially, UPR was performed using
various clustering algorithms [8-10], to
get indications for the data structure. Then, varying the number of the desired clusters, a parametricstudy was performed using the K-Means and LVQ-Neural Net algorithms, in order to assess the
optimum number of classes by means of discrimination-efficiency heuristic criteria. Local minimizationof the D&B Rij criterion [9-11] occurred at seven classes for both the K-Means and the LVQ clustering
algorithms. Therefore, the respective results were used to separate the data into seven classes.
Fig. 2: Feature Correlation Hierarchy.

Following the clustering performed by
UPR on the static test data, a backpropagation neural network (BPNN)
supervised classifier [8,11] was trained tocorrespond AE hits, from any given dataset, to one of the seven classes (output
layer) based upon the values of their AE
features (input layer). Various trials were
performed changing the number of hidden
layers that connect the input and output
layers and showed that one “hidden” layer(Fig. 3) was adequate. Success of training
was assessed by applying this classifier ona subset of the static test data. The
supervised classification yielded only 1%of misclassified hits in comparison to thedesired UPR classification.

Fig. 3: The BPNN classifier topology.

219

FIRST STATIC LOADING
Figure 4 presents the cumulative AE energy vs. time for classes 4 and 6 with the loading envelope
superimposed. It is observed that these classes exhibit similar activity trends. Comparing Fig. 4 with Fig.
5, which presents the cumulative AE energy vs. time for classes 1, 3, 5, it becomes evident that classes
4, 6 and are the only classes with continuous activity throughout the load holds. At the same time,
although classes 3 and 5 exhibit high total AE energy, they totally cease at the end of the last load hold.
Class 1 presents a burst of AE activity in the last load-hold (also observed in class 3) and then ceases.
Classes 0 and 2 are generally very low in AE energy and inactive during load holds.

Fig. 4: Cumulative AE Energy vs. Time(Classes 4, 6).

Fig. 5: Cumulative AE Energy vs. Time(Classes 1, 3, 5).

Feature correlation can be visualized in Figs. 6 and 7, which are scatter plots of Amplitude vs. Counts(in log scale) for classes 4, 6 and 1, 3, respectively. In these two-dimensional correlation plots, theoverlapping between classes is strong in the low and medium Counts-Amplitude region where classes 4,
and 6 coexist (Fig. 6). In the high-medium region, classes 1, 3 are better separated (Fig. 7).

Fig. 6: Amplitude vs. Counts (Classes 4, 6).

Fig. 7: Amplitude vs. Counts (Classes 1, 3).

220

Fig. 8: Location of classes 4 and 6.

Fig. 9: Location of classes 1 and 3.

Aiming to correlate the different classes of data with particular areas of the blade, the linear location
technique was applied separately for each class. For that purpose, the original data acquisition set of thetest was used, which contained all-hits information. By means of specialized PAC software, this set was
split into seven subsets, one for each class, each subset containing all-hits information only for its
corresponding class. Two linear location groups were defined, one with sensors 1-5 and one withsensors 6-10 (see Fig. 1). Upon application of linear location, it was observed that: (a) Classes 4 and 6
were located mainly in the area between sensors 6 and 10 (see Fig. 8), (b) Classes 1 and 3 were locatedprimarily very close to sensor 1 (see Fig. 9), (c) Class 5 was located right on sensor 5, which means thatit might have been triggered anywhere from sensor 5 towards the load application point, and (d) Classes
0 and 2 gave very few locations.
FATIGUE LOADING
Supervised classification was performed on the fatigue loading first-hit data. To gain a thorough insightas to whether particular classes appear at specific parts of or throughout the loading cycle, Figs. 10 and
11 present two scatter plots of the flapwise load vs. edgewise load. The shape of these plots is a good
representation of the ellipsoidal movement of the blade at the load application point. For the plot of Fig.
10, data classes 4 and 6 were used, while Fig. 11 uses only classes 1 and 3 to generate the plot. Strong
dependence of class activity with load is apparent only for classes 1 and 3. On the contrary, classes 4
and 6 appear more or less throughout the loading cycle.

Fig. 10: Flapwise vs. Edgewise Load (Classes 4,
6)

221

Fig. 11: Flapwise vs. Edgewise Load (Classes 1,
3)

These observations can be summarized for all classes by artificially dividing the loading cycle into four
sections, based on the elliptical shape of the flapwise and edgewise loads correlation. Following theconvention of Fig. 10, the loading cycle was divided to the parts (0 to π/2), (π/2 to π), (π to 3π/2) and(3π/2 to 0), where 0 and π coincide with the center values of the edge load and π/2 and 3π/2 with thecenter values of the flap load. Table 1 summarizes the number of events per class, encountered on eachsection of the loading cycle.

1
2
3
4

Section0 to π/2
π /2 to ππ to 3π/2
3π/2 to 0

Class 0 Class 1 Class 2 Class 3 Class 4 Class 5 Class 6
169
25
166
122
512
132
1231
36
52
103
37
168
80
302
59
23
43
27
177
10
337
131
92
19
377
86
976
Table 1: Number of events of each class, per loading cycle section.

Total
2357
778
676
1681

Very appealing on Table 1 are the strong variations of each class’s total events among the sections of theloading cycle. In total, the vast majority of AE is produced at the lower part of the cycle (sections 1 and
4). This trend is followed by all classes except for Classes 1 and 3, which seem to be very concentrated
only on sections 2 and 1, respectively.
CONCLUSIONS
Unsupervised pattern recognition on AE data obtained during the first static loading of a W/T blade
yielded seven classes of data. Class criticality was assessed and it was demonstrated that classes 4 and 6
were the only active classes during the load hold periods. Although classes 1, 3 and 5 were more“intense” in terms of their AE features, their AE activity was rather occasional during static loading withsudden bursts of emission and emission during load increases. Further application of linear location
indicated that different classes mainly originated from different areas of the blade. More specifically,classes 1 and 3 were located very close to sensor 1 where the internal spar ends. Classes 4 and 6 werelocated between sensors 6 and 10. In this area, although there were not any visual indications after the
first static test, a crack was developed upon further application of several millions of fatigue cycles on
the blade. Finally, class 5 was located on sensor 5 and it is believed to be associated with the skindamage that was observed at the load application point.
Based on the results of the UPR on the static test, supervised classification was performed on AE dataobtained during fatigue testing of the same blade. Most classes exhibited strong periodicity and wereactive at particular sections of the loading cycle.
It is worth noting that segregation of the different classes, which was successfully performed with UPR,
would be almost impossible with traditional AE analysis, because of the strong overlapping of theclasses in any two-dimensional space defined by any two of the AE features. On the other hand,
extracting first-hit information from the located AE sources still would not suffice, as one location of theblade might be associated with more than one damage mechanisms. Even more apparent is the need forpattern recognition in cases of fatigue loading since the complexity of the AE analysis increases.

222

REFERENCES[1] Adams, R.D., Cawley, P., "A review of defect types and NDT techniques for composites and bonded
joints", NDT International, Vol. 21, 1988, 207-222.
[2] Anastassopoulos, A.A., Philippidis, T.P., “Pattern Recognition Analysis of AE from Composites”,
Proceedings of the 23rd European Conference on AE Testing, Vienna, 6-8 May 1998, pp. 15-20.
[3] Anastassopoulos, A.A., Vahaviolos, S.J., Lenain, J.C., “Signature Recognition Of Acoustic Emission
From FRP Structures”, Proceedings. 7th ECNDT, Copenhagen, May 1998, pp. 2295-2302.
[4] Sutherland, H., Beattie, A., et al., “The Application of Non-Destructive Techniques to the Testing ofa Wind Turbine Blade”, Sandia National Labs report, SAND93-1380.UC-261, U.S.A., June 1994.
[5] Vionis, P., Anastassopoulos, A.A., Kolovos, V., Kouroussis, D.A., “Non-Destructive Testing of Full
Scale W/T Blade by Means of Acoustic Emission Monitoring During Controlled Static Loading”,
European Wind Energy Conference and Exhibition, Nice, March 1-5, 1999.
[6] Anastassopoulos, A.A., Kouroussis, D.A., Vionis, P., Kolovos, V., “Acoustic Emission NDE of aWind Turbine Blade during Full - Scale Testing”, Insight, Vol. 41, No. 6, June 1999.
[7] Kouroussis, D.A., Vionis, P., Anastassopoulos, A.A., Kolovos, V, “Acoustic Emission Monitoring ofa 12m Wind Turbine FRP Blade During Static and Fatigue Loading”, Proceedings, 2nd International
Conference on Emerging Technologies in NDT, May 24-26, 1999, Athens, Greece.
[8] Envirocoustics A.B.E.E., “Noesis Reference Manual”, rev. 0, October 1999.
[9] Anastassopoulos, A.A., Philippidis, T.P., “Clustering Methodologies for the evaluation of AE from
Composites”, J. of Acoustic Emission, Vol. 13(1/2), 1995, 11-21.
[10] Philippidis, T.P., Nikolaidis, V.N., Anastassopoulos, A. A., “Damage Characterisation of C/Claminates using Neural Network Techniques on AE signals”, NDT&E International, Vol. 31(5), 1998,
329-340.
[11] Anastassopoulos, A.A., Nikolaidis, V.N. and Philippidis, T.P., "A Comparative study of Pattern
Recognition Algorithms for Classification of Ultrasonic Signals", Neural Computing & Applications,
1999, Vol. 8, 53-61.

223

UNSUPERVISED PATTERN RECOGNITION
OF ACOUSTIC EMISSION FROM FULL SCALE TESTING
OF A WIND TURBINE BLADE
Dimitrios KOUROUSSIS, Athanassios ANASTASSOPOULOS,
Pantelis VIONIS* and Vassilios KOLOVOS*
Envirocoustics S.A., Athens, Greece*
Centre for Renewable Energy Sources, Pikermi, Greece

ABSTRACTIn the present paper, acoustic emission (AE) data obtained during a static test of a 12-m FRP windturbine blade was analyzed and classified into classes using various unsupervised pattern recognition(UPR) techniques. Class criticality was revealed by means of correlation of each class’s activity withthe applied load. Application of location techniques pinpointed the specific areas of activity of eachclass, on the blade. Using the UPR results, a supervised pattern recognition (SPR) method was trained,
based on back propagation neural network, and applied to AE data obtained during a subsequentbiaxial fatigue loading of the same blade. Similar classes appeared at the same regions of the blade, butwere triggered at different phases of the loading cycle. The application of UPR on AE data proved to bea promising tool towards the characterization of damage evolution with monotonic or fatigue loading.
INTRODUCTION
Acoustic emission (AE) monitoring during full scale testing of FRP wind turbine (W/T) blades is arelatively new application. The difficulty in such tests arises mainly from the potentiality of different AE
sources, such as delamination, matrix cracks, fiber breakage, etc., which, sometimes, occur concurrently
and are expected due to the nature of FRP materials [1]. The separability of such material-failure
mechanisms by means of UPR analysis of AE data has been demonstrated in the past [2,3]. However,the sophisticated structural design of modern W/T blades, containing reinforcement spars, internal cells
and skin materials with different stacking sequences, introduces additional complexity in theunderstanding and characterization of the detected flaws. More specifically, particular sections of W/T
blades (e.g. the internal spar ends) exhibit high AE activity, even at low load levels, due to local stressconcentrations, but the corresponding mechanisms do not necessarily signify critical damage.
Nevertheless, the same or another area of the blade might experience a different, more critical damage
mechanism at higher loads, which has to be segregated and evaluated separately. Discrimination of thevarious damage mechanisms on FRP materials by means of conventional AE analysis imposes several
restrictions and difficulties, as it is usually based on the location of the AE sources followed by tedious
analysis of the characteristics of the located AE events. The analysis usually involves two-dimensionalfeature correlation (i.e. correlating two AE features at a time), as it is practically impossible to visualize
the whole feature set. This approach does not always yield a reliable identification of the signatures ofthe various damage mechanisms. Even more so, it is not always possible to isolate a particularmechanism and monitor its activity in time, with increasing load, in order to assess its criticality.
The applicability of conventional AE analysis on full-scale testing of W/T blades was assessed by
Sutherland et al. [4]. Further application of the AE technique proved to be successful in detecting andlocating structural flaws on the blade, at very early stages and before they had become visible [5,6].
However, the necessity to discriminate and analyze the detected flaws by means of their AE features still
exists and becomes even more apparent in the case of full-scale fatigue testing of a W/T blade [7]. The
217

purpose of this work is to separately investigate and compare the various classes of AE data, revealed by
UPR techniques, which appear during static loading. Furthermore, application of neural networks forsupervised classification of data obtained during fatigue loading of the same blade, offers a better insight
into the nature of the existing flaws. Successful recognition of the different FRP damage mechanisms bymeans of their AE signatures could, ultimately, lead to the automation of the classification procedure for
future W/T blade tests.
EXPERIMENTAL SET-UP
For the present work, two different AE tests are considered. The first was a static test performed while
the blade was previously unloaded. Conventional AE analysis has already been performed and has
successfully pinpointed “weak” areas of the blade [5,6]. The second AE test refers to the monitoring of
23 cycles of biaxial fatigue loading at 0.1 Hz, which took place after the blade had undertaken several
millions of fatigue cycles in the laboratory, and a visible crack had developed on the trailing edge of theblade between sensors 6 and 10 (see Fig. 1). All tests took place at the Centre for Renewable Energy
Sources (CRES) Wind Turbine Blades Testing Laboratory. The blade was loaded on a single point bymeans of a hydraulic actuator (two in the case of fatigue testing), and AE data were recorded by a
Physical Acoustics Corporation (PAC), ten-channel “SPARTAN 2000” AE system and the associated,
PC-based data acquisition software. Ten 60 kHz resonant frequency PAC R6I AE sensors, equippedwith a 40dB internal preamplifier, were mounted on the compression (suction) side of the blade. The
sensor positioning and the corresponding AE channel numbers are presented in Fig. 1. Duringacquisition, basic AE signal features were recorded and stored in file by the AE system software (PAC“SP2-LOC”), including channel number, hit arrival time, rise time, counts, energy, duration, amplitude,
average frequency, average signal level and counts-to-peak. During the fatigue test, loads in both flap
and edge directions were measured by the load cells between the blade and the actuators.
In the case of the static test, one hydraulic actuator was mounted underneath the blade and was exerting
force perpendicular to the blade’s surface plane, so that the blade’s pressure side was under tension, as
in real conditions. The load was applied at a flapwise position, 7 m from the blade’s root and at 25%
chord station (Fig. 1), resulting in a triangular distribution of the bending moment along the blade’s axis,
aiming to simulate the maximum operational load.

62.5

F

9

8

7

6

5

4

3

2

125

125

10
1

50

62.5

c/4

(cm)

125

125

100

700

AE sensor position

Fi g . : 1AE sen s o r sitio
p o n a n d c helannunmer
b s Lo. a d a pica
p ltio n pino t .
In the fatigue test, two hydraulic actuators were used, at the same axial position as in the static test, one
exerting force in the flapwise direction and the other in the edgewise direction. Both actuators wereapplying periodic force to the blade at the same frequency (0.1 Hz) and the resulting force had edgewise
and flapwise components, with 90° phase shift, causing the blade to move elliptically.

218

PATTERN RECOGNITION ANALYSIS
Unsupervised pattern recognition (UPR) was applied on the data of the first static test using thecommercially available “NOESIS” pattern recognition and neural networks software for AE
applications. A “first-hit” analysis was followed; i.e., only the first hit of each AE event was used. The
first step before application of UPR was to select a representative set of AE features based on which thedata would be segregated into classes, exhibiting similar characteristics. For that purpose, featurecorrelation analysis [8,9] was performed
for all the available AE features. The
degree of correlation among features,presented in the dendrogram of Fig. 2,
demonstrates very strong correlation forCounts and Duration. Subsequently, UPR
was performed with a reduced set of AE
features, comprising Rise Time, Counts toPeak, Energy, Duration, Amplitude and
Average Frequency. Furthermore, allfeatures were normalized individually to arange from 0 to 1, in order to avoid
biasing the classification towards thefeature exhibiting the highest physicaldimensions.
Initially, UPR was performed using
various clustering algorithms [8-10], to
get indications for the data structure. Then, varying the number of the desired clusters, a parametricstudy was performed using the K-Means and LVQ-Neural Net algorithms, in order to assess the
optimum number of classes by means of discrimination-efficiency heuristic criteria. Local minimizationof the D&B Rij criterion [9-11] occurred at seven classes for both the K-Means and the LVQ clustering
algorithms. Therefore, the respective results were used to separate the data into seven classes.
Fig. 2: Feature Correlation Hierarchy.

Following the clustering performed by
UPR on the static test data, a backpropagation neural network (BPNN)
supervised classifier [8,11] was trained tocorrespond AE hits, from any given dataset, to one of the seven classes (output
layer) based upon the values of their AE
features (input layer). Various trials were
performed changing the number of hidden
layers that connect the input and output
layers and showed that one “hidden” layer(Fig. 3) was adequate. Success of training
was assessed by applying this classifier ona subset of the static test data. The
supervised classification yielded only 1%of misclassified hits in comparison to thedesired UPR classification.

Fig. 3: The BPNN classifier topology.

219

FIRST STATIC LOADING
Figure 4 presents the cumulative AE energy vs. time for classes 4 and 6 with the loading envelope
superimposed. It is observed that these classes exhibit similar activity trends. Comparing Fig. 4 with Fig.
5, which presents the cumulative AE energy vs. time for classes 1, 3, 5, it becomes evident that classes
4, 6 and are the only classes with continuous activity throughout the load holds. At the same time,
although classes 3 and 5 exhibit high total AE energy, they totally cease at the end of the last load hold.
Class 1 presents a burst of AE activity in the last load-hold (also observed in class 3) and then ceases.
Classes 0 and 2 are generally very low in AE energy and inactive during load holds.

Fig. 4: Cumulative AE Energy vs. Time(Classes 4, 6).

Fig. 5: Cumulative AE Energy vs. Time(Classes 1, 3, 5).

Feature correlation can be visualized in Figs. 6 and 7, which are scatter plots of Amplitude vs. Counts(in log scale) for classes 4, 6 and 1, 3, respectively. In these two-dimensional correlation plots, theoverlapping between classes is strong in the low and medium Counts-Amplitude region where classes 4,
and 6 coexist (Fig. 6). In the high-medium region, classes 1, 3 are better separated (Fig. 7).

Fig. 6: Amplitude vs. Counts (Classes 4, 6).

Fig. 7: Amplitude vs. Counts (Classes 1, 3).

220

Fig. 8: Location of classes 4 and 6.

Fig. 9: Location of classes 1 and 3.

Aiming to correlate the different classes of data with particular areas of the blade, the linear location
technique was applied separately for each class. For that purpose, the original data acquisition set of thetest was used, which contained all-hits information. By means of specialized PAC software, this set was
split into seven subsets, one for each class, each subset containing all-hits information only for its
corresponding class. Two linear location groups were defined, one with sensors 1-5 and one withsensors 6-10 (see Fig. 1). Upon application of linear location, it was observed that: (a) Classes 4 and 6
were located mainly in the area between sensors 6 and 10 (see Fig. 8), (b) Classes 1 and 3 were locatedprimarily very close to sensor 1 (see Fig. 9), (c) Class 5 was located right on sensor 5, which means thatit might have been triggered anywhere from sensor 5 towards the load application point, and (d) Classes
0 and 2 gave very few locations.
FATIGUE LOADING
Supervised classification was performed on the fatigue loading first-hit data. To gain a thorough insightas to whether particular classes appear at specific parts of or throughout the loading cycle, Figs. 10 and
11 present two scatter plots of the flapwise load vs. edgewise load. The shape of these plots is a good
representation of the ellipsoidal movement of the blade at the load application point. For the plot of Fig.
10, data classes 4 and 6 were used, while Fig. 11 uses only classes 1 and 3 to generate the plot. Strong
dependence of class activity with load is apparent only for classes 1 and 3. On the contrary, classes 4
and 6 appear more or less throughout the loading cycle.

Fig. 10: Flapwise vs. Edgewise Load (Classes 4,
6)

221

Fig. 11: Flapwise vs. Edgewise Load (Classes 1,
3)

These observations can be summarized for all classes by artificially dividing the loading cycle into four
sections, based on the elliptical shape of the flapwise and edgewise loads correlation. Following theconvention of Fig. 10, the loading cycle was divided to the parts (0 to π/2), (π/2 to π), (π to 3π/2) and(3π/2 to 0), where 0 and π coincide with the center values of the edge load and π/2 and 3π/2 with thecenter values of the flap load. Table 1 summarizes the number of events per class, encountered on eachsection of the loading cycle.

1
2
3
4

Section0 to π/2
π /2 to ππ to 3π/2
3π/2 to 0

Class 0 Class 1 Class 2 Class 3 Class 4 Class 5 Class 6
169
25
166
122
512
132
1231
36
52
103
37
168
80
302
59
23
43
27
177
10
337
131
92
19
377
86
976
Table 1: Number of events of each class, per loading cycle section.

Total
2357
778
676
1681

Very appealing on Table 1 are the strong variations of each class’s total events among the sections of theloading cycle. In total, the vast majority of AE is produced at the lower part of the cycle (sections 1 and
4). This trend is followed by all classes except for Classes 1 and 3, which seem to be very concentrated
only on sections 2 and 1, respectively.
CONCLUSIONS
Unsupervised pattern recognition on AE data obtained during the first static loading of a W/T blade
yielded seven classes of data. Class criticality was assessed and it was demonstrated that classes 4 and 6
were the only active classes during the load hold periods. Although classes 1, 3 and 5 were more“intense” in terms of their AE features, their AE activity was rather occasional during static loading withsudden bursts of emission and emission during load increases. Further application of linear location
indicated that different classes mainly originated from different areas of the blade. More specifically,classes 1 and 3 were located very close to sensor 1 where the internal spar ends. Classes 4 and 6 werelocated between sensors 6 and 10. In this area, although there were not any visual indications after the
first static test, a crack was developed upon further application of several millions of fatigue cycles on
the blade. Finally, class 5 was located on sensor 5 and it is believed to be associated with the skindamage that was observed at the load application point.
Based on the results of the UPR on the static test, supervised classification was performed on AE dataobtained during fatigue testing of the same blade. Most classes exhibited strong periodicity and wereactive at particular sections of the loading cycle.
It is worth noting that segregation of the different classes, which was successfully performed with UPR,
would be almost impossible with traditional AE analysis, because of the strong overlapping of theclasses in any two-dimensional space defined by any two of the AE features. On the other hand,
extracting first-hit information from the located AE sources still would not suffice, as one location of theblade might be associated with more than one damage mechanisms. Even more apparent is the need forpattern recognition in cases of fatigue loading since the complexity of the AE analysis increases.

222

REFERENCES[1] Adams, R.D., Cawley, P., "A review of defect types and NDT techniques for composites and bonded
joints", NDT International, Vol. 21, 1988, 207-222.
[2] Anastassopoulos, A.A., Philippidis, T.P., “Pattern Recognition Analysis of AE from Composites”,
Proceedings of the 23rd European Conference on AE Testing, Vienna, 6-8 May 1998, pp. 15-20.
[3] Anastassopoulos, A.A., Vahaviolos, S.J., Lenain, J.C., “Signature Recognition Of Acoustic Emission
From FRP Structures”, Proceedings. 7th ECNDT, Copenhagen, May 1998, pp. 2295-2302.
[4] Sutherland, H., Beattie, A., et al., “The Application of Non-Destructive Techniques to the Testing ofa Wind Turbine Blade”, Sandia National Labs report, SAND93-1380.UC-261, U.S.A., June 1994.
[5] Vionis, P., Anastassopoulos, A.A., Kolovos, V., Kouroussis, D.A., “Non-Destructive Testing of Full
Scale W/T Blade by Means of Acoustic Emission Monitoring During Controlled Static Loading”,
European Wind Energy Conference and Exhibition, Nice, March 1-5, 1999.
[6] Anastassopoulos, A.A., Kouroussis, D.A., Vionis, P., Kolovos, V., “Acoustic Emission NDE of aWind Turbine Blade during Full - Scale Testing”, Insight, Vol. 41, No. 6, June 1999.
[7] Kouroussis, D.A., Vionis, P., Anastassopoulos, A.A., Kolovos, V, “Acoustic Emission Monitoring ofa 12m Wind Turbine FRP Blade During Static and Fatigue Loading”, Proceedings, 2nd International
Conference on Emerging Technologies in NDT, May 24-26, 1999, Athens, Greece.
[8] Envirocoustics A.B.E.E., “Noesis Reference Manual”, rev. 0, October 1999.
[9] Anastassopoulos, A.A., Philippidis, T.P., “Clustering Methodologies for the evaluation of AE from
Composites”, J. of Acoustic Emission, Vol. 13(1/2), 1995, 11-21.
[10] Philippidis, T.P., Nikolaidis, V.N., Anastassopoulos, A. A., “Damage Characterisation of C/Claminates using Neural Network Techniques on AE signals”, NDT&E International, Vol. 31(5), 1998,
329-340.
[11] Anastassopoulos, A.A., Nikolaidis, V.N. and Philippidis, T.P., "A Comparative study of Pattern
Recognition Algorithms for Classification of Ultrasonic Signals", Neural Computing & Applications,
1999, Vol. 8, 53-61.

223

ACOUSTIC EMISSION PROOF TESTING OF
INSULATED AERIAL MAN-LIFT DEVICES
Athanassios ANASTASSOPOULOS, Apostolos TSIMOGIANIS and Dimitrios KOUROUSSISENVIROCOUSTICS S.A., Athens GREECE.

ABSTRACT
Acoustic emission (AE) testing of insulated aerial man-lift devices has proved successful over the years
and is widely accepted for testing both new and in-service vehicles. Although testing is relatively easy,
data analysis and evaluation is a demanding process, since both metal and composite parts are
simultaneously tested, while, the mechanisms used to manipulate the arm movements are potential noise
sources. The existing standards for such type of testing do not outline pass-fail criteria for eachstructural part and material, and evaluation of the results relies basically on the experience of theanalyst. The results of AE monitoring of five insulated aerial man-lift devises are reported in this work.
Data analysis and evaluation is primarily based on multidimensional sorting and unsupervised patternrecognition techniques, which proved efficient for the discrimination of AE sources. Results fromvarious unsupervised algorithms applied either on the AE feature set, or to its principal component
projection, are discussed. Comparison of the proposed analysis process with traditional data treatment,
is presented, as well, Supervised algorithms and neural networks were successfully trained, using theunsupervised pattern recognition results, and can be used for the classification of AE data from future
tests.
INTRODUCTION
Periodic maintenance of aerial man-lifts is of outmost importance for the safety of personnel and for the
economical operation of the device and the electrical utility. The inspection program usually comprisesof structural integrity and dielectric tests in order to identify problems before they become critical [1].
Acoustic emission (AE) testing of insulated aerial man-lift devices has proved successful over the years
and is widely accepted for testing both new and in-service vehicles [1-3]. Although AE was initiallyused to evaluate the structural integrity of the fiberglass booms [2], its applicability extended to theinspection and location of damage in the metal components as well. Through the years, AE has beenused in identifying flaws in the new device, resulted from the manufacturing process, and in-service
damage resulted from accidents, overload, fatigue and environmental conditions. AE testing of
insulated aerial devices has become standard procedure for industries [1-3], supported by relevant
standards [4,5]. However, the existing standards for such type of testing do not outline pass-fail criteria
for each structural part and material, and the evaluation of results relies basically on the experience of
the analyst. Furthermore, major industrial users [3] point out the variation in AE behavior between unitsof different design from different manufacturers.
The results of AE monitoring of five insulated aerial man-lift devices are reported in this work. Data
analysis and evaluation is primarily based on multidimensional sorting and unsupervised patternrecognition techniques [6-8], in order to enhance the analyst efficiency in discriminating the different
AE sources. Results from various unsupervised algorithms applied either on the AE feature set, or to its
principal component projection are presented. Principal component projection proved to be an efficient
pre-processing tool in pattern recognition analysis of AE data. Further automation of the evaluation
process is achieved by neural networks [8,9], which proved suitable for classifying AE data and can beused to evaluate AE activity from future tests of similar devices.
224

EXPERIMENTAL PROCEDURE
The aerial devices were loaded to 1.5 times the rated capacity.
According to the guidelines of ASTM [4], two loading cycle were
performed. The load was applied with the device in the position
shown in Fig. 1. Thirteen (13) PAC-R15I 150KHz resonant sensors
with 40-dB integral preamplifier were mounted at the positions
shown in Fig. 2. Channels 1 to 5 were attached to the composite or
insulated parts, while the remaining channels to the metal parts. A
PAC Spartan-2000 AE system was used for real time dataacquisition. Acquisition was performed at 40 dB threshold and 23
dB gain.
Ch. 7.
BetweenBolts

Ch. 4.
500 mm from
FRP endCh. 3.
1500 mm fromch. 2 and 4.

Fig. 1: Test Position.

Ch. 2.
500 mm from
FRP edge

Ch. 6.

Ch. 1.

Ch. 8.
On metal

Ch. 5.
Middle of FRP

Ch. 10.
50 mm
From edge

Ch. 9.
Between weld
and Bolt
Ch. 11.
BetweenBolts
Ch. 12 ,13.
Base Centre

Fig. 2: Sensors position and overall assembly of the device.
Attenuation study confirmed the adequate coverage of the structure with AE sensors. Pencil breaks wereused as simulated sources at various locations of the device, in order to measure arrival time differences
for location purposes. ASTM [4] guidelines were used for immediate test termination in case of extreme
damage. As indicated in ASTM, these criteria are not acceptance criteria.
DATA ANALYSIS
Conventional data analysis and evaluation is usually [3-5] based on two different thresholds, named“low level” and “high level” threshold, with values of 40 dB and 70 dB, respectively. The total AE
counts and the number of high threshold events (first hits) during the second loading and second holdperiod are used as evaluation criteria. The main problem associated with data analysis is thediscrimination between genuine emission and the various potential noise sources, such as themechanisms (usually chain and gears) used to manipulate the arm movements and the hydraulic system,
which remain under pressure during testing. Furthermore since both metal and composite parts are
simultaneously tested, signature recognition is becoming more complicated.
225

In the present study, unsupervised pattern recognition [6-8], applied on the data recorded from one aerial
device, is used to discriminate the noise sources and establish a procedure for signature recognition of
the structurally significant signal classes. Unsupervised pattern recognition is a numerical processaiming to group a data set of “N” patterns to a limited number of “C” sub-groups, called classes. The
number of classes and their characteristics have to be determined by the unsupervised patternrecognition methodology. Features selection is a critical step in the analysis process and the results
strongly depend on the completeness of feature set.
In the absence of any a priori information on the number of signal classes, features cannot be selectedon the basis of discriminant analysis. Therefore, Amplitude, Duration, Energy (MARSE), Average
Frequency, Rise Time and Counts to Peak, are selected from the eight recorded AE features and used as
the default feature space in order to define a transformation and project the original data on a set oforthogonal axes where maximum variance is achieved [8].
The technique, known as principal component analysis, allows calculation of the “degree of fit” (ascompared to the complete data set) when a subset of the transformed space is used [8]. The first threeprincipal components, named PCA0, PCA1 and PCA2, resulted in a degree of fit of 95.52% and arefurther used for the unsupervised pattern recognition analysis and classifier design.

1

2.5

0.9

2

Tou C riterion

R ij C riterion

Four different clustering algorithms, K-Means, Max-Min Distance, Forgy and Cluster Seeking, wereused for a parametric study in partitioning the AE data in classes ranging from two to twenty-five. The
algorithms performance was evaluated by means of Rij and Tou criteria [6-8]. Both are heuristic criteria
based on the ratio of average within-class distances to the distance between classes. The Rij criterion isan average measure of such a ratio, calculated using all of the different pair of classes, while the Tou
criterion is defined by the ratio of the minimum distance between any pair of classes to the maximum of
the average within-class distances. Therefore, the lower the value of Rij, (or the higher the value ofTou), the higher the discrimination efficiency of the resulting data partition. Furthermore, minimizationof Rij or maximization of Tou criteria as a function of the resulting number of classes can be used toestimate the number of classes in the data [7].

0.8

0.7

1.5

1

0.6

0.5

0.5

0
0

5

10

15

20

0

25

5

10

15

N u m b e r of C la s s e s

N u m b er o f C la s s e sFig. 3: Estimation of Number of Classes by means of Rij and Tou criteria.

226

20

25

Fig. 4: Clustering results in the Principal Component Space.
The results of the parametric study performed with K-Means clustering algorithm are presented in Fig.
3. As can be seen from the figure, Rij criterion is minimized at four classes (a second local minimum
observed at eleven classes), while Tou criterion is maximized for three and four classes. From thenumerical point of view, therefore, the resulting data partition in four classes was considered to be themost representative for the available AE data.
The respective clustering results are shown in Fig. 4, as scatter plot of the first vs. the second principal
components. Figure 5 presents the same clustering results in the original feature space as AE Counts vs.
Amplitude scatter plot.
A closer observation of Fig. 5 indicates an overlap between the resulting classes in the original space.
However, this is an artifact of the specific projection since the clustering algorithms produce clearly
separable classes. On the other hand, the indicated overlap emphasizes the increased efficiency ofmultidimensional analysis and clarifies the limitations imposed by amplitude-based classification withlow and high threshold values.
Experimentation with the remaining clustering algorithms produced similar results. The Max-Min
distance clustering algorithm produced better performance. This algorithm resulted in lower value of Rij
for a seven-class partition. A closer investigation showed that the main classes of the selected K-Meanspartition remained the same, while some of the extreme classes were split. Selection between different
clustering results and the ultimate validity of the resulting data partitions can be performed only in
relation to the physical phenomenon under study. For this purpose, standard AE practices are used.

227

Fig. 5: Clustering results in the Counts-Amplitude projection.
The AE data from each class was
replayed and evaluated by means oflinear and zonal location, as well as
by the activity during the loading
stages. Figure 6 summarizes theresults of class 1. In both hits vs.
time (upper graph) and energy vs.
time (middle graph) plots of Figs. 6
and 7, the load-time curve issuperimposed.

Fig. 6: Activity and location of Class 1 data.
228

Data of class 1, are well located, inthe linear location group (bottom
graph of Fig. 6), with high energy(middle graph presents energy rateof the located events). Furthermore,this class is active only during thefirst loading as can be realized fromthe hits versus time plot. From Fig.
5, the signature of this class comprises of hits, whose amplitude
range from 47 dB to 80 dB. On theother hand, data of class 0, of whichamplitude ranges between 40 dB
and 70 dB, locate between sensors 9
and 10, as can be seen from thebottom graph of Fig. 7. Furthermore, the class is active during all
the different stages of the loading
cycle as can be realized from thehits vs. time graph of Fig. 7. Most
important is the fact that the locatedevents from the linear locationgroup (middle graph of Fig. 7)
appear only during unloading.
AE data of classes 2 and 3 are not
located by the linear location group.
The zonal location results indicatethat data of class 3 is located only
by channels 6 and 7, while data ofclass 2 is located by the channels
associated with the metal parts of
the aerial device. Considering Fig.
5, class 2 has low amplitude andvery few counts. Data of class 3 has
Figure 7: Activity and location of Class 0 data.
amplitude greater than 75 dB and isobserved only in channels 6 and 7. As in the case of class 1, class 3 is active during the first loading,while class 2 has similar behavior with that of class 0; i.e., it remains active during the loading, load
hold and unloading at both cycles.
Summarizing the results, classes 0 and 3 might be characterized as structurally insignificant or friction
or noise related classes, while class 1 and 2 are considered as structurally significant classes.
SUPERVISED PATTERN CLASSIFICATION
Based on the validated results of the previous phase, supervised pattern recognition [8,9] is performed
for the classification of AE data recorded from the remaining four aerial devices as well as for theclassification of AE data recorded in extremely noisy conditions. Two different supervised algorithms
were investigated. The Nearest Neighbor classifier and a Back Propagation (BP) Neural Network with 1
input layer of three neurons, 1 hidden layer of four neurons and the output layer. The nearest neighborclassifier resulted in 1.57% (5 hits) misclassification error, while the BP neural network produced 0.63%(2 hits) misclassification error. The following table summarizes the classification results (AE hits perclass and % of total test hits):
AE data were recorded without load (weight), while the device operator performed standard movementsof the boom to place it to its normal position. Using the location set-up presented in Figs. 6 and 7, theAE data was located between sensors 9 to 11. The classifier assigned 97.3% of the total hits to the noiseclasses (89.7% in class 0 and 8.2% in class 1). The classification results for the case of hydraulic noisewith load, where the device was operated (movements of the boom) with a weight hanging from thebasket. In this case, 4.4% of the data were classified in classes 1 and 3.

229

Table: Summary of the classification results.
Test TypeClass 0Training Data
438 (69%)
AE aerial 2
210 (76.6%)
AE aerial 3
650 (72.7%)
AE aerial 4
386 (72%)
AE aerial 5714 (74.7%)
Hydraulic Noise without 6864 (89.7%)
load
Hydraulic Noise with load 30980 (88.1%)(weight manipulation)

Class 1103 (16.2%)
29 (10.6%)
150 (16.8%)
76 (14.2%)
116 (12.1%)
20 (0.3%)

Class 244 (6.9%)32 (11.1%)
79 (8.8%)
50 (9.3%)
84 (8.8%)625 (8.2%)

Class 350 (7.9%)
3 (1.1%)15 (1.7%)
24 (4.5%)
42 (4.4%)
142 (1.9%)

817 (2.3%)2656 (7.6%)

724 (2.1%)

The number of hits classified in class 1 and class 3 can be used as evaluation criteria for the assessmentof the structural integrity of the tested aerial device. At this point, it is worth noting that the device
number 3 (indicated as “AE aerial 3” test), during the first loading cycle was accidentally loaded to load
higher than 1.5 times the rated capacity. The overloading resulted in the highest number of hits classified
in class 1 (and the highest percentage) among all the devices tested.
DISCUSSION AND CONCLUSIONS
Unsupervised pattern recognition was used to characterize AE data recorded during the testing of fiveaerial man-lift devises. The analysis procedure resulted in four signal classes, two of which wereconsidered to be noise. Comparison of the proposed analysis process with traditional data treatment,
highlights the limitation of data evaluation by means of low and high amplitude thresholds.
Supervised algorithm, back propagation neural network, was successfully trained, using the results fromunsupervised pattern recognition. It is worth mentioning that the high classification performance in caseof hydraulic noise was achieved.
The analysis technique proved effective and can be used for the classification of AE data from future
tests. In order to establish pass/fail evaluation criteria based on the recorded AE hits from each class,further experimentation is necessary in order to enrich the training set with additional AE sources.
Verification of these sources with complementary NDT will allow generalization of the technique.
REFERENCES
1. Kenneth Moore and C.A. Larson, “Aerial equipment requires thorough, regular inspection”, PAC
Technical Report TR-107-2-1/84.
2. J.R. Mitchell and David G. Taggart, “Acoustic Emission Testing of Insulated Bucket Trucks”,
Electric Utility Fleet Management, December/November 1983, pp. 41-50.
3. A.H. Bingham and William C. Veal, “Georgia Power Company Acoustic Emission Test Program”,
ASTM STP-1139, “AE Testing of Aerial Devices and Associated Equipment Used in the Utility
Industries”, 1992, pp. 1-21.
4. ASTM F-914-91, “Standard Test Method for Acoustic Emission for Insulated Aerial Personnel
Devices”.
5. EN-61057: 1993, APPENDIX L (Informative), “Acoustic Emission on Aerial Devices with
Insulated Booms – Test Method”.

230

6. A.A. Anastassopoulos, T.P. Philippidis, “Clustering Methodologies for the evaluation of AE from
Composites “, J. of Acoustic Emission, Vol. 13, Nos. 1/2, 1995, pp 11-21.
7. T. Philippidis, V. Nikolaidis, A. Anastassopoulos, “Damage Characterisation of C/C-laminates
using Neural Network Techniques on AE signals”, NDT&E International, Vol. 31, No. 5, Elsevier
1998, pp. 329-340.
8. Envirocoustics S.A., “NOESIS reference manual”, rev. 0, October 1999.
9. A.A. Anastassopoulos, V.N. Nikolaidis and T.P. Philippidis, "A Comparative study of Pattern
Recognition Algorithms for Classification of Ultrasonic Signals", Neural Computing &Applications, 1999, Vol. 8, pp. 53-61. (Initially accepted April 1996 in J. of Systems Engineering).

231

BOXMAP - NON-INVASIVE DETECTION OF CRACKSIN STEEL BOX GIRDERS
J.R. WATSON, K.M. HOLFORD, A.W. DAVIES, and P.T. COLE*
School of Engineering, Cardiff University, Cardiff, UK*Physical Acoustics Ltd., Cambridge, UK

ABSTRACT
Active defects, such as crack growth, produce acoustic emission (AE) signals. These signals can bedetected by sensors spaced between 3 and 20 m apart, depending on the structure. The sensors aremounted directly onto the external surface and no paint removal is required for their mounting. The useof sophisticated data acquisition and analysis systems enables cracks to be located very early in life,
long before they are detectable by the naked eye or other non-destructive testing methods. This paper
presents BOXMAP technology, which has been developed by Physical Acoustics Limited and Cardiff
University over the past 5 years, as a proven method for locating cracks in steel bridges. The
methodology for monitoring structures is described in detail, highlighting strategic and practicalconsiderations for the client and the engineer. These include access requirements, structuralinvestigation and the filtering of any extraneous “noise” from crack data. One of the main benefits
comes from data collected from continuous or intermittent monitoring, which can be used as structural
fingerprints. These fingerprints can then be compared at regular intervals and the change in condition
and/or rate of crack growth determined. This provides qualitative and quantitative information, fromwhich repairs can be ranked and a priority-based maintenance strategy developed. The overall benefit
is reduced whole-life costs. Details about the capabilities and limitations of different monitoring
strategies are presented, along with examples from bridge trials with particular emphasis on source
location and characterization.
INTRODUCTION
Bridge monitoring has the aim of providing information on the structural condition of a bridge,
identifying and evaluating defects, to optimize maintenance work and maximize the impact of a limited
budget [1]. Currently this is done by visual inspection [2] and monitoring using traditional methods suchas strain gauging with conventional non-destructive testing methods used to evaluate suspected defects.
Whilst this increases confidence in the integrity of the structures, many defects remain undetected until
they reach an advanced stage [3-5].
There is a need for an assessment tool that is capable of identifying defects in their early stages, as risk
and cost can be reduced by early intervention before large-area damage occurs. Monitoring systems
using strain gauges and deflection transducers rely on various indicators such as deformation, strain andthe vibration modes of the structure as a means of evaluation. These systems concentrate on detecting
major structural changes that are caused by notable deformation and redistribution of stresses in the
structure, by which stage severe damage has already occurred.
Traditional NDT techniques such as X-radiography, ultrasonics, magnetic particle and dye penetrant are
widely used throughout industry but are generally cumbersome to use on site. All of these techniques
assess local areas, which require good access to the structure and may entail working at height, above
water, or in the proximity of fast moving traffic.
232

Indeed, some structures have areas that are so difficult to access that it becomes unfeasible, dangerous,
or too costly to inspect. These techniques also provide a “snap shot” of defects and are unable to provide
any more than speculative information about future behavior. They are ineffective and too costly as a“global” inspection method.
The benefits of acoustic emission (AE) are long known in the oil/petrochemical and aerospace industry,
and are becoming increasingly recognized within the civil engineering industry. For 6 years, the Federal
Highways Agency and Physical Acoustics Corporation in the USA monitored known defects andevaluated repairs on a number of bridges and produced a report with guidelines for the local monitoringof cracks and repairs [6]. Over the past 6 years during fatigue tests at Cardiff University’s School ofEngineering, AE from large structural specimens has been studied to identify the characteristics of thedifferent stages of crack growth [7-9].
Physical Acoustics Limited and Cardiff University have developed an AE-based condition monitoring
technique for steel and composite bridges after a number of investigations and trials on a variety ofbridges [10]. The AE system detects and locates active defects, such as crack initiation, anywhere in a
structure using externally mounted sensors, positioned meters away from defects. This method allows
non-invasive 100% volumetric assessment of structures and is therefore particularly effective for box
girders, which has led to the development of the BOXMAP technique.
STRATEGIC CONSIDERATIONS FOR AE BRIDGE MONITORING
Before carrying out an AE test, there are strategic considerations to be made. Three main factors,
structural detail, the acoustical behavior of the structure and the monitoring strategy, namely Global,
Semi-Global and Local, will dictate the number and location of sensors.
Box girders may have some of the following design features; diaphragms (with and without accessholes), diagonal-bracing, stiffeners, and external access holes. All of these features influence the waythat the elastic stress wave travels, decays and is located by the software. It is therefore necessary toreview technical drawings to make use of structural details as wave-guides to ensure the full extent of
the structure is monitored.
The location and number of the sensors to be used is a function of signal attenuation, which is affectedby structural geometry, plate thickness, design, paint characteristics, and background noise. In every
case, there will be unwanted “noise” from external sources such as traffic activity, expansion joint
impact, bearing movement and airborne electromagnetic interference (EMI). It is essential to filter any
extraneous emissions from collected data so that emission from defect growth is recognizable.
Background noise is overcome using a range of methods; guard sensors are used to shield sensor arraysfrom sources of noise such as expansion joints; pattern recognition programs filter out identified noise
sources that are incorporated into the cracking data. These methods have been developed using theexperience gained during 6 years of bridge monitoring work with Federal Highways in the USA andduring numerous bridge trials in the UK, and fatigue testing at Cardiff University.
Structures can be continuously or intermittently monitored with permanently installed sensors from thesite or remotely via a modem. Technological advances and increased accessibility to the Internet allow“remote from site” real-time structural monitoring. Permanent mounting of cables and sensors withpulse and self-test capability eliminates the need for repeated access, giving rapid payback comparedwith the cost of aerial lifts and aerial walkways. Short duration on-site monitoring can provide
233

qualitative and quantitative information that can effectively assess the structure over a period of days
using temporarily mounted sensors.
Three common monitoring methods are used to obtain differing levels of information:• Global• Semi-Global• Local Area
“Global” monitoring using large sensor spacing still offers maintenance engineers the ability to identify
damaged structures and focus further monitoring or inspection on areas sustaining damage. Trials haveshown that an entire box girder can be monitored for one day and reveal a number of active areas. We
are currently developing techniques to reduce the number of sensors required to globally monitor astructure.
“Semi-Global” monitoring provides precise source location, but requires an increased number ofexternally mounted sensors to give 100% volumetric monitoring, i.e. capable of locating damage
anywhere in the structure, including shear studs, diagonal-bracing, welds, stiffeners and diaphragms.
“Local” area monitoring is used to assess active sources and known defects, using a small array of
sensors around the area of interest. Crack orientation, length, activity, and position can be determinedwith accuracy over a short monitoring period, whilst continuous or intermittent monitoring at this level
can identify the direction of crack growth and the rate of propagation. This information is essential forthe bridge engineer to determine the status of the known defect and schedule maintenance or repair.
AE monitoring strategies all offer different levels of information about structural integrity and defects.
AE monitoring using all of the strategies in conjunction may provide priority based assessment andmaintenance methods where structures need to be fully assessed. “Global” monitoring is initially used tograde the structure according to its condition. Structures with active defects can then be “SemiGlobally” monitored to identify and locate significant sources, which in turn can be “Locally”
monitored, or assessed using traditional NDT methods, where access is possible to acquire detailed
information.
FIELD TRIALSOne of the earliest AE tests of a steel bridge was carried out by Pollock and Smith [12] in 1972 on amilitary steel bridge. Since then there have been many more tests on a wide variety of structures, bothsteel and concrete. Huge experience was gained by Physical Acoustic Corporation during local area
monitoring of identified defects and repairs for Federal Highways (F.H.W.A.). This work prompted
Physical Acoustics to develop the Local Area Monitoring (L.A.M.) AE system.
Large area monitoring of steel structures have been developed by Cardiff University in conjunction with
Physical Acoustics Limited. All tests and trials carried out used Physical Acoustics Corporation’s
MISTRAS and SPARTAN AE systems, and sensors. The first trial on Trecelyn Viaduct in 1996 soughtto establish the level of background noise typically encountered in bridge structures and to study theattenuation of signals in large structures. Emissions from expansion joint impact were found to “ring”
throughout the structure, which required digital filtering. Bolted connections were found to significantly
reduce the signal strength, yet artificial sources were adequately picked up at distances in excess of 20
meters.

234

Fig. 1: Saltings Viaduct.
Extensive trials were carried out in 1997 on Saltings Viaduct, a 26-year-old composite box girder on the
A465 near Neath, South Wales (Fig. 1). This study showed that active areas were identifiable at 10metre sensor spacing, many of which coincided with internal diaphragms. Local monitoring of the mostactive diaphragm identified a strong source from the access hole cut into the plate. Recent Finite
Element analysis [13] showed this to be an area of particularly high stress, which has led to on going
fatigue tests on box girder sections with a variety of diaphragm designs.
MOTORWAY TRIAL I - PRELIMINARY INVESTIGATION
During preliminary investigations, a composite box girder bridge was monitored during the morning
rush hour. Prior to monitoring, an acoustic investigation was conducted using an artificial source toconfirm location accuracy for a variety of details within the structure, including stiffeners, diaphragms
and the internal welds. The bridge was monitored using a Semi-Global, externally mounted array. This
test revealed that the bridge was in a very good condition with very few AE signals detected. This
preliminary investigation enabled an optimum test set-up to be determined for the following major
bridge trial.
MOTORWAY TRIAL II - MAJOR TRIAL TEST DETAILS
A 40m span composite box girder was studied over a two-week period, during which it was semiglobally monitored for a one-week period between 5am and 10pm. The most significant AE sources,
identified during investigations in the first week, where locally monitored during the second week. The
girder lies underneath the cheveroned area between the 3 lanes south bound and the merging area of a
two-lane slip road, Fig. 2. This girder was chosen since transverse deformation across the deck induceshigh stresses in the diagonal bracing members, Fig. 3.
235

SOUTH
BOUND
GIRDERPOSITION

NORTH
BOUND
SLIP
ROAD

CENTRALRESERVATION

Fig. 2: Position of box girder.

Fig. 3: Box girder cross section.

In order to identify any sources in the girder and attachments, the beam was monitored using a semiglobal array that gave 100% volumetric monitoring. Two-dimensional location was achieved by “unwrapping” the structure, with damage locations shown on an “opened-up” diagram. Integrity of the crossbraces was assessed using linear source location between sensors at either end of each cross brace. An
initial AE study identified the expansion joint as a large source of extraneous noise. To filter this noisefrom the test data, guard sensors were placed to the ends of the girder. One-day trial monitoring in thefirst week identified 62 sources. Local arrays were set up around the two most active sources.
RESULTS OF THE TRIAL
From the 62 identified sources, 18 were identified as significant and analyzed in detail. These were all
found to be active on a daily basis. All but one of the 18 significant sources were found to be located at
the end of the diagonal bracing. The main source “A”, identified by “Semi-Global” monitoring during
the first day and subsequently monitored by a local array, is crack like and approximately 250 mm long,as illustrated in Fig. 4. It was located below the end of a diagonal bracing connection at the bottom of
the flange. It is difficult to state whether the source is in the internal seal weld or bottom flange/web
weld. The absence of emission from the central region of the suspected crack suggests that there is nofurther growth in this area.
AE activity showed a strong correlation with traffic flow. During periods of peak traffic flow, theaverage traffic speed dropped and at times traffic was stationary. These periods saw a reduced amount ofemission. Most activity occurred when there was a high percentage of lorries travelling at a high speed,
55-65 mph. Figure 5 shows the correlation between strain due to free flowing traffic and AE events
detected from the most active source, source "A".

236

Fig. 4: Local monitoring location graphs for Source “A”; top graph shows activity in 2D, lower graphshows AE activity along the length of the crack like source.

Activity

StrainFig. 5: Correlation between events from Source “A”, and strain, over the same time period during peak
but free flowing traffic conditions.
237

CONCLUSIONS
Acoustic emission monitoring trials on motorway bridges using “Semi-Global” and “Local” sensor
arrays have demonstrated that AE is a very effective method for detecting damage in bridges and steel
structures, and for continuous monitoring of defect growth. A major advantage is that there is no needfor internal access or paint removal. In addition, it is 100% volumetric testing, and damage location isachieved with relatively few sensors. Global monitoring has been developed as a method for identifying
damaged structures; further research is being carried out by Cardiff University and Physical Acoustics
Limited to develop this method to its full potential.
REFERENCES
1.
2.
3.
4.
5.
6.
7.

8.

9.
10.

11.
12.
13.

Bridle, R., “The Economics of Maintenance, Repair and Strengthening.” TRRL Document 1991.
DTp., “Bridge Inspection Guide”, HMSO – Department of Transport, 1984.
Russel, H., “Fears for Flyover as Cracks Appear”, New Civil Engineer, pp. 6, 30th July 1992.
Fowler, D., “Bridge Cracks Paralyse M50”, New Civil Engineer, pp. 5, 13th February 1992.
Fisher, J.W., “Hundreds of Bridges - Thousands of Cracks”, Civil Engineering, 55(4), April 1985.
Physical Acoustics Corporation, “Acoustic Emission For Bridge Inspection” Report No. FHWARD-94, prepared for FHWA and U.S. Department of Transportation, June 1995.
Davies, A.W., Holford, K.H., Sammarco, A., “Analysis of Fatigue Crack Growth in Structural
Steels by Classification of Acoustic Emission Signals”, Proc. 1994 Engineering Systems Design andAnalysis Congress, PD Vol. 64.8.2, Vol. 8, Part B, ASME, USA, pp. 349-354. ISBN 07918-1280-4,
1994.
Davies, A.W., Holford, K.H., “Health Monitoring of Steel Bridges Using Acoustic Emission”,
Structural Assessment: The Role of Large and Full Scale Testing, Joint Institute of StructuralEngineers/City University, E and FN Spon. pp. 54.1-54.8. ISBN 0-419-224-904, 1-3 July 1996.
Yan, T., Holford, K., “Acoustic Emission Analysis Using Pattern Recognition Technique”, 23rd
European Conference on Acoustic Emission Testing, Vienna, Austria, pp. 225-229, 1998.
Holford, K.M., Cole, P.T., Carter, D.C., Davies, A.W., “The Non-Destructive Testing of Steel
Girder Bridges by Acoustic Emission”. 14th World Conference on Non-Destructive Testing, Vol. 4,
pp. 2509-2512, ISBN 8120411269, 1996.
Carter, D., Holford, K.M., “I.M.A.G.I.N.E: Letting bridges do the talking”, INSIGHT, 38(11), 775779, November 1996.
Pollock, A.A., Smith, B., “Acoustic Emission Monitoring of a Military Bridge”, Non-destructive
Testing, 5(6), 164-186, 1972.
Pullin, R., Carter, D.C, Holford, K.M., Davies, A.W. “Bridge Integrity Assessment By Acoustic
Emission - Local Monitoring”, Proc. 2nd International Conference on Identification on Engineering
Systems, Swansea, pp. 401-409, ISBN 0860761584, 1999.

238

MONITORING FAILURE MECHANISMS IN CFRP ORTHOPAEDIC
IMPLANTS DURING FATIGUE TESTING
A. TAYLOR, S. GROSS*, C. ROWLAND¶ and P. GREGSON
Finsbury (Development) Ltd., UK, *University of Southampton, UK,¶
Pancom, UK.
ABSTRACTThe success of an advanced CFRP hip prosthesis of highly anisotropic design is dependent on having afundamental insight into its fatigue behaviour. Evaluation of damage under representative complex
fatigue loading, conducted at various load levels, has been performed utilising non-destructive acoustic
emission testing techniques. It has been demonstrated that the development of failure micromechanisms
can be modelled by a discrete stage damage accumulation curve. Specialised feature extractions weregenerated from static tests and have enabled cumulative damage mode discrimination to be determinedthroughout the fatigue testing. The acoustic emission results have been correlated with complimentary
inspection techniques.
INTRODUCTION
The design of an articulating hip implant from CFRP is attractive since functionally gradient biologicaland mechanical properties can be engineered to enhance the implant life. Specifically it is possible toreduce the interfacial shear stresses between implant and host tissue, thereby promoting more naturalload transfer to adjacent bone. [1]
The CFRP implants are designed for the more active younger patient and will be subject to higher andmore varied activity levels. Testing at the University of Southampton has been designed to simulate thisloading. Damage accumulation throughout the fatigue loading is monitored by acoustic emission (AE)techniques. Prior to fatigue testing, AE signatures were determined from static test data to distinguishthe various classic damage mechanisms.
The present paper focuses on the AE performance in damage mode recognition and location. It also
provides information to formulate a damage development model.
METHODOLOGY
The implant is based on a specifically designed dry carbon preform that accurately defines the complex
fibre architecture. This preform combines various carbon fibres in unidirectional, woven and braided
material formats to achieve the desired mechanical properties. The method of resin transfer moulding is
employed to impregnate the dry carbon to obtain the bio-compatible load bearing implants. This methodof production guarantees control over the entire external geometry.
Initial static tests were conducted in an Instron test frame with the implants embedded in a PMMA resin.
The loading was applied at a compound angle, compliant with the ISO recommendations [2], to replicate
the inclinations of the applied forces found in the body. Damage was monitored utilising a pair of239

resonant 375kHz AE sensors during incremental loading. Off loading between steps facilitated detecting
the change in Felicity ratio [3] on-line. A hold period at each load was carried out to determine thestability of any damage. All salient parameters of the AE events were recorded for characterisation on an
AET5500 acoustic emission monitor.
Fatigue tests were conducted at 64%, 70% and 82% of the average static failure load and with a constantstress ratio of R=0.1. One sensor was positioned at the mounting interface and the other adjacent to theconic loading area, allowing damage location to be conducted based on the arrival time delay theory.
Any signals detected by one sensor only or could not be located between sensors were filtered at thefront end. All the AE signals, after passing over a set threshold level and through the valid event location
filter, were measured to characterise the time-domain features; these include time of arrival, rise time,
amplitude, count, duration and energy. From complimentary NDT methods, characteristic signal
parametres were determined, which discriminated between the types of damage. Cross-correlation withmicro-section microscopy was also conducted at locations where AE indicated damage.
STATIC TESTING AND CLASSIFICATION OF DAMAGE MECHANISMS
A series of static tests were conducted in order to characterise and gain an understanding of theevolution of various failure mechanisms and obtain static mechanical data. The specimens tested inincremental steps to ultimate failure, achieved an average load level of 5.5kN. At this load, damage
propagated in an increasing unstable fashion. The load level at which the Felicity ratio deviated from 1,
indicating the onset of permanent damage, occurred at 90% of the ultimate load (Fig. 5). A clear change
in damage mechanism can be observed between the loadings below and above the point where theFelicity ratio deviated from 1, both on the amplitude and energy of AE data (Figs. 1 to 4). Figures 1 and
2 clearly display a change in failure mechanism above 4.5kN with a notable shift in emission amplitude.
Figures 3 and 4. Provide supporting evidence of an additional failure mechanism above 4.5kN with amarked shift in quantity and energy of emissions.
Static Test On CFRP Implant

90
80

60

0
500
1000

50

1500
2000

70

No. of Events

2500
3000
3500

40
30

4000
4500
5000

20
5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
500

10

100

94

97

88

0

91

82

85

76

Pa (db)

79

70

73

64

67

58

61

52

55

46

Load (N)

49

40

43

0

Fig. 1 The amplitude and energy of AE data.
240

5500

Using intrinsic AE signatures recognised from the preceding specimens, an additional test was
successfully terminated just prior to fast fracture (during the hold period at maximum load). The implant
was then sectioned to determine the initial damaging failure mechanisms. An additional static test was
stopped at a level, where the Felicity ratio was still 1 (80% of the ultimate) and again sectioned. Figure 8
peak amplitude - p17
140

120

No of events

100

80

60

40

20

0

98

10

96

94

92

90

88

86

84

82

80

78

76

74

72

70

68

66

64

62

60

58

56

54

52

50

48

46

44

42

40

0

Amplitude (db)Fig. 2 The amplitude of AE data.

Events below 4.5kN

Total events to 5.5kN

P17 - Energy

8070

No. of events

60
50
40
30
20
10

5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
500

145

140

135

0

130

125

120

115

110

105

95

100

Energy

90

80

85

75

70

Load level (N)

65

60

55

50

45

35

40

0

0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500

Fig. 3 The energy of AE data.
shows a typical section from the specimen loaded to 80% of the ultimate, displaying microcracking onlyin the resin-rich areas. This provided a correlation of the AE data (Fig. 6), with the extent of micro241

120

100

No. of events

80

60

40

20

98
10
1
10
4
10
7
11
0
11
3
11
6
11
9
12
2
12
5
12
8
13
1
13
4
13
7
14
0
14
3

95

92

89

86

83

80

77

74

71

68

65

62

59

56

53

50

47

44

41

38

35

0

EnergyFig. 4 The energy of AE data.

Events below 4.5kN

Total events to 5.5kN

events against load
2500

cummulative no. of events

2000

1500

1000

500

0
0

1000

2000

3000

4000

5000

6000

load (N)Fig. 5 Graph displaying cumulative events/load history during incremental static load test. The
maximum applied load resulting in a felicity ratio of 1 was 4.5kN.
cracking, and also classified and distinguished the evolution of the additional failure mechanisms on
previous micro-sections. The initial damage detected during static testing was a result of stable matrix
242

100

90

80

70

Events

60

50

40

30

20

10

0

98

10

96

94

92

90

88

86

84

82

80

78

76

74

72

70

68

66

64

62

60

58

56

54

52

50

48

46

44

42

40

0

Amplitude

Fig. 6 Display of peak amplitude for a specimen loaded below the point where the Felicity ratio
departed from 1 [4.5kN].
micro-cracking at the position of maximum stress adjacent to the implant mounting; these occurred earlyin the test and then diminished.
The relatively compliant PMMA-resin potting medium to implant interface also emitted emissions
throughout the loading history as a result of a tensile/shear failure, and subsequent friction on theproximal implant surface and a creep/bearing failure at the distal location. These emissions
predominantly occur on the unloading section of the incremental static test. A small degree of microcracking was observed on all the tested specimens and this correlates well with the lower amplitude
emissions centred around 46dB. Figure 7 displays the onset of delamination between tows in resin richareas and microcracking observed in the implant. These delaminations then propagated to form thetrans-tow macro-cracking observed in Fig. 9. This permanent damage results in the Felicity ratio
dropping below 1. This failure propagation mechanism resulted in acoustic emissions centering around
60dB.
THE RESULTS OF THE FATIGUE TESTING
In order to record only useful data and make post-test analysis more efficient, a filter was set up tomonitor events occurring during approximately 90% of the sine-wave fatigue cycle using a voltagecontrol gate. This eliminated most of the noise generated at the lowest load due to the movement at thepotting interface.
A series of fatigue tests were conducted employing the same fixture configuration as with the static tests
but at various load amplitudes. These established the signal/noise characteristics of the implant design
and the fatigue threshold level of the implant (Fig. 12). With the cyclic tests loaded above the fatigue
243threshold, and using the AE signatures recorded from the static tests, it was observed that damage
initially developed with matrix cracks propagating to form stable delamination growth in the criticalsection. Evidence to support this theory can be gained from Fig. 10, which displays the striations left ona typical fracture surface along resin-rich areas in planes orthogonal to the loaded fibres. Figure 11
displays the AE amplitude-event history throughout the test that can be compared to those obtained fromthe static tests. As a result of this failure mechanism, the local stress distribution adjusts with the effectof superimposing additional matrix micro-cracking into the AE data as was observed in Fig. 11.
Once the local delaminations propagated due to continued cycling, friction-induced emissions weregenerated as the fracture surfaces rubbed against each other, this being most prominent on the unloading
section of the cycle. These initial two stages dominated the useful life of the implant as can be observedin the discrete stage damage accumulation curve in Fig. 13. Stage 1, resulting from micro-cracking andthe resulting propagation to form delamination in resin-rich areas adjacent to fibre tows. This produces
cumulative events linearly increasing with cycle count. The number of micro-cracks and delamination
initiation sites continue for approximately half the life of the specimen until the density promotesdelamination to propagate. The result is the second stage displayed by the prominent increase of slope.
This continues for the remaining life of the specimen until rapid final failure, stage 3, which occurs in
the final few cycles. The rapid failure is characterised by a short burst of high amplitude, 92dB,
emissions at the end of the test, Fig. 11.
In contrast to the predictable delamination propagation, the damage growth just prior to the final failure(stage 3) was very short consisting of fibre breakage and gross delamination making prediction of
imminent failure difficult. Bursts of acoustic emissions with very high amplitude and duration
dominated the stage. This stage is difficult to observe in Fig. 13 due to the very short duration.

Fig. 7 Delamination in resin rich area and microcracking observed in samples before onset of permanent
damage.
244

Fig. 8 Areas of high resin concentration often exhibit cracking prior to permanent damage. The initial
microcracking, delamination and resin crack propagation in these areas result in an AE distribution
centred around 46 dB.

Fig. 9 Transtow macrocracks found when the Felicity ratio drops below 1, which during testing ischaracterised by an acoustic emission amplitude distribution centred around 60 dB.

Fig. 10 Scanning electron microscope view of the striations found in the resin rich areas adjacent to thefibre tows.
245

Peak fatigue load 3.5 kN, R-ratio 0.1

14000

12000

No. of events

10000

8000

6000

4000

2000

3.41
3.19
2.98
2.76

0

2.55

40 43
46 49
52 55
58 61
64 67
70 73
76 79

2.33
2.11

No. of cycles (x106)

1.90
1.68

82

Peak amplitude (dB)

1.47

85

88

91

1.25

94

97

10
0

Fig. 11 AE response during a typical fatigue test displaying similar signatures to that observed during
the static investigations.
S-N Curve
passedfailedLog. (failed)120.0%

Percentage of Static Failure Load

100.0%

80.0%

60.0%

40.0%

20.0%

0.0%
1

10

100

1000

10000

1000001000000

CyclesFig. 12 S/N curve generated from the implant test program.

246

10000000

Stage3

Stage 1

Stage 2

3.5E+06

Fig. 13 AE fatigue test history graph. The onset of stage 2 damage propagation is accompanied by adistinct change in event rate. This would coincide with the point at which the Felicity ratio departed
from 1.
CONCLUSIONSThe use of acoustic emission health monitoring throughout this research has established the following:••••

The characterisation and recognition of damage mechanisms.
A reliable method for terminating a test prior to final catastrophic failure, to enable the fundamental
initiation sites to be identified.
The ability to detect the onset of permanent component damage by observation of the Felicity effect.
A discrete three stage progressive damage accumulation mechanism is observed in fatigued
specimens.

The CFRP implants displayed good fatigue immunity with a resulting threshold level equating to 70% of
the average initial static failure load. The failure mechanisms observed in the fatigue investigation withcorresponding AE data matched that of the static testing very well.
REFERENCES[1] Evans, S.L., Gregson, P.J., Doherty, P.D., Osei, D., Revell, P.A., Taylor, A., Tuke, M.A., The
design, development and testing of a novel femoral component using advanced composite technology.
5th World Biomaterials Congress, Toronto, 29 May – 2 June 1996; 2, p. 795.
[2] ISO 7206-4 Implants for surgery – Partial and total hip joint prostheses – part 4: Determination of
endurance properties of stemmed femoral components. 1997.
[3] Hamstad, M.A., A Discussion of the Basic Understanding of the Felicity Effect in Fiber
Composites. Journal of Acoustic Emission, 5(2), 95.

247

CONTINUOUS MONITORING OF ROCK FAILURE
BY A REMOTE AE SYSTEM
Tomoki SHIOTANI, Shigenori YUYAMA*, Mark CARLOS ¶, Sotirios J. VAHAVIOLOS ¶Tobishima Corporation, Chiba, Japan. *Nippon Physical Acoustics Ltd., Tokyo, Japan.
¶
Physical Acoustics Corporation, Lawrenceville, NJ, USA.
SUMMARY
In Japan, rock failure is one of the most frequent disasters. Because rock materials are brittle,
deformation due to micro to macro fracture is thought to be transient phenomenon. Acoustic emission
known as a premonitory phenomenon is expected to be useful for prediction of eventual rock failure. In
order to perform stable AE monitoring to evaluate rock failure, a remote AE system by modem
communication has been successfully developed for continuous rock failure monitoring in field. A
prospective method to install AE sensors into the rock slope (WEAD) has also been developed. A seriesof studies on the WEAD are conducted for evaluation of AE signals in the laboratory. Based on theexperimental results, criteria for classifying fracture states are proposed. The remote AE systems havebeen used at three sites in field for monitoring rock slope failure for about two years. The proposed
criteria for the fractures are applied to the field data and fracture state are evaluated comparing withinternal slope behavior estimated by displacement, borehole-strain, temperature and so forth. Thus, theapplicability of the remote AE system for continuous monitoring of rock failure with WEAD isdemonstrated.
INSTALLATION OF AE SENSORSIn the case of AE monitoring in rock slope, effective detection of AE waves generated due todeformation of rock is usually difficult because AE waves are strongly attenuated when they propagate
through joints of rock. In geotechnical application of the AE technique, to avoid energy attenuation of
AE waves, waveguides are often employed to detect AE signals (Hardy and Taioli, 1988; Nakajima etal., 1988). There are two types of the waveguides. One is low-attenuation type of waveguides devised tolead weak AE signals to AE sensors. Metallic materials are generally adopted as the low-attenution typeof waveguides. Another is deformation-related waveguides designed to generate self-emissions due todeformation of the guide corresponding to the rock motion. Several types of rosin, fiberglass, PVC withsand, PVC with water, and so on, are employed as the self-deformation waveguides. The latterwaveguides, however, are devised on soil materials, which have relatively ductile nature compared withrock materials. Thus, applications of these waveguides to rock materials are thought to be difficult.
Desirable conditions of waveguide for rock materials are as follows: (a) AE waves can propagate up to
the AE sensor to avoid strong attenuation influenced by rock conditions. (b) The characteristics of AE
waves detected should reflect actual fracture mechanisms. (c) The stability of the rock slope can bereasonably evaluated by the AE signals acquired.
In order to meet the above conditions on the waveguide for rock materials, WEAD (waveguide for AE
waves due to rock deformation) has been developed. The WEAD consists of cementitious materials andreinforcement with AE sensors. In the WEAD, borehole excavation is performed to install the AE
sensors and to obtain mechanical properties of the rock. Mixture proportion of cementitious materials is
248

(a)
Fig. 1. Behavior of WEAD under deformation of rock. (a) Before. (b) After deformation.

(b)designed with reference to the mechanical properties of core samples retrieved. Figure 1 shows behaviorof the WEAD under deformation of rock. With the deformation of the rock, the WEAD simultaneously
deforms along with the rock, and then AE wave is generated within the cementitious material. Fracture of
the WEAD occurs similarly to those of the deformed rock. As a result, characteristics of the AE waves
would be compatible to those due to actual rock deformation. Because initial state of the WEAD is
intact, AE waves generated can efficiently propagate to the AE sensors. The WEAD can have otheradvantages. Because filler of cementitious materials in the borehole is mixed from cement, sand andchemical admixtures, AE characteristics of the filler can be experimentally obtained. Thus, comparing
field data with empirical data, fracture state of the rock can be readily evaluated.
In the case that the WEAD contained many cracks due to damage, the rock conditions would be similar
to those of surrounding rock. It may result in the difficulty to detect AE signals effectively. To solve
this problem, reinforcement of steel bar is installed into the borehole with AE sensors. AE signals
subsequent to the local motion would be detected until final failure with the reinforcement. It is notedthat these AE waves may have nothing to do with the actual fracture mechanism of rock. However, AE
counting and AE parameters could provide reasonable information on the fracture state. Figure 2 shows
characteristics of wave attenuation in grouting materials with or without reinforcement. Also in the
figure, results of the filler fractured by bending and shear are shown. Properties of the filler are discussed
in the next session. The grouting material (filler) with reinforcement has more effective propagation
characteristic than that without reinforcement. Even in the cases of the grouting materials failure, those
propagation characteristics are comparable to that without reinforcement. Thus, the reinforcement plays
an important role in the WEAD.
EXPEREVIENTAL STUDIESIn a monitoring site of rock failure, boreholes are excavated to investigate crack conditions and to
determine mechanical properties. To make the strength of the grouting material compatible to the rock,to develop the strength promptly and to ensure high workability, grouting materials of fast-curing typeare employed. Chemical components of CaO: SiO2: A1 2O3: SO3 are 51.5: 26.9: 11.8: 2.6 by weight.
Beam-type specimens of dimensions 10 cm x 10 cm x 100 cm with reinforcement of 13 mm diameter
installed in a neutral axis were cast. These specimens were moisture-cured in water for 28 days in thestandard room (20˚C). Figure 3 illustrates both bending and shear tests along with the arrangement of AE
249

sensors. These two types of failure tests were conducted by four-point loading. Ten AE sensors (60kHz resonance) were set on the specimen in the bending, while eight AE sensors (60 kHz resonance)
were used in the shear test.

Fig. 2. Characteristics of wave attenuation in the waveguides.

Fig. 3. Illustrations of bending and shear tests and arrangement of AE sensors.
250

AE signals over 40 dB were recorded as AE waves by a MISTRAS AE system (PAC). Classical AE
parameters were also analyzed. Using sets of AE signals from more than six independent channels, AE
sources were located in three-dimension. Applied load and deflection of the specimen were alsomeasured.
Results and discussions
Figure 4 shows averaged AE (ringdown) counts of the latest 100 data sets until the final failure. The
dotted lines in Fig. 4 show the elapsed time reached 100 data sets. In both bending and shear tests, stable
increase of the AE counts could not be observed. In the case of bending, the averaged AE counts lessthan 20 may imply the fracture state transferred from early stage to intermediate stage. The AE countsof the final stage would be from 20 to 30. In the result of shear test, the averaged ringdown counts ranged
from 40 to 50 over the whole fracture stages.

(a) Bending test.

(b) Shear test.
Fig. 4. Averaged AE (ringdown) counts of the latest 100 data sets until the final failure.
251

(a) Bending test.

(b) Shear test.
Fig. 5. Averaged energy counts of the latest 100 data sets until the final failure.
Figure 5 shows the averaged energy counts of the latest 100 data sets until the final failure. The energy
counts are defined by the area of a rectified AE signal envelope, and is known as a parameter, which is
closely related to the energy of AE sources. In the result of bending, it is found that energy counts in theearly stage of fracture are less than 20. This is when mortar cracks would be generated in the specimen.
The energy counts ranging from 20 to 40 are thought to represent the intermediate stage of fracture,
although sudden increase up to 160 and a decrease are observed. In the intermediate stage of fracture,bond fracture between reinforcement and mortar would be generated. In the final stage of fracturesuggesting crack coalescence and crack growth, the averaged AE energy counts of over 40 is observed. In
the case of shear, it is obvious that the averaged energy counts successively increase up to the final
fracture level. This implies that the average energy counts of 80, 200 and 300 would become the criteria
to classify the fracture stages.
252

Because AE amplitude is associated with the magnitude of fracture, the b-value that is defined as a slopeof the amplitude distribution is known as an effective index related to the fracture states (Mogi, 1964;
Sholz, 1968). In order to apply the b-value to the AE technique, improved b-value is proposed (Shiotani
et al., 1994). The improved b-value has successfully been applied not only to geotechnical materials(Shiotani and Ohtsu, 1999a; Shiotani et al., 1999b) but also to concrete materials (Shiotani et al., 1999c).
Because the b-value is originally defined in seismology, it is difficult to determine the amplitude range
and the number of AE data to obtain the proper b-value in AE application. The improved b-value isdefined by utilizing such statistical values of the amplitude distributions as mean, µ, and standard
deviation σ. The improved b-value (Ib) is proposed by,(1)
where N(w1) is the accumulated number of AE events, in which amplitude is more than µ – α 1 σ, N(w2)is those of AE events, in which amplitude is more than µ + α 2 σ,; and α 1 and α 2 are constants. Then, theamplitude range is given by (α 1 + α 2 )σ. To compare with the seismic b-value, the improved b-value
above should be multiplied by twenty. β in equation (2) is the total number of AE events when the
improved b-value analysis is performed.
(2)

Fig. 6. Improved b-value due to bend and shear tests until the final failure.
Figure 6 shows the improved b-value due to bending and shear loads, where α 1 = 1, α 2 = 0 and β = 100
are employed. Dotted lines in the figure represent the elapsed time when AE events reached β. In the
result of bending, the improved b-value reached 0.17 in the intermediate stage of fracture, then it sharply
dropped to 0.04. Afterwards, with progressing to the final stage of fracture, it rises and drops repeatedly
ranging from 0.04 to 0.15. Finally, the variation becomes smaller between 0.02 to 0.06. In the result of
shear, the improved b-value perpetually changes in the narrow range from 0.02 to 0.06 over the wholefracture stage. This implies that continuous fluctuations of the improved b-value from 0.02 to 0.06
correspond to the fracture stage where shear-type of fracture is more dominantly generated thantensile-type of fracture. Based on the experimental results, the criteria of fracture levels suggested aresummarized as Table 1.

253

Table 1. Criteria of fracture level based on the experimental results.

APPLICATIONS
Geologically, a monitoring site is made of hornfels. The monitoring slope dips with angle of 80˚. In theslope, it is observed that many diagonal joints are lying perpendicular to the slope surface. After removalof unstable rocks for disaster prevention, AE monitoring was performed with the WEAD. Making
reference to mechanical properties of rock, grouting material, which is previously discussed, was used
for the WEAD. Figure 7 shows the sensor arrangement and the joint condition. Five AE sensors (60kHz) equally spaced on reinforcement 13 mm diameter by 1.5 m were installed into the slope and one
AE sensor (60 kHz) was placed at control room to monitor background noise. To eliminate undesirable
emissions due to natural phenomena induced by rain, sunshine and wind, a surface portion of theborehole was filled with sand down to 1.0 m. AE signals generated from the slope were amplified 40 dB
at sensor-integrated preamplifiers, and the signals over the threshold 40 dB were acquired in a remote AE
system (PAC). To collect data and to change test conditions remotely, an AE system with a modem
connection was developed and deployed. A seismometer, crack gauges in three dimensions and boreholestrain gauges were also set in the slope.

Fig. 7. Sensor arrangement in monitoring slope.
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Results and Discussion
AE activity between March and September 1999 is shown in Fig. 8, although AE monitoring is still inprogress. Because AE activity during six months was mostly of the same trend at all sensors, only
results of AE-4 are shown in Fig. 8. Figure 8(a) exhibits the averaged ringdown counts of the latest 100
data sets. A dotted line in the figure denotes the elapsed time reached when data number reaches 100. It
is observed that the averaged ringdown counts stayed around 10. Thus, fracture level of the site was
evaluated as level I/II from Table 1. Figure 8(b) shows the averaged energy counts. The same trend as the
ringdown counts was confirmed. Because the averaged energy counts varied around 10, fracture level was
evaluated as level I from the table.
Figure 8(c) represents the variation of the improved b-value. Because remarkable increase and decrease
could not be observed, the fracture level was evaluated as less than level I. Thus, fracture levels evaluatedfrom these parameters agreed quite well. From all those results on the ringdown counts, the energy
counts and the improved b-values, the monitoring slope was thought to be sound. Moreover, from thetrends of temperature and crack gauges in three dimensions, emissions detected during the measured
period were estimated as those due to volumetric expansion and shrinkage of the rock blocks induced bytemperature variation.
CONCLUSIONS
An installation method of AE sensors for the rock AE monitoring technique called WEAD is introduced.
In order to evaluate the slope stability, the fracture criteria are proposed on the basis of experimental
studies on the WEAD. A remote AE system has been developed. The WEAD and the remote AE
system were applied to in-situ. slope monitoring and then the AE data acquired are evaluated by the
proposed criteria comparing with slope behavior. It is found that both fracture levels estimated from AE
activity and slope behavior agreed quite well. Thus, reliability of the WEAD and the remote AE system
is verified.
REFERENCES
Hardy, H.R. and Taioli, F. (1988). "Mechanical Waveguides for Use in AE/MS Geotechnical Application," Progress in Acoustic Emission IV, JSNDI, pp. 293-301.
Mogi, K. (1964). "Magnitude-Frequency Relation for Elastic Shocks Accompanying Fracture of Various
Materials and Some Related Problem in Earthquakes," Bulletin of Earthquake Research Institute, 40, pp.
831-853.
Nakajima, L, Sato, J., Taira, N. and Kubota, N. (1988). “The Observation of Landslide by the Acoustic
Emission Monitoring Rod," Progress in Acoustic Emission IV. JSNDI, pp. 273-281.
Sholz, H. (1968). “The Frequency-Magnitude Relation of Microfracturing in Rock and Its Relation to
Earthquakes" Bulletin of Seismological Society of America, Vol. 58, No. 1, pp. 399-415.

255

Fig. 8. AE activity in AE-4 between March and September, 1999.
256

Shiotani, T., Fujii, K., Aoki, T. and Amou, K. (1994). ”Evaluation of Progressive Failure Using AE
Sources and Improved b-Value on Slope Model Tests," Progress in Acoustic Emission VII, JSNDI, pp.
529-534.
Shiotani, T. and Ohtsu, M. (1999a). "Prediction of Slope Failure Based on AE Activity," Acoustic Emission: Standards and Technology Update, ASTM STP 1351 pp. 156-172.
Shiotani, T. Ohtsu M and Monma. K. (1999b). "Rock Failure Evaluation by AE Improved b-Value,” The
Second Japan-US Symp. On Advances in NDT Proc. Book, JSNDI & ASNT. pp. 421-426.
Shiotani, T., Yuyama, S., Li, Z., Okamoto, T. and Ohtsu M (1999c). Quantitative Evaluation of Fracture
Process in Concrete by the Use of Improved b-value.” Proceedings of 6th Domestic Conf. on Subsurface
and Civil Engineering Acoustic Emission. MMIJ, pp. 123-128, (in Japanese).

257

NEW AE SIGNAL CONDITIONNER FOR INDUSTRIAL USE
Hartmut VALLEN, Jens FORKER and Jürgen VON STEBUT*
Vallen-Systeme GmbH, Icking (Munich) Germany,*Ecole des Mines, Lab. de Science et Genie des Surfaces, Nancy, France
ABSTRACTThe analysis of transient acoustic emission bursts is used relatively rarely in industrial environments.
Highly sophisticated AE-systems as available today are powerful tools for the development of new AE
applications and the demonstration of their feasibility. Integrating these complex systems into industrial
environments causes various fundamental problems. The ASCO-P Signal Conditioner presented here ismade for rugged industrial environment. The ASCO-P is easy to handle and allows for detection andmeasurement of short AE-bursts, for example from crack growth, delamination, fibre breakage, partial
discharge, etc.. The ASCO-P converts these short bursts into DC-signals that can be measured by simple
devices. No complicated software setup is required whatsoever. The ASCO-P measures the peak
amplitude levels of burst AE from the lowest that can be discriminated against background noise to thatof a pencil lead break with approximately 1 dB accuracy and without any need to enter a threshold. A
simple recorder can present the time and peak amplitude of a burst. Additionally, the ASCO-P possessesa digital output for external controls and an analog output that represents the continuous backgroundsignal. In addition to AE bursts the ASCO-P provides also information about continuous AE.
INTRODUCTION – BURST AND CONTINUOUS SIGNALS
An AE application works with either burst or continuous AE signals. AE burst signals are distinct
signals where the beginning and end can be recognised. Common examples include crack growth,particle impact, partial discharge, or other source mechanisms of short duration. Continuous AE are
endless although the amplitude and frequency content can change over time. Examples are flow noisefrom leaks and rubbing noise, among others. A rapid sequence of overlapping burst signals can also
appear as a continuous signal, when the individual signals can no longer be separated. The measurement
type is chosen according to whether burst or continuous signal types are being investigated.
For continuous signals, one measures usually the averaged amplitude. A signal cond itioner amplifies,
rectifies, and averages the high frequency AE-signal over an integration time and by a specific method.
The RMS-value is often used, which is the square root of the averaged signal square. From burst signals
one usually extracts a variety of features. These features include arrival time, peak amplitude, rise time,
signal duration, counts and energy. The acquisition threshold is the most important criterion for
discriminating AE burst signals.
PROBLEMS FROM THE INFLUENCE OF THRESHOLD ON MEASUREMENTS
Signals whose peak amplitude is not large enough to cross the threshold are not detected. Setting thethreshold too high will prevent potentially important signals from being recorded. Setting the thresholdtoo low will cause the background noise to cross the threshold and will result in a great deal of unwanted
data to be recorded.
If the background level rises during the test, the signals may remain above the threshold for long times,
so that the AE system “sees” only a few especially long hits. In this case, despite a large number of burst258

signals occurring, only a small amount of data will be produced. Possibly a lot of important info rmation
is lost.
Next to the severe influence of threshold on general detection of the signals is the dependence between
the threshold setting and the values obtained for many measured features. An example is the value
measured for the duration of the AE signals. The duration is defined as the time period between the firstand last threshold crossing of a signal. A signal, which decays slowly, will result in a much longer
duration for low thresholds than for higher thresholds.
The optimum threshold setting depends specifically on the amplitude of the AE signals of interest andon the actual noise signals. Further, the signal amplitude depends on many factors, such as sensor
sensitivity, the sensor coupling quality, the source energy, the signal attenuation related to distance
between source and sensor, changes in wave propagation conditions, and so forth. With presently
available AE systems, the optimum threshold setting is of primary importance and fundamental for the
success of every application that makes use of burst emission analysis. One needs experience torecognise if acquired data is plausible and then conclude a correct threshold setting.
There are numerous possible applications, where threshold independent detection of short AE burstsignals in a wide range of amplitudes would be helpful. These measurements should be both foolproof
and require no special AE knowledge.
As an example the scratch tester application is presented.
SCRATCH TESTER APPLICATION EXAMPLE
Scratch testers are used to determine failure limits of surface coatings. Consider the fo llowing: a test
fixture set-up draws the tip of a diamond indenter with continuously increasing force and constant speed
across a test surface. The linearly increasing contact load causes tensile stress behind the indenter tip(trailing edge) and compressive stress ahead of the cutting tip (leading edge). At some point during
scratching, a crack is initiated. The position of the first crack within the scratch path is determinedvisually using a microscope. From the crack position within the scratch track one can determine thecorresponding critical contact load
With modern coatings, the first cracks are so small that they are difficult to perceive even under the
microscope. They may even close up within a few ms and thus become optically “undetectable”. This isa task just waiting for AE as a “crack damage warning – validation” indicator. However, this requires an
AE system that is easy to use and cost effective.
In the course of an EC funded project (SMT4- CT97-2150), a better scratch test procedure is currently
under development. Here, an AE module and a coupled video system are added to a conventional
scratch tester. The AE module is used to determine the cracking and trigger the video system. These
video images (potentially taken on-line, in the stressed state) will then be the material proof of AE
signals caused by cracking.
For this application, it was necessary to study the relation between AE and different variables - such as
different diamond tips, coatings and substrates. These measurements were made with the multipurpose
Vallen AMSY4 instrument. Results were published by von Stebut et al. [1]259The goal was to find means to differentiate between crack emissions and noise such as friction,mechanical vibrations etc. For this purpose, many burst signals were studied, including investigation of
waveforms and their frequency content.
For this application, the sensor has to be attached to the shaft of the scratch tester for practical reasons.
In this position, the geometry of the shaft influences the wave propagation very strongly. It was notpossible to find a waveform or frequency analysis criterion suited to differentiate crack signals fromfriction and mechanical vibration artefacts that could be obtained with a realistic and cost effective
instrument.
The best criterion found for separating cracking and unwanted noise is the peak amplitude. It is easy tounderstand that the absolute peak amplitude is strongly dependent on the brittleness of the test sample,
the shape and size of the diamond tip as well as the quality of the AE sensor coupling on the shaft, alongwith other factors.
Several requirements were established from the preliminary study and knowledge of how scratch testers
are used. These requirements and the corresponding solutions are described hereafter.
FIRST REQUIREMENT
The AE module should be as foolproof as possible. This means that as many cond itions, which cause
errors or mistakes, such as bad settings, should be eliminated. One cannot expect that a user will perform
multiple tests on each new coating only for the reason of finding the optimum threshold. Despite these
constraints, determining the instant of crack formation remains the goal, which must be reached inpractice. This must even be true when the amplitude-dependent AE conditions change, as can be
expected with every new test material. It must also hold true when the diamond-tip sharpness and thequality of the coupling changes.
Solution:
The AE signal is converted into a DC-signal, which is proportional to the logarithm of the peak
amplitude detected. The scaling factor is 40 mV/dBAE. A noise signal at the input of 20 dBAE, or 10 µV,
produces an ASCO-P output of 800mV (40mV/dB * 20dBAE). A pencil lead break which generates apulse of 100dBAE produces 4V (40mV/dB * 100dBAE).
After each increase in amplitude, the peak signal remains for 50 ms at this last peak. This time period iscalled the peak stretching time (PST), because the peak output voltage is maintained constant for thisperiod. This allows a slower, inexpensive data acquisition system to be used, such as one for measuring
the force. If the amplitude increases still further during the 50-ms PST, the output increases immediately
to this new value and the PST begins again. After the expiration of the PST, the signal quickly falls tothe value of the input amplitude, thereby allowing further hits with lower amplitudes to be easily
recognised.
Figure 1 shows the ASCO-output signal (upper curve) caused by a simulated AE signal (lower curve).
To make this process clearer, the peak-stretching time was lowered from 50 ms to approximately 0.080
ms in this example.

260

Figure 1: ASCO-output reply to a short transient AE-signal

The arrows 1 and 2 on the left edge
show the zero point of both signals.
The ASCO output. begins at the farleft at about 0.8 V, before the AE
signal begins. This corresponds tothe peak value of the electricalnoise, which is around 20 dBAE (40mV/dBAE * 20 dBAE = 800mV).
The ASCO-output follows the rising
AE-Signal without delay. The peak
amplitude of the signal reaches
approximately 99 dBAE (90 mV),which causes a corresponding
increase in the output signal to3960 mV (99 dBAE * 40 mV/dBAE =3960 mV) and remains at this value
for the duration of the PST (herereduced to 0.08 ms; default is 50ms). When the PST is over, theoutput signal falls immediately tothe current value of the input signal.
This allows short duration signals tobe separated even when they arefollowing quickly after each other.
To further exploit these signals, a chart recorder or a simple data acquisition system can be used. The
sampling rate should be shorter than the PST; with a 50 ms PST, a 25-50 Hz sampling rate isrecommended. This combination will guarantee that short bursts can be measured with an arrival time
resolution of 20-40 ms. If one acquires an additional test parameter, such as load or displacement, then
one can read the activation point of the first crack in a simple load-time diagram. An example is shownin Fig. 2. The steadily increasing line curve shows the contact load of the diamond normal to the surface(right axis). Every bar represents the peak amplitude of at least one AE-signal. The dB scale (left axis) is

Figure 2: ASCO-Output and Load vs. Time261

obtained by the factor 40mV/dB.
The high amplitude signal at t = 17 s was correlated unambiguously with a crack under the microscope.
All activity before this peak was essentially low amplitude friction or other noise. The 60-dB signal at
10s could not be correlated to visible cracks either. The first crack is found to occur at 27 N from thecorrelation with the load curve shown here.
Materials behave differently. Most, however, follow a similar pattern: a clear increase of amplitude at a
specific load. The actual value of the peak amplitude is not important, as long as crack signals rise above
the amplitude range of the friction noise.
SECOND REQUIREMENT
When crack growth occurs, a digital switch shall be activated. In the case of the scratch tester this switch
triggers a video system.
Solution:
To satisfy this requirement, an absolute threshold setting is used. This is defined as the level, which is
expected for peak amplitudes of cracking. Because the measurements are completely independent of thethreshold, the first test with a crack is already sufficient to show the optimum threshold for a reliable
separation of useful signals from the noise. In the example shown in Fig. 2, one would set the thresholdat about 75-80 dB.
The ASCO-P does not have a direct threshold setting; instead an alarm threshold is specified by ananalogue input signal. A PC-plug-in-board used for data acquisition can normally also supply thisanalogue setting.
The ASCO-P compares the logarithmic AE signal with the supplied alarm threshold signal. When the
AE signal exceeds the alarm threshold, a 50-ms long impulse is produced. With the scratch tester
application, this alarm signal is used to trigger the video system to record crack initiation.
Triggering the mega-pixel video system by the AE module reduces the amount of video dataconsiderably. Images are stored only when there is a high probability of crack activity. The recorded
video verifies the indication of the AE-module. New knowledge of not yet understood artefacts thenhelps to improve the understanding of the AE-module indications. In other applications, this output canhave other functions, for example to turn off a test or as an impulse for an event counter.
Presently available commercial instrumentation often requires a threshold setting that determines
whether data is acquired and the values of the recorded features. On the contrary, the ASCO-P
measurements are not influenced by a threshold. Merely the digital switch function is dependent on a
threshold.
BLOCK DIAGRAM AND PRACTICAL INFORMATION
Figure 3 shows the ASCO-P block diagram with essential functional groups of the ASCO-P. The filter
module can be chosen appropriate for specific applications.

262

The frequency range which is best for crack detection is 100-300 kHz. Even higher frequency ranges areuseful to get farther away from noise. For detection of leakage the frequency range of 20-100 kHz is
recommended. The ASCO-P has the additional outputs, OutputFilter and OutputASL, which are usefulfor maximising potential applicability. The OutputFilter signal is the amplified and filtered highfrequency AE signal. This can be used with a transient recorder, for example.

Figure 3: ASCO-P block diagram.

The logarithmic AE signal is fed through a low-pass filter and presented as OutputASL signal. On this
output the influence of short burst signals is insignificant and it shows the essential features of thecontinuous background noise. It is especially useful in monitoring continuous AE signals
Figure 4 shows the actual
device, cover removed. The
module fastened by 2
screws is the filter modulethat can be easily replaced.
ASCO-P AND AMSY4The ASCO-P is an analogue
front-end, easy to use, andcost effective. It is suited forindustrial applications as
well
as
for
research
activities. But where moredetailed information about
the AE signals is required, ahigh-end system is needed.
Figure 4: ASCO-P with removed cover
The possibilities of the
ASCO-P can be easily
extended by changing the PST when industrial applications require this. The analogue output can thenbe combined with other instruments and the ACSO-P then becomes an AE front end providing filtered
AE signal output that can be acquired and analysed with an external transient recorder.
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FURTHER DEVELOPMENTS
The AMSY4 AE system has been extended to include a threshold independent mode that corresponds tothe function of the ASCO-P. Thereby new applications of the ASCO-P can be developed anddocumented with both the convenience and greater possibilities of the AMSY4. When the initial work iscompleted, the ASCO-P is then ready to be put into service.
The ASCO-P is also the basis for a new battery-powered hand held instrument LSM1: The Leak Signal
Monitor. The peak amplitude of the AE signals can be read in dBAE on a digital display. With thisdevice, the AE operator can test a structure for background noise levels before testing. Either the
background level (ASL) or peak amplitude level can be chosen for display. Headphones can be usedwith an adjustable threshold so that even the smallest variations can be tracked. Differences in signal
intensity can be used to find the direction from where noise signals arrive.
The LMS1 helps determining the origin of specific noise sources before a time-consuming multichannel test set-up. The field-test engineer, and even the field test client, can use the LSM1 to find out if
a structure is sufficiently “quiet” for AE testing. The device allows for scheduling only quiet tanks for
AE field test and avoids costs that unnecessarily arise, when a AE test crew comes and cannot performthe test because the structure is too noisy.
CONCLUSION
The ASCO-P allows for the measurement of short peak amplitude signals with large dynamic range in a
simple, easy to use instrument. It makes the realisation of new, simpler and cost effective applications
for the AE method possible.
The ASCO-P is an ideal accessory for any universal testing machine. This is especially true when themachine is already equipped with an existing data acquisition system for measuring load and/or
displacement, to which the ASCO-P output can be added. One then possesses a simple, manageable, yet
powerful AE system that shows the first onset of damage as it occurs.
ACKNOWLEDGEMENTS
This work has been accomplished in the framework of the European programme “Standards,
Measurements and Testing“ as part of an on-going EC funded project (SMT4- CT97-2150).
REFERENCES[1] J. von Stebut, F. Lapostelle, M. Bucsa, H. Vallen, Acoustic Emission Monitoring of Single Cracking
Events and Associated Damage Mechanism Analysis in Indentation and Scratch Testing. Surface andCoatings Technology, 116-119 (1999), 160.

264

NEW SOFTWARE TOOLS FOR THE AE-PRACTITIONER
Hartmut VALLEN and Jochen VALLEN
Vallen-Systeme GmbH, Icking (Münich), Germany
ABSTRACTThe analysis of AE data is more complex compared to traditional forms of data acquisition. In addition
to the presentation of measurement values against time, as one expects in any basic data acquisition
system, AE analysis software is usually expected to perform extensive data processing, for example:• Statistical distributions of AE signals according to AE features and/or external load parameters,• Calculating the source location of each event based on the measured arrival time differences and thegeometry of the structure under test,• Automated cluster analysis emphasizing the places of high location density and more.
The capabilities stated above are basic features found in today's AE systems and frequently used. In
addition to basic system capabilities, AE users desire a series of advanced tools. This paper describes
some of the new tools that have been implemented in the VisualAE™ software package. These tools
utilize extensively the graphical representation possibilities offered by the 32-bit Windows® software
platform.
Tools for Checking Sensor Coupling
Normally, before an AE test begins, the coupling between the sensors and the test structure is checked.
Even though the auto-calibration feature of modern AE-systems is extensively in use today, it cannot
replace the pencil-lead break calibration method that can be performed at any desired position of a test
object. A pencil-lead break of known diameter and hardness produces an AE signal with sufficiently
repeatable amplitude. It is usually best to read and judge these amplitudes in a listing. Unfortunately,
additional unwanted signals are often produced from the pencil-lead breaks due to various reasons
including the impact of the pencil-lead fragment with the surface, reflections and more. In many cases
the number of unwanted signals that arrive after the pencil break shift the lines containing the pencil
break signal out of the screen. One remedy is to use a higher threshold; however, this is inconvenient at
best.
The tools in VisualAE offer a more efficient approach:1a) A scrollbar in the listing,1b) Direct addressing of listing lines by clicking on points in amplitude-time diagrams,1c) Analysis filters which are setup quickly.
A few examples follow:
1a)
Scrollbar in Listing
Figure 1 shows a screen with various diagrams and a listing window. With the vertical scrollbar on theright edge, the user can view the AE data with similar ease to the text in a word-processor (like MSWord). Both upward and downward commands make it easy to display the desired listing line.
With the horizontal scrollbar, the area of the listing that is being displayed can be shifted sideways. This
helps with data presentation when there are more desired result columns than fit onto one screen area.
The user can position the display window to the currently desired columns, without disturbing
simultaneously running data acquisition.
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1b)
Direct Addressing of Listing Lines
With a double click on an amplitude point within a correlation plot (or on a location point in a locationplot), the user also positions the listing window on the selected line.
When waveforms were also recorded and a TR-diagram is present, the waveform diagram is also
updated to the TR-data corresponding to the selected point. This allows for immediate visualization of
the maximum amount of information from any selected data set.

Fig. 1: Listing with scrollbars. Diagram with identifying inset windows.
Figure 1 shows data from a pencil-lead break. In the location diagram (below left) a point was selectedwith a double-click as the arrow in the small inset window shows. The data corresponding to theselected event has been automatically identified in other display areas with small inset windows. The
mouse-click has also highlighted the line on the listing and brought the corresponding waveform andFFT from the first hit channel of the event into the TR-diagram display. With the forward and backward
arrow symbols in the waveform diagram, one has a simple means to view the other channels in theevent. The data acquisition can be running simultaneously to this analysis without any disturbance fromon-screen manipulations. This makes possible both fast selection and visualization of data from any
pencil-lead break.
266

1c)
Analysis Filters
During breaking pencil leads, one can often produce many unwanted signals with smaller amplitude. For
coupling checks, these signals need to be skipped quickly. VisualAE provides fast and easy means toinsert, modify or remove a filter from within the analysis path.
Figure 2 shows a structure menu for a normal analysis process. An additional filter processor for A > 80
dB has been prepared in the file TEST.PRI. There is, however, no element (diagrams, listings) attachedto this filter, means the filter is not used. The location processor (Lc1...) has four elements (three
diagrams and a listing) attached.
If one wants now to only locate data that passes through the filter A>80 dB, one can simply drag thelocation processor with the mouse and drop it under the filter processor. The structure menu will be
modified as shown in Fig. 3.

Fig. 2: Structure menu where filter (FL1) has noattached elements

Fig. 3: Structure menu after moving location processor
behind filter (FL1)

Location Analyzer - a Tool for Verifying Calculated Location
The location calculation is increasingly important. The significant and numerous uses include:A) Recognition of signals from known and locatable noise sources. These signals should be
eliminated from the data sets without being mistakenly assigned to potential failure processes.
B) The location calculation shows the general area of suspected defects for focused, cost-effective
follow-up NDT search.
C) From the measured amplitude and determined distance between the sensor and the source, anamplitude correction can be calculated, which indicates the source amplitude before propagation.
This higher, corrected value is already a part of the standard prEN13445-5 Appendix E.
To gain the most possible information from location analysis, it is necessary to understand why andwhere errors in location analysis occur and potentially eliminate them.
267

The correct settings and interpretation for location calculation requires experience. Without experience,
it is difficult to tell which combinations of arrival times are at all plausible. The wave propagation
starting from the source on its way to the sensor may be influenced by many different factors: pipe inlets
and outlets, welded areas on the structure, and propagation through fluid within the test object, as well as
many others. It is very difficult to judge how strongly such influences alter location results.
An understanding on how the various influences actually effect location results is highly desirable to auser. A user-friendly tool, with which the operator can quickly check location results is needed. This
tool should use real data recorded from real structures and offer the possibility to manipulate values by
the user. One such tool, the Location Analyzer in VisualAE, is presented here.

Fig. 4: Location Analyzer from an analysis window with diverse diagrams and listings.
Figure 4 shows data from a cylindrical pressure vessel, 3.4 m in diameter. The AE sensors are found in 3
rings with 4 sensors per ring. The location analyzer (window in foreground) shows the hyperbolae for alocation after a mouse click. The location was selected by a double click on a location dot in the location
plot below right. This dot is now indicated by an inset window. Every hyperbola shows all the points on
the surface of the test object corresponding to a certain distance difference to two sensors. With isotropic
wave propagation, one finds the difference in distances between each sensor pair by multiplying thearrival time difference by the sound velocity. The intersection of all three hyperbolae is the location268

result. The legend beneath the graph is color-coded to show which hyperbola belongs to which sensor
pair.
On the right side of the window of the location analyzer (Fig. 4), there is an entry point for different
sound speeds (velocity, at top) and for individual arrival time differences for every channel (below). The
time differences initially correspond to the measured values. The user can change these values and see
immediately how these modifications change the location results.
From three hit channels are three pairs of channels possible, and there can only be one intersection
within the sensor array. The selected event in Fig. 4 contains 6 hit-channels. Only the hyperbolae for the
combinations of sensors 7, 12 and 8 are shown because these have been checked in the menu (Fig. 4,
right above).
After checking channel 11 in that menu, the software shows the hyperbolae from four channels (7, 12, 8,11), reproduced in Fig. 5. Four channels make six channel-pairs, hence Fig. 5 shows 6 hyperbolae. Here,the hyperbolae do not show a single common intersection point. This happens, when the effective
velocity to at least one channel differs from the user-defined velocity that is used for location
calculation. The location result represents the position that gives the least squared Distance Difference.
The term Distance Difference is explained below. The RMS (root mean squared) value of all involved
Distance Differences is called LUCY (Location Uncertainty) and its numerical value is shown in thelegend, below of the location result.

Fig. 5: Location Analyzer showing hyperbolae from 4 hit channels.

269

“Velocity: 316 cm/ms”, shown on top of the legend, defines the Used Velocity, the speed of sound thesoftware uses for location calculation and for determining the time of source. The “Calculated
Velocities” and “Distance Differences”, also shown in the legend, help the user interpret the uncertaintyof the source position. These values are based on the time of flight (TOF) between assumed source
location and sensor. The TOF is the difference between Time of Source (TOS) and Arrival Time (AT).
The AT is measured for the channel, the TOS results from first hit AT minus first hit sensor-source
distance divided by Used Velocity. “Calculated Velocity” means “source-to-sensor distance divided by
TOF”. “Distance Difference” means “source-to-sensor distance minus TOF x Used Velocity”. A
positive Distance Difference indicates that the shown result is too far away from the sensor, or, the hit
was triggered by a wave mode faster than Used Velocity, or, the wave reached the sensor over a shorter
path, e.g. through a liquid in the structure.

Fig. 6: Intersection point obtained by decreasing the time difference from Channel 11
The Location Analyzer lets the user play with alternative velocities and time differences to see how thisinfluences the location result and the plausibility indicators. In Fig. 6, for example, a common
intersection point is found, when the arrival time difference from channel 11 is reduced from 569.9 µs to399.9 µs.

270Fig. 7: Intersection point obtained by changing the velocity to 226 cm/s.
In Fig. 7, the velocity of 316 cm/ms is reduced to 226 cm/ms for all channels. This method also finds
one common intersection for the four hyperbolae.
The location analyzer is a helpful tool that quickly develops experience on effects of errors of velocity
used. At the same time, it helps to find out which location results are plausible and which are not. For
non-plausible results, the user can quickly determine what plausible arrival-time differences would be,
and whether this corresponds to certain conditions of the test object; e.g. a direct wave path through aliquid instead of over the structure wall.
Another purpose of the Location Analyzer is to improve the understanding of the capabilities and limitsof today’s location algorithms, and to generate feedback for improvement and further development.

271

IMPROVED SOURCE LOCATION METHODS FOR PRESSURE VESSELS
Valery GODINEZ, Sotirios J. VAHAVIOLOS, Richard D. FINLAYSON,
Ronnie K. MILLER and Mark F. CARLOS
Physical Acoustics Corporation, Princeton Junction NJ, USA

ABSTRACTIn this work, results of an acoustic emission source location study using both linear and planar location
strategies are presented. The study was performed on a submarine steel gas flask with pencil-leadbreaks used as artificial acoustic emission sources. Plate wave propagation theory is used to analyze theeffects of dispersive Lamb wave modes on source location accuracy. Narrow-band filtering techniques
are used in conjunction with different sensor arrays of 2, 4, 6, and 8 sensors to improve source location
accuracy. By combining these two factors, location accuracy of 1% of the length of the flask and 3% of
its circumference can be achieved. The results are presented in the form of 2-D accuracy color maps.
The total average error in source location for each one of the sensor configurations is calculated.
Finally, a summary of the results is presented in the form of a table indicating the advantages anddisadvantages of every sensor configuration studied.
INTRODUCTION
Acoustic emission (AE) source location in an extended structure, such as a pressure vessel, is subject toinaccuracies caused by two factors: the location method, i.e. linear or planar, and by the ratio of theacoustic signal frequency to the thickness of the structure under study. When AE signals received by
two sensors are used to locate an AE source, i.e. linear location methods, the arrival times of the signal
produced by an AE source will define a hyperbola with its foci located on the position of the sensors.
Thus, the position of the AE source could be at any point along this hyperbola [1]. To avoid thisproblem, planar location methods must be used; that is, signals from a minimum of three sensors have to
be used to calculate the position of the AE source [2].
The ratio of the AE signal wavelength (λ) to the vessel wall thickness (T) determines the nature of the
wave propagation phenomena in the structure. In the case of a wavelength much smaller than the wall
thickness, λ/T << 1, the stress waves produced by the AE source will propagate in the form of bulk
longitudinal, bulk shear, and surface waves, with constant velocities. When the wavelength iscomparable to the wall thickness, λ/T ≈ 1, the situation becomes more complex. In this case, the stresswaves produced by the AE source will propagate as multiple Lamb (plate) wave modes, each with aparticular velocity and attenuation depending on its frequency; that is, the modes will be dispersive [3].
The immediate effect in the location method is, depending on the position of the AE source in relation toeach of the sensors, that different wave modes, traveling with different speeds, may trigger different
sensors.
In the case of very large structures, such as large storage tanks or civil structures, the problem ofmultiple wave mode propagation does not have an effect on AE source location due to the long paths
that the wave modes have to travel to reach the sensors. However, in the case of AE inspection ofrelatively smaller pressure vessels, such as compressed gas cylinders, it is important to precisely locate
AE sources. The solution of the problem becomes even more critical when these compressed gas
cylinders are located inside large structures such as submarines and other ships. In submarines, for272

instance, the cylinders are located behind bulkheads and the inspector must be certain of the location of
any AE indications, as removal of the bulkheads is very time consuming and expensive. Therefore, it isimportant to study the effect of multiple wave mode propagation in these pressure vessels in order toimprove the accuracy of source location techniques.
EXPERIMENTAL SETUP
In order to study the effect of Lamb wave mode propagation in AE source location, a steel gas flask wasremoved from a Trident nuclear submarine. The cylindrical shell is 4.57 m (180”) long, with an external
diameter of 509 mm (20”), and spherical end caps. The flask was instrumented with eight PAC AE R15I
sensors (with a peak resonance frequency at 150 kHz) as shown in Fig. 1. Figure 1(a) presents an
unwrapped view of the flask with the approximate positions of the sensors on its surface and Fig. 1(b)shows the approximate position of the sensors around the circumference of the flask.
The wall thickness of the cylindrical shell of the flask was measured using an ultrasonic thickness gauge
and was found to be 31 mm (1.2”). The sensors were connected to a PAC DiSP 52 channel AE system.
According to the US Navy requirements, the maximum permissible error in AE source location is 127
mm (5”) in the circumferential and axial directions. Accordingly, the entire surface of the flask was
divided into a rectangular grid of 127 x 127 mm (5” x 5”).
A series of five pencil-lead breaks (PLB) using a 2H/0.3 mm mechanical pencil with a Neilsen shoe,
were performed at the center of every square on the grid. Files containing the amplitudes, arrival times,
and waveforms of each PLB were stored on the DiSP AE system. Later, the data were processed using
PAC’s source location software in order to calculate the position of every PLB. The five readouts
obtained for each PLB position were averaged and the result was assigned to the PLB location. The
difference between the real and the measured locations of the PLB was calculated and then plotted on
2D-color maps of the extended surface of the flask.
LINEAR LOCATION
The first case analyzed is that of linear location, in which the sensors are located along the axis of theflask. In this case, sensors 2 and 6 were used to calculate the positions of the PLB. In order todemonstrate the effect of the different wave modes propagating on the flask as a result of a simulated
source, the averaged differences in arrival times (∆T) from the five PLB on each square of the grid wereused to calculate the position of the simulated sources. For this purpose, a set of two values of the wave
velocity was selected: 3.10 mm/µs (122 x 103 in/s) and 4.88 mm/µs (192 x 103 in/sec), which correspondto the a0 and s1 Lamb wave modes. These values were selected based on the group velocity dispersion
curve for Lamb waves propagating along a steel plate with a thickness of 31 mm in the frequency rangeof the sensors used or 150 kHz. This group velocity dispersion curve is shown in Fig. 2.
Figure 3 presents accuracy maps of PLB location obtained using linear location. These maps present theerror point by point on the surface of the flask. The x-axis represents the flask coordinates along theaxial direction and the y-axis represents the flask coordinates along the circumferential direction. Eachsquare on the error map, corresponding to a 13 cm square on the real flask, has been colored according
to the difference between the real and calculated PLB for that particular point. Red and yellow tones
indicate higher differences while blue tones represent small differences. The color bars situated by themaps indicate the numerical equivalence of the error in source location.

273

(0,5)
Sensor 1y

Sensor 4

Sensor 7

Sensor 2

Sensor 5
Sensor 8

Sensor 3

Sensor 6(52.5, 175)(a)Sensor 2x
Sensors 7, 4

Sensor 5

Sensor 1

Sensors 3, 8
Sensor 6(b)Fig. 1. Position of the AE emission sensors on the Trident submarine steel gas flask. (a) Extended
surface of the flask. (b) Position of the sensors around the circumference of the flask.
6

s1 (0.192 in/µsec)

5Group Velocity [Km/s]4a1

a0 (0.122 in/µsec)

3

s0
2

s2

1

0
3

3.5

4

4.5

5

5.5

6

Frequency x Thickness [MHz x mm]

Fig. 2. Lamb group velocity dispersion curves for a 31 mm (1.2”) thick steel plate.
Figures 3(a) and 3(b) clearly show the influence of wave-mode velocity choice on linear location andconfirm the hypothesis of higher velocity modes arriving at nearby sensors, while the lower velocity
modes are the ones that trigger the sensors furthest away. When the a0 wave mode group velocity (3.10mm/µs) is used to calculate the positions of the sources, the areas close to sensors, located at the edgesof the cylindrical shell, present an error in location as high as 76 cm (30”). The points located towards
the center of the flask present errors below 25 cm (10”). When the s1 (4.88 mm/µs) wave mode velocity
is used in the calculation, the large errors in location shift towards points in the center of the cylindricalshell, while the areas close to the sensors present errors below 13 cm.

274

Location error [in](a)Location error [in](b)Fig. 3. Accuracy maps for linear source location on a cylindrical gas flask. These maps weregenerated using: (a) the a0 wave mode group velocity, and (b) the s1 wave mode group velocity.
NARROW BAND FILTERING
In order to reduce the inaccuracy of source location due to the effect of Lamb waves, we haveinvestigated the technique of narrow-band filtering. Previous studies at PAC have shown that by
applying narrow-band digital filters to signals from simulated AE sources in a gas flask, accuracy insource location can be improved [4,5]. The dispersion curve shown in Fig. 2 indicates that in thefrequency band of interest, the a0, a1, s0 and s2 modes have very similar group velocities, around 3.10mm/µs, and the s1 mode has a maximum group velocity of 4.88 mm/µs. However, the s1 mode
propagates with low amplitude when compared to that of the a0 mode. Therefore, if the signals from theAE source are narrow-band filtered in the frequency range of interest (before being processed by the AE
system), the amplitude of the s1 mode will not be large enough to trigger the sensors while the a0 mode
will. An accuracy map generated using narrow-band filtered signals and a group velocity of 3.10 mm/µs
is presented in Fig. 4. In this case, the errors observed along the flask are below 13 cm for all the points.
Location error [in]Fig. 4. Accuracy map for linear source location on a cylindrical gas flask. This map was generatedusing the a0 wave mode group velocity and narrow-band filtered signals.
PLANAR LOCATION
Although linear location combined with narrow-band filtering can achieve good location accuracy, its
main drawback is the fact that it only provides the source position along the longitudinal axis between
two sensors. In order to obtain the axial and circumferential position of an AE source on the flask, planar
location methods must be used.
275

In this work, three different configurations for planar location were studied. The first configuration wasa “F-placement” using sensors 1, 2, 4, and 5 positioned at coordinates (0,5), (31.5,5), (15.75, 175), and(47.25,175), respectively, according to Fig. 1(a) coordinates (unit in inches). As in the case of linear
location, the signals were narrow-band filtered. The results obtained with the F-placement configuration
are shown in Figs. 5(a) and 5(b).
Location error [in](a)Location error [in](b)Fig. 5. Accuracy map for planar source location on a cylindrical gas flask using an array of four
sensors. (a) Along the circumference of the flask. (b) Along the axis of the flask.
Figure 5(a) presents the accuracy map for source location in the circumferential direction of the flask. It
is clear that two bands of high inaccuracy, where the error can be as high as 76 cm, appear on thesurface of the flask. These bands are located symmetrically on opposite sides of a band of very low
inaccuracy, where the error is lower than 13 cm, and which runs from the (0,5) to the (47.25, 175)
position on the extended flask surface. The accuracy map of source location along the axial direction isshown in Fig. 5(b). In this case, the accuracy is very good and the error is below 13 cm in nearly all the
positions of the simulated AE sources. A practical way of estimating the overall accuracy of the sensor
configuration is to calculate the total average error (TAE) in both directions, circumferential and axial.
In the case of the F-placement with 4 sensors, the TAE is 15.5 cm (6.11”) and 4.30 cm (1.69”) in thecircumferential and axial directions, respectively. It is important to mention that the F-placement is an
ideal configuration for a length-to-circumference ratio approximately equal to 1. In the present case, thelength to circumference ratio is 2.77.
The second configuration studied was an array of six (6) sensors, with sensors 1 to 6 located according
to Fig. 1. In this case, the PLB positions were calculated using PAC’s arbitrary sensor configuration
software. This software selects the three first arrival times, regardless of the sensor position, andcalculates the intersection point of the three hyperbolae defined by the three sets of two-sensor
combination. Figure 6(a) presents the accuracy map for source location along the circumference of theflask and Fig. 6(b) the corresponding one for the source location along the axis of the flask. The
circumferential error decreases substantially when compared to the previous case. Except for a section
on the center of the flask, the location error is less than 13 cm in the circumferential direction. For theaxial direction, the map indicates errors below 13 cm in almost every point of the flask. The TAE in thiscase is 5.23 cm (2.06”) for the circumferential direction and 4.55 cm (1.79”) for the axial direction.
276

Fig. 6. Accuracy map for planar source location on a cylindrical gas flask using an array of six
sensors. (a) Along the circumference of the flask. (b) Along the axis of the flask.
The last configuration analyzed is the array of eight sensors positioned as indicated in Fig. 1. Arbitrary
location software was used for source location and Figs. 7(a) and 7(b) show the resulting accuracy maps
for the axial and the longitudinal directions, respectively. Figure 7(a) shows no spots with errors above
13 cm, the same is observed in Fig. 7(b). The TAE is 4.04 cm (1.59”) and 4.50 cm (1.77”) for thecircumferential and longitudinal direction, respectively.

Fig. 7. Accuracy map for planar source location on a cylindrical gas flask using an array of eight
sensors. (a) Along the circumference of the flask. (b) Along the axis of the flask.
277

CONCLUSIONS
The results obtained in this study clearly demonstrate that the propagation of Lamb wave modes inplate-like structures has a serious impact on the accuracy of AE source location. The use of narrow-band
filtering eliminates this problem by filtering out low amplitude higher velocity Lamb wave modes. In
addition, if very accurate source location is required, such as in the case of the Trident nuclear
submarine compressed gas cylinders, planar source location methods have to be implemented during
inspection. Also, other factors have to be taken into account when a source location strategy isimplemented, such as cost, availability of AE channels, number of cylinders to inspect, and access to thewhole surface of the cylinders. Finally, Table 1 summarizes the practical advantages and disadvantagesof using different source location strategies.
Table 1. Comparison between advantages and disadvantages of linear and planar source location strategies
SensornumberTotal Average Error(TAE) [in]Circumf

2

4

--

6.1

Longitudinal

1.95

1.69

Advantages

Disadvantages• Easiest Set Up• Excellent longitudinal location• 26 tubes inspected in a single
pressurization*• Does not provide circumferential
position of the source• It may require additionalpressurization on suspectedcylinders with 8 sensors• Excellent longitudinal locationand fair circumferential location• No access to the center of the
tube is needed• 180° “Flip Over” on thecircumferential position of AE
sources in certain areas of the tube
• 13 tubes inspected in a single
pressurization*• Errors in circumferential location aslarge as 15 inches at the center of
the tube• 8 tubes inspected in a single
pressurization*• Access to the center of the tube isneeded• 6 tubes inspected in a single
pressurization*

Combined

----

6.88

6

2.06

1.79

3.25

8

1.59

1.77

2.39

• Excellent longitudinal locationand very good circumferential
location• No access to the center of the
tube is needed• Excellent longitudinal andcircumferential location*These percentages were calculated assuming PAC DiSP 52 channel system.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the support of Jon Fallick from the Naval Sea Systems
Command in Arlington VA USA, for the completion of this work.
REFERENCES
1. Godínez-Azcuaga, Valery, “Acoustic Emission and Ultrasonic Testing for Periodic Inspection of
Pneumatics Pressure Vessels: Comparison of Linear and Planar Source Location Methods for Gas
Flask Inspection,” Physical Acoustics Corporation Project No. R-97-573 Final Report, March, 1999.
2. Miller, Ronnie K. and McIntire, Paul, Editors, “Nondestructive Testing Handbook, Vol. 5, Acoustic
Emission Testing,” American Society for Nondestructive Testing, Second Edition, 1987.
3. Viktorov, Igor A., “Rayleigh and Lamb Waves. Physical Theory and Applications.” Plenum Press,
1967.
4. Shen, Warren, “Acoustic Emission and Ultrasonic Testing for Periodic Inspection of Pneumatics
Pressure Vessels: Waveform Analysis of Simulated AE Transient Signals,” Physical Acoustics
Corporation Project No. R-97-573 Final Report, March, 1999.
5. Promboon, Yajai, “Source Location Studies on a Tank Car,” Acoustic Emission Working Group
Meeting, Princeton, NJ, June 1999.
278

NEURAL NETWORK LOCALIZATION OF NOISY AE EVENTS IN
DISPERSIVE MEDIA
M. BLAHACEK, Z. PREVOROVSKY, J. KROFTA, and M. CHLADA
Institute of Thermomechanics AS CR, Prague, Czech Republic

ABSTRACT
New philosophy for AE source localization under high background noise is designed. The algorithm isbased on probabilistic and fuzzy-neuro principles, so AE events may be classified according to theirenergy and location probability. AE signals recorded during the stamping processes of a thin metal
sheet are used for new algorithm testing.
INTRODUCTIONClassical algorithms of AE event detection and localization give satisfactory results in relatively simple
situations. Nevertheless, they can completely fail under conditions when dispersion and highbackground noise are present. For example, the detection and localization of AE events occurring during
the stamping process of thin metal sheets is complicated by the dispersion of waves propagating in the
sheet, and by the presence of noise produced during plastic deformation.
As an alternative approach, the artificial neural network localization algorithms based on signal arrival
time differences were extensively studied ([1-3], theoretical aspects in [5]), but the accurate signal
arrival time detection is still required. Therefore, a new philosophy of signal processing and AE source
localization is suggested to solve the problems with noisy AE events where the signal arrival time
detection is rather complicated. The philosophy is based on probabilistic and fuzzy principles reflectingsome uncertainty of AE event detection and localization.
AE signals recorded during the stamping processes of a thin metal sheet (Fig. 1) are used for new
algorithm testing. Both problems - AE event detection and source localization - are treated together. We
monitored AE by four wideband transducers placed at appropriate positions on a cutting tool.
Preprocessed AE signals were digitally recorded by a storage oscilloscope at 10 MHz sampling rate.
Recorded signal length is up to 0.5 s, so huge data must be analyzed.
As we suppose that the AE events caused by shear deformation and cutting mode fracture in metal sheet
are concentrated along the edge of a stamp, the localization may be reduced to a curvilinear 1-D
problem. Redundant information obtained from four AE transducers (only two transducers are neededfor 1-D localization) is used to reduce errors resulting from high background noise and wave dispersion.
AE EVENT DETECTION
It is usually easier to recognize individual AE events in a signal envelop than in the original signal,
especially if the high level noise is superimposed. The enveloping may be realized by convolution of therecorded AE signal with functions

279

0.05
0
-0.05
-0.04
0.1

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0
-0.1
-0.04
0.05
0
-0.05
-0.04
0.1
0
-0.1
-0.04

Fig.1: AE signals recorded during the stamping process (4 channels; time in s).
F1 ( x ) = max( f ( x ),0),F2 ( x ) = − min( f ( x ),0),f ( x ) = −3 x e − x2Here, f(x) is a function used to enhance signal edges. The envelopes F1 and F2 of the AE signal are thendivided to obtain new signal F with highlighted transient effects (see Fig. 2).
2

0

-2
-2
30

0

2

4

6

8
x10

-4

20
10
0
-2
30

0

2

4

6

8
x10

-4

20
10
0
-2

0

2

4

6

8
x10

-4

Fig.2: Original AE signal, convoluted signal F1 , and ratio F of the signals F1 and F2.
280

To detect AE events, the function F applied on the whole AE record was searched for its maxima.
Corresponding maxima in channels 1 to 4 have been determined, and if at least two maxima co-exist, theAE event is registered.
AE SOURCE LOCALIZATION
The first problem that must be accounted in a source localization algorithm is high level backgroundnoise, and the second consists in the fact that many AE events must not be well pronounced at alltransducer positions (the information redundancy helps to event recognition).
The signal F with enhanced transient effects is suitable for event arrival time ti detection under the highlevel noise. Time differences ∆tij = ti - tj are calculated for each pair of transducers (if the event is
detected by all transducers) or only for those pairs of transducers at which the AE event is detected.
Time differences ∆tij obtained by pencil-lead break calibration tests were used to construct localization
matrices Li , i = 1, ...,4 corresponding to channels 1,...,4 (Fig. 3). The stamp contour is split into 32
location sectors. Each row of matrix Li corresponds to one location sector. The maximal arrival timedifference allowed on the contour (50 µs) has been divided into 50 equidistant intervals creating matrix
columns.

5

5

10

10

15

15

20

20

25

25

30

30
5

10

15

20

25

30

35

40

45

5

50

10

15

20

25

30

35

40

45

50

Fig.3: Localization matrices L1, L2 . Black color represents ones, and white color zeros.
The localization matrices can be treated in terms of neural networks (NN) - see schematic diagram in
Fig. 4. In NN notation, the matrix Li represents weight matrix of one layer neural network (NNi) withlinear transfer function, i.e. associative memory type of NN. Each NN has 50 inputs corresponding totime differences ∆tij, and 32 outputs corresponding to 32 location sectors.
If we put a signal without noise to the networks (e.g. pencil-lead break test signal), then each input
vector will contain 31 zeros and one unit element, as it is possible to determine near exact timedifferences ∆tij in this case. Quadratic mean P of output vectors Pi (see Fig. 4) will have more than one
non-zero elements, but one of them is much higher then the others (supposing that the NN's are alreadywell trained).
Dealing with the noisy AE events, the matrices Li are convoluted with 2-D function281

G ( x, y ) = e − x .e − yto include possible errors in time difference determination (see Fig. 5). Now the i-th element of output
vector P corresponds to the probability that AE event occurs in the i-th location sector.
22t i − min( t u )t j − min ( t u )u

t k − min(t u )ut l − min(t u )ux( tiu)x( tju)x( tku)x( tlu)

NN1

NN2

NN3

NN4

P1(h)

P2(h)

P3(h)

P4(h)

u

P ( h) =(P12(h) + P22(h) + P32(h) + P42(h)) /4

Fig. 4: Schematic diagram of the localization algorithm.
The input vectors were also “fuzzyfied” to include the uncertainties of individual AE event detection.
All the above algorithms were programmed in MATLAB [4], and tested on recorded experimental data.

5

5

10

10

15

15

20

20

25

25

30

30
5

10

15

20

25

30

35

40

45

50

5

10

15

20

25

30

35

40

45

50

Fig. 5: Matrices L1, L2 after convolution with the function G.
DISCUSSION OF RESULTS
The algorithms described above were used to detect and localize AE events in signals obtained during
stamping processes. Time - location distributions of AE events were plotted for better understanding of
running processes. Examples of two evaluated experimental results are shown in Fig. 6. The history of282

loading force P is plotted in Fig. 6 along with localized AE events represented by point markers
distributed in 16 circumferential sectors, and the energy of AE event is quantified by marker size.
The whole stamping process may be observed in first part of Fig. 6, while the second part shows only
beginning of the process under different experimental conditions. Metal sheet thickness, sheet positionin stamping machine, position of transducers, AE analyzer settings and process parameters are the main
factors influencing the AE results. Stamping tool diagnostics and process parameters optimization canbe supported by diagrams in Fig. 6.
# M04
3
16

2.5

14

2

13
12
11

1.5

10
9

1

8
7

0.5

6
5

0

4
3
2

-0.5

1

-1
0

100

200

300

400

500

600

700

800

Time [ms]

# V09
1.2

P

16
15
14
13
12
11
10
9
8

1

0.8

0.6

0.4

7
6

0.2

5
4

0

3
2
1

-0.2
0

20

40

60

80

100

120

140

160

Time [ms]Fig. 6: Loading history with time - location - energy plot of AE events.
283

180

200

Location sector

P

15

As the demonstrated examples represent very noisy processes of relatively short duration, the combined
probabilistic and fuzzy-neural approach give more reliable diagnostic view than quasi-deterministic
treatment charged by many unknown errors. In this new approach, the fixed localization of eachindividual AE event is substituted by the event occurrence probability distribution. In Fig.6, the marked
source locations represent distribution maxima of individual events. Summation of fuzzy localized
events registered during the whole experiment is illustrated in Fig. 7. Distributions of individual events
have been weighted by event energy and summed to obtain distribution of the event energy density
parameter Q in location sectors on the contour.

12140# V406# V410
120

10

Q

Q
100
8

80
6

60
4

40
2

20

0
1

2

3

4

5

6

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8

0

9 10 11 12 13 14 15 16

1

Sector No.

2

3

4

5

6

7

8

9 10

11 12 13 14 15

16

Sector No.

Fig. 7: AE event energy density (Q) distribution in location sectors 1 to 16
The left part of Fig. 7 illustrates typical experiment where the total AE energy is predominantly
concentrated around defective place while in the right part experiment, the defect dominance is masked
by near random distribution.
In some cases, it is not easy to perform enough pencil-lead break calibration experiments to the trainingof NN algorithms described above. Nevertheless, the localization procedure may be successfully appliedin such situation, as the theoretically computed velocity diagram can be used to construct localization(weighting) matrices Li of time differences ∆tij. Once determined, velocity diagram may be usuallytransferred from one case to another when the same geometry and material are used [3]. If the theoreticalvelocity diagram cannot be easily obtained, it is possible to establish localization matrix in one dummy
application.
CONCLUSIONS
New philosophy for AE source localization under high background noise is designed. The algorithm isbased on probabilistic and fuzzy-neuro principles, so AE events may be classified according to theirenergy and location probability. It is shown that the AE source location distribution can bring more
information about a complex process than erroneous localization of individual AE events. The anotherAE event characteristics may be also included into the algorithm (in the NN weight matrix) to enhancesome important features of diagnosed process.

284

ACKNOWLEDGEMENT
The present research has been supported by the project No. P4.30 of the European COST P4 action.
REFERENCES[1] Z. Prevorovsky, M. Landa, M. Blahacek, D. Varchon, J. Rousseau, L. Ferry, D. Perreux : Ultrasonicscanning and AE of composite tubes subjected to multiaxial loading. Ultrasonics, 36(1-5), 1998, pp.
531-537.
[2] Z. Prevorovsky, M. Blahacek : Acoustic emission source location by artificial neural networks.
Tagungsband der IV.Kolloquium Technische Diagnostik, Dresden, 1996, p. 212.
[3] M. Blahacek. Acoustic emission source location using artificial neural networks. PhD Thesis,
Institute of Thermomechanics AS CR, Prague, 1999.
[4] H. Demuth, M. Beale. Neural Network Toolbox for Use with MATLAB, The Mathworks, Inc.,
Nattick, MA, (1994).
[5] I. Grabec, W. Sachse. Synergetics of Measurements, Prediction and Control. Springer-Verlag,
Berlin, (1995).

285

DYNAMICS AND DAMAGE ASSESSMENT IN IMPACTED CROSS-PLY CFRP
PLATE UTILIZING THE WAVEFORM SIMULATION OF LAMB WAVE
ACOUSTIC EMISSION
Y. MIZUTAMI, H. NISHINO, M. TAKEMOTO and K. ONO*
Aoyama Gakuin University, Setagaya, Tokyo, JAPAN*University of California. Los Angeles, CA, U.S.A.
ABSTRACT
Utilizing the simplex-assisted waveform simulation of the Lamb AE waves, we successfully estimatedimpact force histories in cross-ply CFRP plates impacted by 10 –43 m/s steel ball. The force amplitudes
estimated by the waveform simulation are compared with those calculated by energy balance and massspring models. The force estimated by the AE waveform simulation appears to represent the most
realistic value. The maximum force estimated from the measured plate deflection agreed with that by theenergy balance model at slower ball velocities, but deviated significantly at higher velocities. Criticalball velocity to cause large internal damage was detected by plate deflection monitoring.
INTRODUCTION
Cross-ply CFRP plates suffers various internal damages upon the collision with flying objects. The
estimation of impact force history and the detection of damage onset are critical for transportationequipment. Impact force has been intensively studied based on the energy balance (1) and mass-springmodels (2), (3). The former can calculate the force amplitude, and the latter the force history as well as
the displacement response. In spite of extensive numerical calculations, propriety of estimated impact
forces has not been verified by experimental work (4).
Accurate measurement of the impact force history was first attempted by the quantitative AE source
characterization. Force amplitude, duration and momentum of elastic impact (ball drop) on an infinite
elastic body were studied by direct inverse processing (decovolution) of bulk mode waves (5),(6),(7).
However, AE waves in a thin plate propagates as the Lamb waves. Source characterization of the Lambwaves is generally difficult due to their dispersive nature, more so due to large plate vibration inducedby the impact (8). Direct deconvolution of Lamb waves often fails.
In this study, we estimated the force history by the simplex-assisted waveform simulation of Lambwaves, utilizing the experimental transfer function. The force amplitudes estimated for ball velocities
from 10 to 43 m/s were compared with that estimated by energy balance and mass-spring models.
TEST SPECIMEN AND EXPERIMENTAL METHOD
Impact test method is shown in Fig.1. CFRP plates of 100x100 mm square with 2 mm thick, (04/904)sym.,
was firmly supported by a circular steel flanges with an inside diameter of 61 mm. A 7-mm diameter
steel ball (1.404 g mass) was accelerated by a nitrogen gas gun and guided by a steel pipe, hitting theplate at the center. Ball velocities, 10 to 43 m/s, were measured by a laser –photodiode pair near the endof the guide pipe. An AE transducer (PAC, PICO) was pressed to the plate via aluminum support, whoseends are firmly attached to the flange. Sensor output was attenuated by 20dB using a high impedance
attenuator and digitized by an A/D converter at 50ns sampling interval with 4096 points at 10 bits. We
monitored plate deflection by a laser displacement meter (frequency 50 kHz) from the bottom surface.
286

Fig.1 Experimental Setup for impact test.
RESULTS AND DISCUSSION
Force history estimated by waveform simulation
Figure 2 represents a flowchart for estimating the impact force history by simplex-assisted waveform
simulation. An asymmetric bell-shaped source function, approximated by 11th order Fourier series, wasoptimized by iteration so that the error between the computed and monitored waveforms was minimized.

Fig.2 Lamb waveform simulation for estimating the impact force function by the simplex method.
The Lamb waveform was obtained by the time domain convolution of the source function with theoverall transfer function determined as a response to a pencil-lead break(8). An example for 16-ply
CFRP impacted by 12 m/s ball is shown in Fig. 3.
287

The overall transfer function (a) was obtained by Gauss-Zeidel deconvolution of detected wave by astep-wise source function (lead break at angle 60o, force release of 5 N at 0.9 µs rise time). By matching
the synthesized waveform (c) to the detected one (b), asymmetric force history or the source function (d)with the maximum force of 245 N and duration of 15 µs was obtained Another examples at 20 and 23
m/s ball impact are shown in Fig. 4. Here, the waveforms were matched for S0-mode and first portion of
A0-mode (to 30µs), since the later portions are insensitive to the source function. Estimated force
amplitudes are shown in Fig. 5, together with those estimated from plate deflection and two theoreticalmodels.

Fig.3 Procedure for estimating the impact force history for 12 m/s ball impact. (a) overall transfer
function, (b) detected Lamb wave, (c) synthesized Lamb waves, (d) source wave (impact force history).

Fig.4 Overlapping of detected and synthesized waves (the upper) and estimated impact force history (thelower) by 20 and 23 m/s ball.
288

Fig. 5 Ball velocity vs. impact force F for cross-ply CFRP subjected to impact test.
( : Estimated from plate deflection, : Estimated from Lamb wave)
Force history predicted by mass-spring model
Displacement responses of the plate and an impacting ball were obtained by solving the kinetic
equations (1) and (2) for two-degree-of-freedom mass and spring model with the following initialconditions; x&1 =V x1 = x2 = x& 2 = 0 (1).
M i &x&1 + n (x1 − x2 )

1.5

=0

(1)

M p &x&2 + K bs x2 + K m x2 − n (x1 − x2 )31.5

= 0 (2)

λ= 1 for x1>x2 , λ= -1 for x1>x2
M1 and Mp designate the mass of the ball and one-quarter of plate mass, and x1 and x2 the displacementsof the ball and plate, respectively. Displacement response, for the case of 21 m/s ball impact, is shownin Fig. 6. Intersection of the two curves occurs at 25 µs indicating that the ball leaves the deflecting
plate and flies off in the opposite direction. The maximum deflection of the plate occurs at 60 µs.
Shown in Fig. 7 is the force history estimated from the Hertz contact law F1=n(x1-x2)1.5 . It was obtainedby using the instantaneous indentation depth: (x1- x2) and n=9.97x108 Nm2/3. The source functions in
Figs. 4 and 7 resemble each other with the almost same impact duration, which also agrees with the timeof the ball-plate separation shown in Fig. 6. However, the force amplitude (831N) estimated by the AE
waveform simulation for 23 m/s ball impact is 3.5 times smaller than that (2960N) predicted by the
mass-spring model. This difference may be due to the model ignoring the wave propagation effect andalso calculating the force from the maximum deflection when the ball had separated from the plate.

289

0.8

Displacement, mm

21 m/sSteel ball, x 1

0.6
0.4

collision

0.2
0
-0.2
-0.4

Plate, x 2

Separation of ball from plate

-0.6
-20

0

20

40
60
Time, usPlatelocation
beforeimpact

80

100

120

Fig.6 Displacement response of the ball and the plate predicted by the mass-spring model.
500

Impact force, N

0
-500

-1000
-1500
-2000
-2500
-3000

F=2960 N

-3500
0

5

10
15
Time, us20

25

Fig.7 Impact force history of cross-ply CFRP plate predicted by the mass-spring model.
(V=23 m/s)
Force amplitude estimated by energy balance model and measured plate deflection
Force amplitude, denoted as F2, can be estimated by the energy balance model; Eqs. (3) and (4), by
equating the kinetic energy of the ball to the sum of the energies due to contact, bending, shear andmembrane deformation.

W designates the plate deflection at impact position. This model assumes that both x1 and x2 reach theirmaxima at the same timing, while the spring-mass model (Fig. 6) predicts different timing. Thus, themaximum force F1 estimated by the mass-spring model differs from F2 predicted by the energy balance
model.
We measured the plate deflection w in Eq. (4) (see Fig. 8) by a laser displacement meter, and calculated
the maximum force F2. Results are shown in Fig. 5 as filled circles. These values agree with F2
predicted by the energy balance model (the lower curve in Fig. 5) up to 30 m/s velocity.
290Fig. 8 Displacement response of CFRP plate impacted by 11 to 35 m/s ball.
It is noted that the plate deflection shows symmetric shape at lower velocities (the left of Fig. 8), but
asymmetric shape with fast rise and slow recovery at higher ball velocities (the right). This transition
appears to be due to the internal damages, as discussed later. The maximum deflection (488 µm) andtiming (103 µs) by 21 m/s ball impact agreed fairly well with that (426 µm and 163 µs in Fig. 6)
estimated by the mass-spring model.
Change of impact force as a function of ball velocity
Impact forces estimated are represented in Fig. 5 as a function of ball velocities. The mass-springmodels shows the largest values, since it calculated the idealized (and unrealistic) Hertz contact force.
The force calculated from the measured deflection are on the curve of the energy balance model at ballvelocities below 21 m/s, but deviates from the curve above 30 m/s. This deviation is found to be due tothe onset of large internal damage. Post-impact ultrasonic C-scan inspection (Fig. 9) revealed a large
extension of delamination at back surface (tension side) at around 28 m/s. Plate deflection monitoring
can be used for inspecting the critical internal damage onset in impacted CFRP plates. The force
amplitude calculated from the measured plate deflection is two times larger that estimated by the AE
waveform simulation. This overestimate appears to be due to the continued deflection of the plate byinertia, as shown in Fig. 6. From the consideration, the force estimates by the AE waveform simulation
appear to represent the most realistic values.
CONCLUSION
We estimated the impact force histories in impacted cross-ply CFRP plate by the waveform simulationof Lamb AE signals, and compared them with those predicted by the mass-spring and energy balance
models. Results are summarized below;
1) Waveform simulation method can correctly predict the force history, since AEs are monitored before
the onset of plate deflection. Impact force increased with ball velocity, while the duration is almost
constant, irrespective of the ball velocity.
2) The force estimated by the AE waveform simulation appears to represent the most realistic value. The
present methodology of using experimental transfer function and simplex-assisted matching of
calculated and observed waveforms can be a useful tool in other circumstances as well.
3) The impact force estimated from the measured plate deflection agreed with that estimated by theenergy balance model at slower ball velocities, but deviates above the critical velocity where large
delamination has occurred.
291

ACKNOWLEDGMENTS
A part of this research was supported by the fellowship from the Japan Society for Promotion of Scienceto Y. Mizutani.
REFERENCES
1

K.N. Shivakumar: Transactions of the ASME, Vol.52, 1985, pp.674-680
S. Abrate, IMPACT ON COMPOSITE STRUCTURES, Cambridge Univ. Press, (1998), p.85
3
Goldsmith, Impact: The Theory and Physical Behaviour of Colliding Solid, Arnold Publ., Ltd. London,(1960)
4
S. Abrate, IMPACT ON COMPOSITE STRUCTURES, Cambridge Univ. Press, (1998), p.85
5
N.N. Hsu and S.C. Hardy, AMD-Vol.29, ASME(1978)85.
6
T. Ohira and T. Kishi, The iron and Steel Institute of of Japan, 70(1984), pp. 2188-2195.
7
M. Takemoto and Y. Hayashi, J. Acoutic Emission, 7-4, (1988), p.185
8
Y. Mizutani, F. Nishiwaki, M. Takemoto and K. Ono, 2nd ESIS TC4 Conference on Polymers andComposites, Sept., 1999 (in press).
2

Fig. 9 C-scan images of internal damage in cross-ply CFRP plate impacted by 21 and 28 m/s ball.

292

ACOUSTIC EMISSION DETECTION DURING STRESS CORROSIONCRACKING AT ELEVATED PRESSURE AND TEMPERATURE
R. VAN NIEUWENHOVE and R. W. BOSCH
Studiecentrum voor Kernenergie – Centre d’Etude de l’Energie nucléaire,
Reactor Materials Research, Mol, Belgium

ABSTRACT
In order to test the feasibility of using acoustic emission for the detection of stress corrosion cracking
under pressurized water reactor conditions, experiments were carried out on sensitised and solution
annealed AISI 304 cylindrical samples, loaded by an SSRT unit in an autoclave. To enhance intergranular cracking, the measurements were carried out in an oxygen containing solution of 0.01 M
Na2 SO4. Using the SSRT pull rods as waveguides, it has been demonstrated that acoustic emission
events can be detected outside the autoclave, revealing increased activity during the elastic-plastictransition and in the plastic region before the actual failure of the sample. Whereas this increased
activity could be caused both by dislocation movements and real cracking events, simultaneouselectrochemical impedance spectroscopy measurements seem to favor the interpretation that real crack
initiation events are being detected.
INTRODUCTION
Due to the increasing age of nuclear power plants, stainless steel core components suffer from increasingradiation damage. These irradiated stainless steels are susceptible to stress corrosion cracking, in thiscase called irradiation assisted stress corrosion cracking (IASCC)[1]. To study this complex
phenomenon, it is important to monitor not only the crack propagation rate but also the exact time ofcrack initiation. Acoustic emission (AE) is a powerful and sensitive technique [3,4], but it’s application
to pressurized water reactor (PWR) conditions poses a real challenge. A feasibility test has been
performed in an autoclave, equipped with a slow strain rate tensile testing (SSRT) unit at hightemperature (300°C) and pressure (105 bar).
EXPERIMENTAL SET-UP AND CONDITIONSAutoclave system and material
The experiments were carried out in a Hastelloy autoclave equipped with a double SSRT loading unit
system (see Fig. 1). The experiments were carried out on AISI 304 cylindrical samples (length 10 mm,
diameter 2.5 mm) in an oxygen containing solution of 0.01-M Na2 SO4 at 300°C, which should result ininter-granular stress corrosion cracking (IGSCC) [2]. Before heating the autoclave, the autoclave waspressurized with 10 bar of air to ensure that the test solution contains a large amount of oxygen. The
experiments were conducted under static conditions, i.e. no recirculation of the test solution was
performed. In each test, one sample was subjected to a constant elongation rate of 1x10-6 s-1 while theother sample was free of stress. One experiment was performed on solution-annealed AISI 304, while asecond one was carried out at 300 °C on sensitized (621°C) AISI 304.

293

Acoustic emission detection system
The AE sensors (DECI SE25-P; broadband, single-ended) were mounted on the SSRT pull rods which
served as waveguides (see Fig. 1). The sensor signals were amplified by preamplifiers (type AEP4; 34dB) and fed to a Vallen AMSY4-PC system, equipped with a 300-kHz low-pass filter. The low
frequency-limit was chosen deliberately to avoid high-frequency electrical noise generated by the
autoclave heating system. Reducing the electrical noise to an acceptable level was found to be very
difficult. An extra grounding pin was installed just outside the laboratory to provide a more effective
grounding of the whole SSRT and autoclave structure. Even then, there was some correlation between
the electrical-heating phases of the autoclave and the electrical noise. To reduce this effect on themeasurement, a signal was derived from the heating system, which was fed into a data acquisition
control input of the AMSY4 system, such that data were only recorded during relatively ‘quiet’ heatingphases. In a next step, differential AE sensors will be used to reduce the noise even further.
Electrochemical impedance spectroscopy system
Every hour, electrochemical impedance spectroscopy (EIS) measurements were performed quasisimultaneously at both samples. The EIS measurements were performed with one potentiostat and one
frequency response analyzer connected to a multiplexer. Pt wires were used as pseudo-referenceelectrodes while the autoclave was used as counter electrode. The impedance measurements were
performed from 1000 to 1 Hz with amplitude of 10 mV RMS. One measurement (containing all
frequencies) took about 3 min. The difference in phase shift between the stressed and the non-stressed
sample, at low frequency (1 Hz) was found to be a sensitive indicator of cracking [5].
load
AE sensors

CEPre-amplifiers

Pt-wire (RE)
autoclavetensilespecimen(WE)

Vallen AMSY4

Fig. 1: Experimental set-up.
294

MEASUREMENTS
For the case of the solution-annealed AISI 304, the rate of the cascaded hits and the amplitude (bothwith a time step of 0.2 h) are plotted in Figs. 2 and 3. Strong AE can be observed at t = ~20 h, whichcorresponds to the transition between the elastic and plastic deformation regions. Between 30 and 40 h(already in the plastic region), again strong AE activity is observed and finally also before (around 87 h)
the sample breaks (at t = 95 h). Simultaneous EIS measurements on the solution-annealed AISI 304 areshown in Fig. 4, revealing also crack initiation at t = ~20 h (deviation from zero phase difference).
Often, AE at the elastic-plastic transition region is attributed to the motion of dislocations [3, 4], but thesimultaneous observation of a phase shift by EIS seems to support the view that the AE events aremainly caused by cracking. SEM pictures of the fracture surface of the solution-annealed AISI 304
sample are shown in Fig. 5, revealing trans-granular stress corrosion cracking.
For the sensitized AISI 304, the rate of the cascaded hits and the amplitude (both with a time step of 0.2h) are plotted in Figs. 6 and 7. An increased hit rate is already observed several hours before the final
cracking of the sample. The amplitude of the hits in this period is, however, lower as compared to theelastic loading phase (from 0 to 13 hours).

Fig. 2: Rate of cascaded hits (time step 0.2 h) during the SSRT test on solution annealed AISI 304
revealing several signatures of crack initiation (at 20 h,30-40 h,87-90 h).

295

Fig. 3: Amplitude of hits (time step for averaging: 0.2 h) during the SSRT test on solution-annealed AISI
304 revealing signatures of crack initiation.
450

12

400

Load

10

Delta 1350

2

Stress (N/mm )

250

6

200

4

150

Delta phase shift (°)

8
300

2
100
0

50
0

-2
0

20

40

60

80

100

120

Time (hours)

Fig. 4: Phase difference between a stressed and a non-stressed AISI 304 solution annealed sample, as
obtained by EIS measurements, starts to deviate from zero at ~20 hours, providing a
possible indication of crack initiation.
296

Fig. 5: SEM pictures of fracture surface of the solution-annealed AISI 304 sample, showing transgranular fraction (stress corrosion cracking).

Fig. 6: Rate of cascaded hits (time step: 0.2 h) vs. time in h during the SSRT test on sensitized AISI 304
revealing several signatures of crack initiation (at 5-10 h, and at 20-25 h)297

Fig. 7: Amplitude of hits (time step for averaging: 0.2 h) vs. time in h during the SSRT test on sensitized
AISI 304 revealing signatures of crack initiation.
CONCLUSION
Acoustic emission detection can be successfully applied to detect stress corrosion cracking on samples
located in an autoclave at high temperature and pressure.
ACKNOWLEDGEMENTS
The authors wish to acknowledge Ludo Eysermans for the autoclave and sample preparations.
REFERENCES
1. S.M. Bruemmer, E.P. Simonen, P.M. Scott, et al., Radiation-induced material changes andsusceptibility to intergranular failure of light water-reactor core internals, Journal of Nuclear
Materials, 274 (1999), 299-314
2. M.E. Indig, A.R. McIlree, Corrosion, 35(7), (1979), 288.
3. H.N.G. Wadley, C.B. Scruby, and J.H. Speake, Acoustic emission for physical examination of
metals, International Metals Review, 1980, No. 2, Review 249, 41.
4. W.W. Gerberich, et al., Acoustic Emission Monitoring of Stress Corrosion Cracking, Materials
Science and Engineering, A103 (1988).
5. R.-W. Bosch, Investigation of stress corrosion cracking at elevated temperatures with Electrochemical Impedance Spectroscopy, EUROCORR 99, 1999, Aachen.
298

RELIABILITY OF ACOUSTIC EMISSION TECHNIQUE
TO ASSESS CORROSION OF REINFORCED CONCRETE
H. IDRISSI and A. LIMAM*
Physico-chimie Industrielle, I.N.S.A. Lyon, Lyon, FRANCE* U.R.G.C. “Structures”, I.N.S.A. Lyon, Lyon, FRANCE

ABSTRACTThe deterioration of reinforced concrete structures is of major concern. Most of the damages are theconsequences of reinforcement (steel-rebar) corrosion. Rebar corrosion can be induced by chloride or bycarbonation. It is well known that the chloride rebar corrosion is more detrimental and that this processcontains three basic components: chloride diffusion, electrochemical corrosion and concrete fracture.
Therefore, the early detection and the checking of state of corrosion of reinforcing rebars by nondestructive techniques are important not only in an academic point of view but in practical applications
concerning structure’s maintenance and repair. This paper presents the results of an experimental
investigation on the use of acoustic emission during the corrosion of steel rebars embedded in mortar
and immersed in sodium chloride solution. The process of corrosion is accelerated by various imposed
potentials and is followed by AE coupled to electrochemical techniques. The experimental results showthat AE methods can detect the initiation steps damage and a perfect correlation between the evolutionof the acoustic activity and the current of corrosion density. This could allow us to evaluate the corrosion
rate and to simulate the long-term degradation behavior of reinforced concrete.
INTRODUCTION
Nowadays, the durability of concrete structures has become a matter of concern, primarily because of thecorrosion of steel reinforcement. The problem is not due to mechanical strength deterioration of rebar, as
only a few percent of the steel cross-section is corroded after 10 years [1], but it is because the corrosion
products exert stresses within the concrete, which cannot be supported by its limited plastic deformation,and the concrete therefore cracks [2], leading to delamination and spalling of the concrete cover. The
break-down of the steel-concrete interface can lead to the failure of the structure. The tendency of steel
rebars to corrode or not depends on its potential corrosion evolution in its environment. Hence,
infrastructure corrosion is linked to the composition and to the fabrication process of the concrete; it
depends particularly on the presence of aggressive agents such as chlorides, sulphates, acids, in theemployed materials and in their environment of use.
The processes or mechanisms of rebar corrosion are well known [3-5]. The importance of both chloride
and hydroxide ions in reinforcement corrosion process is studied by several authors [6,7]. While most of
the concrete damage processes are well explained and studied with accuracy by laboratory techniques,current inspection methods of reinforced concrete structures lack accuracy and can provide sufficient
information only after corrosion has occurred.
This study aims at establishing a laboratory technique, based on the coupling of acoustic emission withelectrochemical methods allowing, on the one hand, early detection and control of the reinforcements
corrosion in the concrete, and on the other hand, the simulation of the long-term behavior of reinforced
concrete degradation. The degradation mechanism of mortar, and the reinforcement corrosion itself, wasfollowed by acoustic emission. The acceleration of the corrosion process was carried out by various
307

imposed potentials. The obtained results first show a perfect correlation between the acoustic activity
and the behavior of reinforcements when corroded in the NaCl solution.
TEST SPECIMENS, EXPERIMENTAL DEVICE AND PROCEDURE
The samples used in this study are made from a normalized mortar with a water-cement ratio of 0.5. The
composition of this mortar is given in Table1. The dimension of these samples (Fig. 1) was defined in away representative of the real structures, and the standard cover concrete dimension is preserved.
Table 1: Chemical composition of concrete.
Component
Water / Cement
Water
Portland cement CPA 52-5
Normal sand ISO 679

Weight (g)
0.5
225
450
1350

steel reinforcement
φ 30 mm
90 mm

φ5 mm

60 mm

50 mm
epoxy coating
Fig. 1: Test specimen.
The mortar was mixed automatically and poured in PVC mould in which a carbon steel reinforcement, 5
mm in diameter and 60 mm length, was placed along the mould longitudinal axis. The sample is thencompacted with the assistance of a vibrating table during 60 s, then cured in a climatic chamber, with arelative humidity higher than 50% during 4 weeks. The upper face of the cylinder is polished with asilicon carbide paper (standard P180) so as to obtain a smooth surface giving a better contact interface
with the sensor of acoustic waves. The contact for electrochemical measurements is ensured by directconnection on the steel reinforcement. An epoxy resin coating is then applied to the lower face of thesample so as to impose the infiltration of the solution only by the side faces.
The experimental device used is composed of two systems (Fig. 2):
1. An electrochemical system of measurement consisted of a potensiostat Tacussel-Radiometeur PGZ
301 coupled to a computer and of a software of acquisition Voltamaster 4, with the sample being theworking electrode, a platinum mesh as the counter-electrode and a saturated calomel electrode(Hg/Hg2Cl2/KCl) as a reference. In order to avoid acquisition of acoustic noise induced by hydrogen308

potentiostat TACUSSEL PGZ 301

Electrodeof work

R eferenceElectrode

Auxiliary
ElectrodepH - meter

Acquisition System
Voltamastervers. 1.0
CaptorSEC

EA

Acquisition

Pre - amplificator

System of filter

Electrolytic bridge

Sampleamplification
VALLEN

NaCl solution- AMS3Fig. 2: Experimental device.

evolution from the counter-electrode during anodic polarization of the specimen, the platinum mesh
had to be placed in a near-by annex cell connected to the corrosion cell via a salt bridge.
2. An instrumentation of AE consisted of a transducer, a preamplifier and an acquisition device(MISTRAS from Physical Acoustic Corp.). The transducer is resonant R15 type from PAC. It has
been selected because of their high sensitivity around the value of 150 kHz. The acquisition system
was completely computer controlled. The waveforms and the characteristic acoustic parameters(events, amplitude, rise-time, energy, counts number, duration) are stored on a hard disk as soon as
detected, and are available for treatment under the form of ASCII files, as well as the electrochemicalparameters.
The non-acoustic parameters such as the free potential of corrosion, the pH of the medium, and thecurrent of corrosion density, were recorded during tests, through the analog input available on thissystem.
Potentiostatic tests were carried out on the standardized reinforced concrete in a 1M-NaCl (58.44 g/l)
solution. The applied potential is 500 mV/SCE. It was selected according to the Pourbait diagram [8] inorder to avoid the release of oxygen coming from the reaction of water reduction.
RESULTS AND DISCUSSION
pH Evolution: The experiment consists in following the evolution of the medium pH, initially neutral,in order to study the action of the concrete on the reinforcement during the immersion of the sample inthe solution. Indeed, Fig. 3 shows that, from the immersion, the pH increases quickly to reach a value ofapproximately 11.5. In the same way, a gradient of pH was measured in the mass of the mortar. With309

this pH value, the steel reinforcement is naturally protected by the formation of a protective coating on
its surface [8]. On the other hand, when the immersion is alternated every 24 h with drying of the sample
and renewal of the medium, the pH decreases [9] to reach a value of approximately 8, for a test total
duration of 200 h. Under these conditions, the deterioration of the protective coating is easier,particularly with the presence of chloride in a medium of low alkalinity.
12

PH

11
10
9
8
5E2

10E2
15E2
Time (s)20E2Fig. 3: Evolution of medium pH during the immersion of the sample.
AE study: The recording of the detected AE signals, under our experimental conditions, show three
noted types of waves, A, B and C. The types A and B signals are characterized by shorter duration andrise times (lower than 20 µs). This result is illustrated in the three-dimensional presentation of duration,
time and rise time (Fig. 4). This presentation also shows that the AE activity starts after a latency time of
approximately 10 h.
As for the follow-up of the current density and of the AE activity evolution recorded (Fig. 5), it showsthat there is a close connection between electrochemical measurements and the AE activity. The shape ofthese curves can be divided into three steps:
1. A first step of relatively long duration, during which the current density I and the AE activity density
remain low. This step can be associated with the infiltration of the medium through porosity andwith the medium modifications in the vicinity of the reinforcement by diffusion of ions Cl–. Under
these conditions, the corrosion of the reinforcement is weak and the energy resulting from theelectrochemical reaction is insufficient to be emissive. On the other hand, the rupture of the layer
previously formed at free potential on the reinforcement surface can be responsible for the signals
recorded during this step (Fig. 6a). Their duration is weak (<20 µs) and their rise time is about 5 µs.
2. A second step characterized by an increase in current density I and in AE (Fig. 6b). This activity canbe explained if it is considered that the diffusion of chlorides, more particularly when theconcentration ratio of the ions [Cl–]/[OH–] is higher than 0.6 [6,7], leads again to the dissolution of
the reinforcement. Thus, the accumulation of corrosion products at the interface ofreinforcement/concrete develops stresses on the internal surface of the concrete. This leads to theinitiation of microscopic cracks, which are AE sources [10,11]. The population of signals is of type
B and is characterized by a rise time lower than 20 µs.

310

2

60

1

Duration (µs)

40
0

20

Rise time (µs)

3

400
800
0

10E4

5E4

15E4

Time (s)Cumulated signals number

Intensity ( 10-1 mA/cm2)

Fig. 4: 3D Correlation curve (Duration – Time – Rise time) for normalized reinforced concrete.

Time (s)Fig. 5: Simultaneous evolution of current density (a) and AE (b) for normalized reinforced mortar.
3. A third step corresponding to a notable increase in the AE activity, which seems to be the consequence of the microscopic crack development and their propagation in the concrete to discharge thecorrosion products. The friction of the latter against the walls of these cracks and pores may also beemissive source. The characteristics of the signals detected during this step (Fig. 6c) are different
from those recorded beforehand in the second step, in particular in term of duration and rise time.
The concrete matrix being fissured, and the infiltration of the electrolyte in the vicinity of thereinforcement is characterized by a new corrosion current increase and by signals of more diversified
characteristics.

311

Amplitude (mV)

Amplitude (mV)

A

Time

Time

Amplitude

C

Time (µs)

Fig. 6:Waveform recorded for corrosion of rebar in concrete.
CONCLUSION
The corrosion current measured during the degradation of the samples shows that the protective partplayed by the concrete on the reinforcements is not always assured. This corrosion current is conditionedby the density of concrete open porosity, which represents the surface of reinforcement in contact withthe electrolyte. The accumulation of corrosion products at the reinforcement/concrete interface causes
microscopic cracks in the reinforced concrete matrix.
The perfect correlation between the AE activity and the corrosion current density shows that the AE
detection of the degradation of the reinforced concrete infrastructures seems to be completely feasible.
Furthermore, the AE parameters related to the detected AE signals, such as the rise time and energy, canconstitute significant components to monitoring and even to qualify the damage.
The AE is a powerful non-destructive technique by its capacity to detect the degradation of reinforced
concrete from the first step of the process. It appears necessary to look further into research in this fieldwith an aim of controlling real reinforced concrete structures. For that purpose, the characteristics of theemitted signals need to be better correlated with the nature of the physical phenomena, which generatethem during the various steps of corrosion.
312

REFERENCES[1] R. DUVAL. La durabilité des armatures et du béton d’enrobage, in La durabilité des bétons. Presses
de l’Ecole Nationale des Ponts et Chaussées. Edited by J. BARON and J.P. OLIVIER.
[2] J.E. SLATER, Magnitude of the problem, ASTM – STP 818. Corrosion of Metals in Association
with Concrete, pp. 5-9, 1983.
[3] C.L. PAGE and K.W.J. TREADAWAY, Aspects of the electrochemistry of steel in concrete, Nature,
297 (1982), pp. 109-115.
[4] L. HACHINI, J . CARPIO, E. TRIKI, C. FIAUD, A. RAHARINAVIO, Steel corrosion in concretes
deteriorated by chlorures and sulphate : Electrochemical study using impedance spectrometry andstepping down the current method. Cement and Concrete Research, V. 22; 1992; pp. 56-66.
[5] K. VIDEM, R. MYRDAL, Electrochemical Behaviour of steel in concrete and evaluation of thecorrosion rate. Corrosion, V. 53; No. 9; 1998; pp. 734-743.
[6] D. A. LEWIS, Some aspects of the corrosion of steel in concrete, Int. Proc. 1st International
Conference Met. Corr., London, 1962, pp. 547-555.
[7] S. VASANTH, N. S. RENGASWAMY, S. SRINIVASAN and K. BALUKRISHNAN, Evaluation ofIndian Pozzolan Cements for their Corrosion Resistance, Transaction of the Society for
Advancement of Electrochemical Science and Technology, 2 (1988), pp. 273-277.
[8] H. POURBAIX. Atlas of electochemical equilibria in aqueous solutions, Oxford, (1966).
[9] L. HACHINI, E. TRIKI, D. MOUSSA, A. RAHARINAVIO, Etude expérimentale de la dégradationdu béton armé dans diverses solution aqueuses. Bull. liaison Labo P et Ch- Réf. 3526, 169, (1990) ,pp. 69-75.
[10] A.PROUST, P. FLEISCHMANN, H. MAZILLE, R. ROTHEA, Acousitic emission monitoring ofstress corrosion cracking on ferritic, austenitic and duplex stainless steels". Les Editions de Physique(France) (1993), pp. 925-934.
[11] H. CIFLET, H. IDRISSI, F. FERRER, F. DALART, Etude du claquage de la couche d’oxyde duzircaloy-4 en acide nitrique concentré et chaud par émission acoustique. Journées d’électrochimie,
Toulouse, 1-4 June 1999.

313

e-mail Address of Author
SurnameAnastasopoulos
BAYRAYBLAHACEK
CATTY
COLE
DUBIEL
DVORACEKFERRER
FINLAYSONGOMERAHERVEIDRISSIIVANOVLENAIN
MAJEWSKAMAZALNISHINOONO
PININSKA
PRATEEPASEN
RAUSCHERRAZUVAEVREUBENROWLANDSHIOTANISKRABERSURGEON
TSCHELIESNIGTSIMOGIANNISVALLENVAN DE LOOVAN NIEUWENHOVEWEBER
WEVERSYOSHIDAFirst name
Nassos
Mulu
MichalJohannPhil
Ryszard
Viri
Fréderic
RichardViktorCatherine
HassaneValeryJean-Claude
Zofia
Pavel
Hideo
Kanji
Joanna
Asa
Franz
Igor
Bob
Chris
Tomoki
Alain
MarnixPeter
Apostulos
Hartmut
Peter J.
Rudi
ZdenekMartineKenichie-mailnassos@envirocoustics.gr
mbayray@iaa1.iaa.tuwien.ac.at
blahacek@bivoj.it.cas.cz
johann.catty@cetim.fr
ptc@pacuk.co.uk
dubiel@uranos.cto.us.edu.pl
dvoracek@cms.fme.vutbr.cz
frederic.ferrer@crde.elf-atochem.fr
rfinlayson@pacndt.com
Gomera_V_P@kinef.ru
catherine.herve@cetim.fr
idrissi@insa-lyon.fr
pac.rus@g23.relcom.ru
epa.jcl@wanadoo.fr
majewska@geol.agh.edu.pl
mazal@cms.fme.vutbr.cz
nishino@me.aoyama.ac.jp
ono@ucla.edu
joannap@geo.uw.edu.pl
Asa.Prateepasen@brunel.ac.uk
f.rauscher+E329@tuwien.ac.atalcor@alcor.nnov.ru
R.L.Reuben@hw.ac.uk
101750.2134@compuserve.com
shiotani@mx1.alpha-web.ne.jp
alain.skraber@cetim.fr
marnix.surgeon@metalogic.be
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martine.wevers@mtm.kuleuven.ac.beyoshida@me.tokushima-u.ac.jp

Theoretical Treatment of AE in Massive Solid
Masayasu Ohtsu
Graduate School of Science & Technology, Kumamoto University
2-39-1 Kurokami, Kumamoto 860-8555, Japanohtsu@gpo.kumamoto-u.ac.jp
1. BackgroundThe generalized theory of acoustic emission (AE) has been established on the basis ofelastodynamics (Ohtsu and 000, 1984). Thus, it is clarified that AE waves are elastic waves
due to dynamic failure in a solid.
Here, AE waves due to microcracking are discussed in
massive solids, in particular, in concrete.
Theoretical treatment of AE in concrete was studied
as elastic waves in a homogeneous medium (Ohtsu, 1982).
The results were remarkably
successful, whereas concrete is not homogeneous but heterogeneous.
This is because
elastodynamic properties of material constituents are physically dependent on the relation
In the case thatbetween the wavelengths and the characteristic dimensions of heterogeneity.
the wavelengths are even larger than the sizes of heterogeneous inclusions, the effect ofheterogeneity is inconsequent. This is the case of massive solids such as concrete and rock, if
the sizes of specimens are large enough compared with the wavelengths.
For AE waves in concrete, the velocity of elastic waves is over 1000 m;s, and thus thewaves of frequency range up to some 100 kHz correspond to the wavelengths of several
It results in the fact that the concrete specimen consisting of normal aggregate (ofcentimeters.
The wavelengths to bearound 10 mm diameter) is reasonably referred to as homogeneous.
detected should be shorter than characteristic lengths of the specimen or the propagating medium
in order to detect body waves which are responsible for the source mechanisms.
This isoccasionally not the case of AE waves in plate specimens of steel, of ceramics, and ofcomposites, where the thickness of the plate is normally shorter than the wavelengths. In thecase, diffracted and dispersive waves are dominantly observed.
Because these waves are notassociated with generating mechanisms, source characterization of AE in the plate specimen isnormally a difficult task.
2. Fundamentals
Elastodynamic solutions of wave motions u(x,t) are mathematically represented as,(1)

where u(x, t) and u(y, t) are displacements, and t(y, t) are tractions. The asterisk symbol *represents the convolution integral in time. Gik(X,y,t) are Green's functions and Tik(X,y,t) are
the associated tractions with Green's functions,(2)
Here Cpqji are the elastic constants, and GiP,q(x,y, t) are the spatial derivatives of Green's
functions as they imply f} GiP(x,y ,t)/ f} xq. n is the normal vector to the boundary surface S.
In the boundary element method (BEM), eq. 1 is directly digitized and numerically solved.
For theoretical development, first, only one force f(y, t) is applied at point y OIl the boundary.
Thus, eq. 1 becomes,(3)This is the governing equation in the case where a dynamic force is applied to the specimen.
From eq. 3, the physical meaning of Green's function is readily obtained as Gi/x,y, t) is an51

elastodynamic solution of the displacement in the Xi direction at point X and at time t due to an
Since Green's functions are
impulsive force in the x. direction at point Y and at time 1.
dependent on not only m~terial properties but also on configuration of the medium, they have to
be computed numerically except for an infinite space where analytical solutions are known.
Semi-analytical solutions are also known in a half space (Ohtsu and Ono, 1984) and in an infinite
plate (Pao and Ceranoglu, 1981). For a finite body, numerical solutions of Green's functions
are reported as the waves due to a monopole source (Hamstad, O'Gallagher and Gary, 1999)
A famous experiment of pencil-lead break (Hsu and Hardy, 1978) is also represented by eg.
3. A pencil-lead break is known to generate a Heaviside step function force, H(t)ej(y), wheree(y) is the unit direction vector at point y.
Since the convolution with the step function leads
to the integration, eg. 3 becomes,(4)
Accordingly, Green's function of the arbitrary specimen, Gij(x,y), due to the pencil-lead breakat point y in the xj direction could be empirically obtained from,(5)
3. Crack Modeling
In order to model a crack as an AE source, the boundary surface S in eg. 1 is replaced by
To introduce the discontinuity of the displacements,
internal surface F of a crack surface.
Before a crack is nucleated,
virtual two surfaces F+ and F are considered as shown in Fig. 1.
these two surfaces make a concurrent motion.
Due to cracking, the discontinuity ofdisplacement n(y, t) is nucleated between the two surfaces and denoted by using superscripts +and - on surface F+ and F-,

Vector b(y, t) is called the dislocation and is identical to Burgers vector.
the surface F, eg. 1 becomes,

Setting t(y, t) = 0 onuk(X,t) = f FJ - Tki+(X,y ,t)*u/(y ,t)]dF,+ f FJ- Tki-(X,y ,t)*ui"(y ,t)]dF.

(7)

Here Tik + and Tik - contain the normal vector n+ and n°, respectively.
and F = F-,

Assuming n = n" = -n+,uk(X,t) = - f FTki(X,y ,t)*[ -ui+(y ,t)]]dF + f F[ - Tki(X,y ,t)*[ui-(y ,t)]dF= f FTkJX,y ,t)*[ui\y ,t) - ui-(y,t)]dF = J FTki(X,y ,t)*bi(y ,t)dF.

(8)Comparing eg. 3 with eg. 8, it is noted that Green's functions empirically obtained from a pencillead break are not definitely applicable to AE waves due to cracking.
From egs. 2 and 8,= GkP,q(x,y,t)*S(t) Cpqij n)i J Fb(y,t)dF(9)52

where 1 is the unit direction vector and Set) is the source-time function of crack motion. i1 V isthe crack volume. As can be seen, the amplitude of AE waves, u(x, t), is directly associatedThis implies that someonly with the crack volume, neither with the area nor with the opening.
attempts to determine the crack area or the magnitude of shear slip (Enoki, Kishi and Kohara,
1986: Dai, Labuz and Carvalho, 2000) are not rational, because only a disk-shaped crack is taken
into account. According to the simulation analysis (Ohtsu, Yuyama and Imanaka, 1987), thecrack volume estimated as the penny-shaped crack was more than 30% different from that of the
disk-shaped.
Equation 9 is the integral representation of AE wave due to cracking and can be applied tosimulation analysis as shown in Fig. 2.
Assuming a buried inclined crack (45° to the surface),
AE wave due to a tensile crack is synthesized.
The spatial derivatives Green's functions,
G kP q' in eg. 9 are computed in a half space.. As can be seen, AE wave detected in concrete is inreasonable agreement with the synthesized.
Here, the tensile crack is represented as the casethat vector 1 is parallel to vector 0, and elastic constants of an isotropic material are applied.
4. Moment Tensor
Since eg. 9 is fairly complicated and contains two vectors 1 and 0, it is not suitable for anThus, introducing moment tensor ~q' eq. 9 is simplified,inverse problem.

S F Cpqk1 b(y)lk n l dF = Cpqkllknl [ S Fb(y )dF= Cpqkllknl i1 V = ~q(10)uk(x,t) = GkP,q(x,y ,t) ~q *S(t).

(11)

The moment tensor, ~q' is defined by the product of the elastic constants [N/m 2 ] and the crack
In the case of an isotropic
volume [m3], which leads to the moment of physical unit [Nm].
material,~q =Alknk + 2/l11nl' /l(ll n Z + Izn1), /l(ll n3 + 13 n l)/l(lzn 1 + 1Illz), Alknk + 2/llzn z, /l(lzn3 + 13 nz)

i1V,

(12)

/l(l3 n l + 11n 3), /l(l3 n Z + ~n3)' Alkn k + 2/l(l3 n3)
where A and /l are Lame constants.

Originally, the seismic moment was defined by the

product /lbi1F of shear modulus /l, shear displacement b, and area of fault i1F.

Setting 1 = (1,0, 0), 0 = (0, 1, 0) (I is perpendicular to 0) and i1 V = bi1F in eq. 11, ~q = 2~lb~F.
Thus,
the tensor is named the moment tensor. This suggests that the moment tensor is comparable to a
stress due to crack nucleation, as a symmetric second-order tensor as shown in Fig. 3.
Crack kinematics are sometimes represented by such equivalent forces as dipole forces
The dipole forces are known as two forces of the same magnitude and theand couple forces.
The couple forces are two parallel andopposite directions on the coincident straight line.
opposite-direction forces with infinitesimal distance. Mathematically, these forces correspond
As can be seen in Fig. 3 (b), normal components of theto particular components of the stress.
moment tensor are identical to dipole forces, while couple forces correspond to tangential (shear)
components. The concept of the equivalent forces is sometime so misleading that nucleationof a tensile crack is represented by only a pair of dipole forces.
In the case of a pure tensile
Since all diagonal components contain the scalar product as
crack, a scalar product lknk =1.
given in eg. 12, the tensile crack should be modeled by three normal components (three pairsof dipole forces). In contrast, couple forces correspond to off-diagonal components in eq. 12.
Since the moment tensor is symmetric, double-couple forces are rational.

An application of the moment tensor analysis to AE waves was previously reported on
53cracking mechanisms of glass due to indentation (Kim and Sachse, 1984), where only diagonal
From eq. 12, it is realized that the presence of allcomponents of the tensor were assumed.
components is not actually associated with the type of the crack, but closely related with thecoordinate system. Although the crack orientations are often assumed as parallel to thecoordinate system (Saito, Takemoto, Suzuki and Ono, 1998), they are generally inclined to thecoordinate system mostly because of the configuration of the specimen. As a result, thepresence of all the components is consequent whether the type of the crack is of tensile or ofshear. In contrast, if the coordinate system is taken as parallel to the crack orientation, elastic
waves in eq. 11 could be represented as the vector obliquely incident to the surface which further
complicates the reflection at the surface and is not easily analyzed.
5. SiGMA CodeFor the analysis of the inverse problem, the spatial derivatives of Green's functions areinevitably required. Accordingly, numerical solutions are obtained by FDM (Enoki, Kishi andKohara, 1986) and by FEM (Hamstad, O'Gallagher and J. Gary, 1999). These solutions,however, need a vector processor for computation and are not readily applicable to processing alarge amount of AE wave.
Based on the far-filed term of P wave, therefore, a simplified
procedure was developed, which is suitable for a PC-based processor and robust in computation.
The procedure is now implemented as a SiGMA (Simplified Green's functions for Moment
tensor Analysis) code.
By taking into account only P wave motion of the far field (1/R term) of Green's function
in an infinite space, the displacement Uj(x,t) of P wave motion is obtained from eq. L1 as,(13)
Here p is the density of the material and vp is P-wave velocity. R is the distance between thesource y and the observation point x, of which direction cosine is r = (r p rz' r3 ). Considering
the effect of reflection at the surface and neglecting the source-time function, amplitude A(x) of
the first motion is represented,
m lP m12 , m l3
m 12 , mzz , m23
m l3 , m23 , m33(14)

where Cs is the calibration coefficient including material constants in eq. 13.
t is thedirection of the sensor sensitivity.
Ref(t,r) is the reflection coefficient at the observation
location x. In the relative moment tensor analysis (Dahm, 1996), this coefficient is not taken
into consideration, because the effect of the sensor locations are compensated. Since themoment tensor is symmetric, the number of independent unknowns ~q to be solved is six.
Thus, multi-channel observation of the first motions at more than six channels is required todetermine the moment tensor components.
From AE waveform, two parameters of the arrival time (P1) and the amplitude of the first
motion (P2) are determined. In the source location procedure, source location y is determinedfrom the arrival time differences. Then, distance R and its direction vector r are determined.
The amplitudes of the first motions at more than 6 channels are substituted into eq. 14, and theSince the SiGMA code requires only
components of the moment tensor are determined.
relative values of the moment tensor components, the relative calibration of the sensors is
sufficient enough.
Then, the cla'isification of a crack is performed by the eigenvalue analysisof the moment tensor. Setting the ratio of the maximum shear contribution as X, threeeigenvalues for the shear crack become X, 0, -X.
Likewise, the ratio of the maximum
It is reasonably
deviatoric tensile component is set as Y and the isotropic tensile as Z.
assumed that the principal axes of the shear crack is identical to those of the tensile crack.
Then, the eigenvalues of the moment tensor for a general case are represented by the54

combination of the shear crack and the tensile crack.
the SiGMA, eq. 16 is normalized and decomposed,

Because relative values are determined in
1.0 =the intermediate eigenvalue/the maximum eigenvalue
the minimum eigenvalue/the maximum eigenvalue

=

=

x+ Y

+ Z,

o - Y/2 + Z,

-x - Y/2 + Z,

(15)

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
In thecracks. In between 40% and 60%, cracks are referred to as mixed mode.
eigenvalue analysis, three eigenvectors are also determined, and then the vectors I and n, which
are interchangeable, are recovered.
In a three-point bending test of a concrete beam, SiGMA analysis was performed (Ohtsu
and Ohtsuka, 1998). Results are given in Fig. 4.
The tensile crack is indicated by arrow
The directions of two vectors I
symbol, while the shear crack is denoted by cross symbol.
and n are shown in their directions. It is found that both types of tensile cracks and shear
cracks are observed in the fracture process zone ahead of the notch in concrete.
Thus, themoment tensor analysis is very promising for practical applications to identify fracture
mechanisms in concrete.
REFERENCES
Dai, S. T., J. F. Labuz and F. Carvalho (2000),"Softening Response of Rock Observed inPlane-Strain Compression," Trends in Rock Mechanics, Geo SP-102, ASCE, 152-163.
Dahm, T. (1996), "Relative Moment Tensor Inversion based on Ray Theory: Theory and
Synthetic Tests," Geophys. J. Int., No. 124,245-257.
Enoki, M., T. Kishi and S. Kohara (1986), "Determination of Micro-cracking Moment Tensor of
Quasi-cleavage Facet by AE Source Characterization," Progress in A coustic Emission III,
JSNDI, 763-770.
Hamstad, M. A, A O'Gallagher and J. Gary (1999), "Modeling of Buried Monopole and Dipole
Source of Acoustic Emission with a Finite Element Technique, Journal ofAE, 17(3-4), 97-110.
Hsu, N. N. and S. C. Hardy (1978), "Experiments in AE Waveform Analysis for
Characterization of AE Sources, Sensors and Structures, Elastic Waves and Nondestructive
Testing of Materials, AMD-Vol. 29,85-106.
Kim, K. Y. and W. Sachse (1984), "Characterization of AE Signals from Indentation Cracks in
Glass," Progress in Acoustic Emission II, JSNDI, 163-172.
Ohtsu, M. (1982), "Source Mechanism and Waveform Analysis of Acoustic Emission inConcrete," Journal ofAE, 2(1), 103-112.
Ohtsu, M. and K. Ono (1984),"A Generalized Theory of Acoustic Emission and Green's
Functions in a Half Space," Journal ofAE, 3(1), 124-133.
Ohtsu, M. , S. Yuyama and T. Imanaka (1987), "Theoretical Treatment of Acoustic Emission
Sources in Microfracturing due to Disbonding," J. Acoust. Sco. Am., 82(2), 506-512.
Ohtsu, M. and M. Ohtsuka (1998), "Damage Evolution by AE in the Fracture Process Zone ofConcrete," J. Materials, Cone. Struct. Pavement, JSCE, No. 599N-40, 177-184.
55

Pao, Y. H. and A. N. Ceranoglu (1981), "Propagation of Elastic Pulse and Acoustic Emission in
a Plate," J. Appl. Mech., 48, 125-147,
Saito, N., M. Takemoto, H. Suzuki and K. Dno (1998),"Advanced AE Signal Classification forStudying the Progression of Fracture Modes in Loaded UD-GFRP," Progress in Acoustic
Emission IX, JSNDI, V-1 - V-lO.

X3

o

25

50

Time (I-lsec)( a) detected

X1
Fig. 1

Crack (dislocation) surfaces F+ and F.

o

12.8

25.6

38.4

51.2

Time (I-lsec)( b) synthesized
Fig. 2(a) Crack Motion
Fig. 3

AE waveforms in concrete.

(b) Moment tensor

Modeling crack motion by the moment tensor.

1:
x
'-#
+ mixed-modex& shear crack
Elevation view
Fig.4
S6

Results of SiGMA analysis of the notched beam.

Diagnosis of Concrete Structures by AE
Masayasu Ohtsu
Graduate School of Science and Technology,
Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, JAPANohtsu@gpo.kumamoto-u.ac.jp
1. Scope
Acoustic emission (AE) techniques are extensively studied in concrete engineering and are recently
going to be applied to practical use. In particular, the increase of aging structures and the disastrous
damage due to earthquakes substantially demand for the maintenance of reinforced concrete (RC)structures in service. It results in the need for the development of advanced and effective inspection
techniques. Accordingly, AE techniques draw an attention for diagnostic applications to RC structures.
By several authorities In Japan, activities for the standardization of AE testing procedure are in progress.
The concrete structures have their particular functions in service, which may deteriorate due to heavy
traffic loads, fatigue, chemical reactions, and other disasters. To perform their functions properly, repair
and rehabilitation of the structures are eventually necessary. In this case, an inspection on the current
state is necessary. Consequently, to estimate the degree of the damage or structural integrity of theexisting concrete structures, in situ monitoring techniques by AE are going to be standardized. Somefundamental results associated with these actions are reviewed.
2. Measurement
According to extensive research on AE in concrete (reported to properly detect AE signals.

Ohtsu, 1987), the following

conditions are(1) AE device for concrete usually requires total amplification around 60 dB to 90 dB. To decrease theeffect of noises on site, 1 MHz low-pass filter is desirable, as well as a high-pass filter over several kHz.
The noise should be lower than 20 mV as input.
(2) A monitoring system can measure at least some of the following parameters: count, hit, event,maximum amplitude, energy, rise time, duration, energy-moment, RMS voltage, frequency spectrum,
and arrival-time difference.
(3) Elimination of the noises shall be performed by setting the threshold level, filtering and post-analysisof the data. In any cases, the averaged amplitude of the noise should be managed to be lower than 10
mV as input.
(4) Inspection of the operating system shall be carried out routinely, checking that the variation ofsensitivities in the channels is within 3 dB. Sensor array shall be determined from the attenuation
properties of AE waves, setting the distance where attenuation of travel is less than 30 dB. Then, thefrequency range from 20 kHz to 100 kHz is recommended for in situ monitoring.
S7

3. Monitoring
For AE monitoring of existing concrete structures, it is essential to confirm that any AE signals
responsible for the damage are not observed under service conditions. In the case that AE signals not ofnoises but due to deterioration process are detected, the monitoring and the analyses shall be conducted.
The monitoring shall be performed continuously or routinely, and sometimes temporarily after thedisasters.
Due to the sudden increase of AE activity, the deterioration process and in many cases the impending
failure can be estimated. There is a report on AE activity under freezing and thawing process (Murakami
et al., 1991), where AE counts rapidly increase prior to the volumetric expansion associated withmicrocracking and scaling in concrete. Another result on continuous monitoring under corrosion of thereinforcement in RC beams is given in Fig. 1. By visual observation prior to the test, the beam atchannel 5 was found to be sound without any cracks, while one surface crack running vertical to thereinforcement axis was observed at that of channel 6. These beams were exposed on shore for threemonths. Due to rainfall, some AE activities were observed simultaneously at both the beams as seen in
the figure. At the beam of channel 5, AE activity always ceased right after the rain. In contrast, after
heavy rain at 50 days elapsed and 60 days AE counts were periodically observed following the activities
due to the rain. This was considered to be an evidence for corrosion of the reinforcement. Water of therainfall permeated into the concrete along the existing crack and probably corroded the reinforcement,
which could generate microcracks and AE signals in concrete around the rebars. In the test, thecorrosion in the beam at channel 6 was confirmed after removing rebars. These findings confirm anapplicability of AE monitoring to estimate nucleation of microcracks in concrete structures.

4. Trend Analysis
From a relation between any AE parameters and other physical parameters, its trend can be analyzed
and applied to the diagnosis. For example, the change of AE activity could be related with the rate of
S8the deterioration process. The rate-process analysis was introduced to evaluate quantitatively thechange of the activity. When concrete contains a number of critical microcracks, active AE occurrenceis expected under compression due to crack propagation from existing defects or microcracks. In
contrast, AE activity in sound concrete is known to be stable and low prior to final failure. Thus, toformulate AE activity under loading, the rate process theory is introduced. Probability function of AE
occurrence from stress level V(%) to V+ dV (%) is formulated as a hyperbolic function. Eventually, a
relationship between the number of total AE events N and stress level V(%) is obtained as,
N = C Va exp (bV).

(1)

Here a and b are empirical coefficients and C is the integration constant.
From previous research, the rate a is, in particular, known to be sensitive to the damage degree. Core
samples were taken from an aqueduct of a nuclear power plant. To drill core samples, three sites wereselected: at the gate (Site A), 20 m inland (Site B), and 30 m inland from the gate (Site C). Then, auniaxial compression test of the sample was conducted. Three samples at each site were tested. The
uniaxial compressive strength and the rate a were determined as the averaged value of the three. In
order to quantify microscopic damage, distribution of pore radii was also measured by the mercury
intrusion method from concrete fragments at the three sites. After determining the pore distribution, thevolume of pore radii over 0.5 mm was determined, because microvoids over 0.5 mm are predominantly
responsible for deterioration of concrete. Results of the pore volumes over 0.5 mm radius, the rate a,and the compressive strength are summarized in Fig. 2. From Site A to Site C, the pore volume over 0.5
mm radius decreases with the increase of the distance from the sea. This implies that the heaviest
damage was introduced in concrete at the gate (Site A), where concrete was frequently deteriorated byseawater. Apart from the seaside, it is expected that the damage of concrete decreases. In accordance

S9with the increase of the pore volume over 0.5 mm radius, the rates a increase from Site C to Site A.
Thus, the increase of the pore volume over 0.5 mm radius corresponds remarkably to the increase of therate a. It suggests that with the increase in the rate a, the volume of the microvoids responsible fordamage increases. Consequently, the trend analysis of AE occurrence could give us information on thedeteriorated degree of concrete.
5. Distribution Analysis
Distribution of AE parameters is analyzed in relation to the occurrence of AE counts, hits, and events.
The purpose of the analysis is variation of such AE parameters as RMS, energy, amplitude distribution
and so forth. Concerning the variation of AE parameter during the deterioration process, it is found thatthe change of amplitude distribution is useful. In Fig. 3, amplitude distributions observed in RC beams
under bending are shown. RC beams were damaged by cyclic loading of prescribed level. After
particular loading cycles, the beam was statically loaded up to the service-load limit, and AE wasmeasured. In Fig. 3(a), RC beam was repeatedly loaded at 85% static load-bearing capacity until final
failure. After several cycles of loading, amplitude distributions of AE signals were measured undermonotonous loading. With the increase of loading cycles, amplitude distributions shift to the
distribution where AE signals of the larger amplitudes are dominant. In contrast, there exist no change ofthe amplitude distribution in RC beam (Fig. 3(b)) which was repeatedly loaded at only 75% until 2, 500,
000 cycles. The beam was not broken in the test. This implies that amplitude distribution can beapplied to routinely inspection, investigating the variation of the distribution. The distribution is also
quantitatively estimated from the gradient of the slope, which is called the b value (Shiotani et al. 1999).

6. Clustering Analysis
From the arrival time differences, AE sources are located one-dimensionally, two-dimensionally, or
three-dimensionally. By applying AE location procedure, a relation between the location of AE clusterand existing defects is readily obtained. In Fig. 4, AE locations observed in a retaining wall are shown(Matsuyama et al., 1993). According to the two-dimensional locations of the raw data, wide scattering of
S10

AE locations is observed. Eliminating the noises and compensating the effect of the existing surface
cracks, AE locations are calibrated as shown in Fig. 4(b). All signals are mainly observed along theexisting cracks. This implies that AE events are mostly nucleated due to fretting of the existing cracks.
Thus, repair of the wall was carried out, just sealing the surface cracks. In addition to AE location, themoment tensor analysis is developed to classify the source into the tensile crack and the shear crack, andto determine crack orientations.

7. Estimation Based on Kaiser EffectConcerning AE activity under repeated loading, the Kaiser effect is well known. In RC beams, therelation between crack opening and the presence of the Kaiser effect was reported (Yuyama et al.,1996). In order to apply the Kaiser effect, further, to evaluate the damage of concrete structures, thefollowing two parameters are proposed.
(a) Ratio of load at the onset of AE activity to previous load:
Load ratio = load at the onset of AE activity under the repeated loading / previous load.
(b) Ratio of cumulative AE activity under unloading to that of previous maximum loading cycle:
Calm ratio = the number of cumulative AE activity during unloading / total AE activity at theprevious maximum loading cycle.
One result on the relation between these ratios and crack-mouth opening displacements (CMOD)
observed in RC beams tested is given in Fig. 5. Based on the ratios, one criterion the damaged degree ispresented in the figure. Agreement between the damage estimated from the maximum CMOD observedin the beams after the tests and the zone of the criterion is remarkable. This implies that the deterioration
S11

process and the current conditions of the concrete structure could be estimated under repeated loadingor traffic loading from this kind of criterion.

REFERENCES
Akiyama, T., N. Mikami, H. Yokota and H. Hamada (1999),”AE Characteristics of Damaged
Reinforced Concrete Beams due to Marine Environment,” Proc. JCI, Vol. 21, No. 2, 1231-1236 (inJapanese).
Matsuyama, K., T. Fujiwara, A. Ishibashi and S. Fukuchi (1993),”AE Measurement of Reinforced
Concrete Retaining Wall,” Proc. 9th AE Conf., JSNDI, 131-136 (in Japanese).
Murakami, Y., H. Yamashita, T. Kita and H. Yoshikawa (1991),”Deformation and AE Characteristics ofConcrete in freezing - Thawing Process,” Proc. 4th AE Conf. in Subsurface and Civil Engineering,
JSNDI, 47-51 (in Japanese).
Ohtsu, M. (1987),”Acoustic Emission Characteristics in Concrete and Diagnostic Applications,” Journalof AE, 6(2), 99-108.
Shiotani, T., S. Yuyama, Z. Li and T. Okamoto (1999),” Fracture Process of Concrete Members by
Improved b-Values,” Proc. 6th AE Conf. in Subsurface and Civil Engineering, JSNDI, 123-128 (inJapanese).
Yuyama, S., T. Okamoto, T. Kamada, M. Ohtsu and T. Kishi (1996),”A Proposed Standard for
Evaluating Structural Integrity of Reinforced Concrete Beams by AE,” Progress in AE VIII, JSNDI,
295-304.

S12

EWGAE 2000
24th European Conference on Acoustic Emission Testing,published by CETIM, Senlis, France
SOMMAIRE
Studies of the non-linear dynamics of acoustic emission generated in rocks by Z. Majewska,
Z. Mortimer (Institute of Geophysics, University of Mining and Metallurgy- Poland)1

Acoustic emission as result of tensil and shearing processes in stable and unstablefracturing of rocks by J. Pininska (Warsaw University - Poland)

9

Relation between acoustic emission signals sequences induced by thermal loading and thestructure of sedimentary rocks by B. Zogala, R. Dubiel (University of Silesia - Poland)

15

Electromagnetic and Acoustic Emission from solids under ramp increasing pressure
by J. Sikula, B. Koktavy, V. Sedlakova (Technical University - Czech Republic)

21

Acoustic emission/Acousto-Ultrasonic data fusion for damage evaluation in concreteby A. Tsimogiannis, B. Georgali, A. Anastassopulos (Envirocoustics S.A. - Greece)

27

Concrete crossbeam diagnostic by acoustic emission method by Z. Weber, P. Svadbik,
M. Korenska (Technical University of Brno - Czech Republic)

33

Fluctuation of ultrasonic beam in inhomogeneous media by S. Ahmed, R. Roberts,
F. Margetan (Saint Louis University - USA)39

Acoustic emission in PZT ceramics generated by electric fileds of high amplitude and low
frequency by M. Aleksiejuk, V. Busko, F.R. Rejmund (Institute of Fundamental
Technological Research PAS - Poland)

45

Amplitude analysis in the assay of impacted composites damage tolerance
by F. Billi, C. Caneva, M. Valente (University of Rome « La Sapienza » - Italy)

51

A new acoustic parameter for the analysis of composite materials damages
by F. Lachaud (LGMT - France)

57

Waveform based analysis techniques for the reliable acoustic emission testing of composite
structures by M. Surgeon, C. Buelens, M. Wevers, P. De Meester (Metalogic and Katholieke
University Leuven - Belgium)

63

Optical fibres for in situ monitoring the damage development in composites and the relation
with acoustic emission measurements by M. Wevers, L. Rippert, S. Van Huffel(Katholieke University Leuven - Belgium).

69

Lamb-wave source location of impact on anisotropic plates by H. Yamada, Y. Mizutani,
H. Nishino, M. Takemoto, K. Ono (Aoyama Gakuin University –Japan and UCLA - USA)77

Characterisation of the damage and fracture mechanisms in Ti3SiC2 using acoustic emission
by P.T. Cole, P. Finkel, R. Miller (Physical Acoustics Corp. - USA)85

Crack microstructure and sources of acoustic emission in metals
by L. Rogers (Lloyd's Register of Shipping - United Kingdom)

91

Evaluation of martensitic transformation dynamics of Cu-Al-Ni shape memory alloy single
crystals by acoustic emission method by K. Yoshida, S. Kihara, K. Sakamaki(University of Tokushima - Japan)

107

The identification of basic fatigue parameters on electroresonance pulsator with help of acoustic
emission technology by P. Mazal, J. Petras (Brno University of Technology - Czech Republic) 113
The phase of contact damage and its description by help of acoustic emission
by J. Dvoracek, J. Pazdera, and L. Petras (Brno University of Technology - Czech Republic)

119

Acoustic emission monitoring of delayed hydride cracking in zirconium
by A. Barron, C. Rowland (Pancom - United Kingdom)

125

Examination of plate valve behaviour in a small reciprocating compressor using acoustic
emission by J.D. Gill, R.D. Douglas, Y.S. Neo, R.L. Reuben and J.A. Steel(Heriot-Watt University - United Kingdom)133

A new method of acoustic emission source location in pipes using cylindrical guided waves
by H. Nishino, F. Uchida, S. Takashina, M. Takemot, K. Ono (Aoyama Gakuin University- Japan and UCLA – USA)139

Some approximate measurements of acoustic emission energy associated with dislocation
motion in metal alloys by E.R. Brown, R.L. Reuben and J.A. Steel (Heriot-Watt University- United Kingdom)

145

Acoustic emission method for pressure vessel diagnostics at a refinery by V.L. Sokolov,
B. Kabanov, V.P. Gomera, A.A. Okhotnikov (Kirishinefteorgsintez - Russia)

153

Optimisation of acquisition parameters for acoustic emission measurements on small pressure
vessels by F. Rauscher (Vienna University of Technology - Austria)

161

Monitoring of weld's defects evolution submit to static and dynamic loading thanks
to the acoustic emission method by C. Hervé, R. Pensec, A. Laksimi, (CETIM - France)

167

Acoustic emission due to cyclic pressurisation of vessels with partially penetrated longitudinalseams by M. Bayray (Institute of Pressure Vessels and Plant Technology - Austria)

175

Inspection of LPG vessels with AE examination
by P. Tscheliesnig, G. Schauritsch (TÜV Austria - Austria)

183

Inspection of pressure vessels used in refrigeration and air conditioning systems
by A. Amber, F. Zhang, M. Cherfaoui, L. Legin (CETIM - France)

189

The new Russian standards in the field of acoustic emission
by V.I. Ivanov, I.E. Vlasov (Orgenergoneft - Russia)

195

The feasibility of using passive ultrasonic techniques for the analysis of active corrosion
processes by M. Surgeon, C. Buelens (Metalogic - Belgium)

201

Using acoustic emission to monitor metal dustingby F. Ferrer, E. Andres, J. Goudiakas, C. Brun (Elf Atochem - France)

209

Use of acoustic emission to detect localised corrosion philosophy of industrial use,
illustrated with real examples by A. Proust, J.C. Lenain (Euro Physical Acoustics - France)

215

Inspection of flat bottomed storage tanks by acoustical methods. Classificationof corrosion related signals by M. Gori, P. Tscheliesnig, G. Lackner, H. Vallen and
B. Herrmann (CESI – Italy, TUV – Austria, Vallen Systeme -Germany)

221

Screening of tank bottom corrosion with a single point AE detector : AE-simple
by P. J. Van De Loo, D.A. Kronemeijer (Shell Global Solutions - The Netherlands)

227

Case histories from ten years of testing storage tank floors using acoustic emission by P.T. Cole,
S.J. Schoorlemmer, S.N. Gautrey (Physical Acoustics Limited - United Kingdom)233
Approach to the acoustic-emission diagnostics of main oil & gas pipeline systems
by V.M. Belov, M.N. Podlevskikh (Eltest Company - Russia)

241

Acoustic emission detection of damage in reinforced concrete conduit by H.W. Shen, S. Iyer,
M.A. Friesel, F. Mostert, R.D. Finlayson, R.K. Miller, M.F. Carlos and S. Vahaviolos(Physical Acoustics Corp. - USA)

247

Comparison of artificial acoustic emission sources as calibration sources for tool wear
monitoring in single-point machining by A. Prateepasen, Y.H.J. Au, B.E. Jones (Brunel
University - United Kingdom)

253

Development of an equipment to monitoring and control the quality of resistance welding
by J. Catty and CRAFT Partners (CETIM - France)

261

Comparison of acoustic emission with sound waves generated in arc welding processby I. Polajanr, L. Grad (Faculty of mechanical engineering - Slovenia)

267

Monitoring aging structures with acoustic emission and acousto-ultrasonics
by R.D. Finlayson, P.T. Cole, J.C. Lenain (Physical Acoustics Corp. - USA)273

A study of small HSDI Diesel engine fuel injection equipment faults using acoustic emission
by J.D. Gill, R.L. Reuben, J.A. Steel (Heriot-Watt University - United Kingdom)281

Acoustic emission development at Rolls Royce Plc U.K.
by S. Church (Rolls-Royce PLC - United Kingdom)287

Unsupervised pattern recognition of acoustic emission from full scale testing of a wind turbine
blade by D. Kourousis, A. Anastassopoulos, P. Vionis (Envirocoustics S.A. - Greece)

291

Acoustic emission proof testing of insulated aerial man lift devices
by A. Anastassopoulos, A. Tsimogianis, D. Kourousis (Envirocoustics S.A. - Greece)

299

BOXMAP - Non-invasive detection of cracks in steel box girders
by J.R. Watson, P.T. Cole, K.M. Holford (Physical Acoustics Ltd - United Kingdom)305

Monitoring failure mechanisms in CFRP orthopaedic implants during fatigue testingby A. Taylor, C. Rowland, P. Gregson (Pancom - United Kingdom)

313

Continuous monitoring of rock failure by a remote AE system by T. Shiotani, S. Yuyama,
M. Carlos (Research Institute of Technology, Tobishima Corp. - Japan)

321

Reciprocity calibration of acoustic emission transducers
by H. Hatano (Science University of Tokyo - Japan)

327

Influence of mounting between acoustic emission sensor and tested surface by M. Korenska,
L. Pazdera, L. Ritickova (Technical University of Brno - Czech Republic)333

New AE signal conditionner for industrial use
by H. Vallen, J. Von Stebut, J. Forker (Vallen Systeme GmbH - Germany)339

New software tools for the AE-practitioner
by H. Vallen, J. Vallen (Vallen Systeme GmbH - Germany)

347

Improved source location methods for pressure vessels
by S. Vahaviolos, V. Godinez, R.D. Finlayson (Physical Acoustics Corp. - USA)355

Neural Network localization of noisy AE events in dispersive media by M. Blahacek,
Z. Prevorovsky, J. Krofta (Institute of Thermomechanics AS CR - Czech Republic)363

Waveform analysis of simulated transient signals
by S. Vahaviolos, H.W. Shen, R.D. Finlayson (Physical Acoustics Corp. - USA)369

Waveform analysis of acoustic emission on thin composites structures
by M. El Moudni, S. Benmedakhene, A. Laksimi (UTC - France)

375

Development of a moment tensor analysis system for quantitative waveform analysis of AE
by S. Yuyama, M. Ohtsu, M.F. Carlos (Nippon Physical Acoustics Ltd - Japan)

381

The diagnostic of cutting tool wear
by V. Poppeova (University of Zilina - Slovakia)

387

Dynamics and damage assessment in impacted cross-ply CFRP plate utilizing the wavaform
simulation of Lamb wave acoustic emission by Y. Mizutami, H. Nishino, M. Takemoto,
K. Ono (Aoyama Gakuin University –Japan and UCLA - USA)395

Acoustic emission detection during stress corrosion cracking at elevated pressure and temperature
by Van Nieuwenhove, R.W. Bosch (Centre d’Etude de l’Energie nucléaire - Belgium)
403
Detection of pitting corrosion of aluminium alloys by acoustic emission technique by H. Idrissi,
J. Derenne, H. Mazille (Laboratoire de Physico-chimie Industrielle - France)
409
Reliability of acoustic emission technique to assess corrosion of reinforced concreteby H. Idrissi, A. Limam (Laboratoire de Physico-chimie Industrielle - France)

417

The evaluation of the influence of metrological and constructural parameters on the acoustic emission
generated by partial discharges by T. Boczar (Technical University of Opole - Poland)425
The analysis of the possible use of the descriptors determined in the time and frequency domains
for the characteristics of acoustic emission signals from partial discharges
by T. Boczar (Technical University of Opole - Poland)
433
Determining the influence of mechano-electrical coupling of measuring transducers usedin acoustic emission from partial discharges on the values of the registered descriptors
by L. Marcin (Technical University of Opole - Poland)

439

Applied of acoustic emission during high temperature oxidation under stress of nickel by L. Gaillet,
S. Benmedakhene, A. Laksimi, G. Moulin (Université de Techn. de Compiègne - France)
445

Proceedings volume, EWGAE 2000
The 24th EWGAE Meeting (EWGAE 2000) was held May 24-26, 2000 at CETIM, Senlis, France.
Program Chairs were C. Rigault and M. Cherfaoui. Over 90 participants from 15 countries attended themeeting. Proceedings “EWGAE 2000”, containing 66 papers, 460 pages, can be purchased from
CETIM. Contact: christel.rigault@cetim.fr

AVANT-PROPOSThis is the 24th European Conference on Acoustic Emission Testing organised by the EWGAE(European Working Group on Acoustic Emission) at CETIM-Senlis from the 24th to 26th May 2000. In
addition of the European contribution, the American and Asian contingents taking part in thisconference is important. This gives the conference an international radiance.
The Proceedings are the result of a two and a half day programme, significantly over-subscribed. As thecontributions are interesting, the EWGAE has tried to include as many papers as possible into
programme.
The technical programme has been organised along traditional lines:
Day 1: is devoted essentially to fundamental aspects: calibration of transducers with dislocation,
damage evaluation in concrete, rocks, ceramics and composite materials, cracks in metals, newmulti-technique methods, pattern recognition, neural network, etc.
Day 2: is predominently devoted to applications in traditional and new fields in industry:• Pressure vessels, LPG vessels, storage tanks• Pipes, pipeline• Composites, ceramics, concrete• Corrosion• Aging structures• Welding, cutting tool wear, etc.
Day 3: gives us an overview of the newest developments in the field of the AE instrumentation.
Exhibitions and posters is held through the 3 day conference.
The EWGAE committee has tried to meet the needs of both:• Industrialists worried about minimising cost and increasing availability while maintaining safeand efficient operations. Application sessions appeal to process, production, maintenance,
quality-control engineers concerned with asset management.
• Scientists and technologists concerned with the fundamental mechanisms, fracture and crack
propagation in materials and its measurement by acoustic emission.
The Proceedings provide an up-to-date reference on recent developments in AE technology throughout
Europe and the wide world. Beside the technical programme, we prepare a social highlight where you
have an opportunity to meet the colleagues within the AE community and to encourage exchanges andcooperation between laboratories and industries.
We hope this conference gives you strong image and encourage you to attend the first EWGAE meetingof the 3rd Millenium in the year 2002.

Mohammed CHERFAOUI, Christel RIGAULT
Conference Organisers

Présentation
Founded in 1972 by Dr Adrian Pollock, this year marks the 28th Anniversary of EWGAE (TheEuropean Working Group on Acoustic Emission). It has remained an important forum for exchange ofscientific and technical information and for the promotion of quality AE testing practice. The Group is acharitable society and survives through the voluntary efforts of its committee and the support of its
membership by attendance at its now biennal conference.
The Code Sub-Group of EWGAE published 5 codes of practice during the late 1970s and early 1980s.
The work of this group has now been taken up by a new working group on AE within the Commission
for European Norms (CEN), the European Standardisation Commission. The technical committee TC
138 is concerned with NDT in general and working group WG-7 within the committee is devoted to AE.
The working group is comprised of national representatives from the European Union and from other
European countries, which have been nominated by their respective National Standards Institutions. The
convenor of WG-7 is Emilio Fontana of CISE and the group's first meeting was held in Milan in
September 1991. Norms has been made in the standardisation of:• AE technology• General principles of AE testing and monitoring• Equipment characterisation
AE testing in Europe is now well established in several important areas of proof testing, in-service
monitoring and leak detection. Development of the technology continues to be driven by the needs ofprimary industry to reduce inspection and maintenance costs while preserving its assets and personnel
safety.
The need to exchange ideas and experience is stronger than ever, as is the requirement for the
standardisation of measurement and test procedures. EWGAE will continue as a focal point for theformer with CEN now fulfilling the latter.

Notes for Contributors
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
6531 Boelter Hall, MSE Dept.
University of California
Los Angeles, California 90095-1595 USA
FAX (1) 818-990-1686
e-mail: ono@ucla.edu
Authors of any AE related publications are encouraged tosend a copy for inclusion in the AE Literature section to:
Mr. T.F. Drouillard, Associate Editor - JAE
11791 Spruce Canyon Circle
Golden, Colorado 80403 USA
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.
Compilation of annotated data files will be accepted forinclusion in CD-ROM distribution, so that others can sharein their analysis for research as well as training. ASCII or
widely accepted data formats will be required.
The language of the Journal is English. All papers should
be written concisely and clearly.
2. Page Charges
No page charge is levied. One copy of CD-ROM will be
supplied to the authors free of charge.
3. Manuscript for Review
Manuscripts for review need only to be typed legibly;
preferably, double-spaced on only one side of the page withwide margins. The title should be brief. An abstract of 100200 words is needed for Research and Applications articles.
Except for short communications, descriptive heading
should be used to divide the paper into its component parts.
Use the International System of Units (SI).
References to published literature should be quoted in the
text citing authors and the year of publication. These are to

be grouped together at the end of the paper in alphabeticaland chronological order. Journal references should bearranged as below. Titles for journal or book articles arehelpful for readers, but may be omitted.
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.
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.
Illustrations and tables should be planned to fit a single
page width (178 mm or 7"). For the reviewing processes,
these need not be of high quality, but submit glossy prints
or equivalent electronic files with the final manuscript.
Lines and letters should be legible.
4. Review
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
Board member may seek another independent review. In
case of disputes, the author may request other reviewers.
5. Electronic MediaThis Journal will be primarily distributed electronically by
CD-ROM. In order to expedite processing and minimize
errors, the authors are requested to submit electronic files
or floppy disk copy of the paper. We can read Macintosh
and IBM PC formats. On the INTERNET, you can send an
MS Word file or the text portion to "ono@ucla.edu".
6. Color Illustration
We can process color illustration needed to enhance thetechnical content of an article. With the new format,
authors are encouraged to use them.

C. Hervé, 125
K.M. Holford, 232
H. Idrissi, 299, 307
V.I. Ivanov, 144
S. Iyer, 189
B.E. Jones, 196
B.S. Kabanov, 111
S. Kihara, 68
V. Kolovos, 217
M. Korenska, 29
D. Kouroussis, 217, 224
J. Krofta, 279
D.A. Kronemeijer, 174
G. Lackner, 167
A. Laksimi, 125
L. Legin 144
J. C. Lenain, 161
A. Limam, 307
Z. Majewska, 1
P. Mazal, 75
H. Mazille, 299
R.K. Miller, 61, 189, 272
Y. Mizutani, 51, 286
Z. Mortimer, 1
F. Mostert, 189
Y.S. Neo, 96
H. Nishino, 51, 102, 286
M. Ohtsu, S1, S7
A.A. Okhotnikov, 111
K. Ono, 51, 102, 286
L. Pazdera, 29
J. Pazdera, 81
R. Pensec, 125
J. Petras, 75
L. Petras, 81
J. Pininska, 8
A. Prateepasen, 196
Z. Prevorovsky, 279
A. Proust, 161
F. Rauscher, 118
R.L. Reuben, 96, 211

AUTHORS INDEX
A. Anastassopulos, 21, 217, 224
E. Andres, 155
J. Asquith, 211
Y.H.J. Au, 196
A. Barron, 87
M.W. Barsoum, 61
M. Bayray, 131
M. Blahacek, 279
R.W. Bosch, 293
C. Brun, 155
C. Buelens, 34
M.F. Carlos, 189, 248, 272
J. Catty, 205
M. Cherfaoui, 144
P.T. Cole, 61, 180, 232
A.W. Davies, 232
P. De Meester, 34
J. Derenne, 299
R.D. Douglas, 96
R. Dubiel, 15
J. Dvoracek, 81
T. El-Raghy, 61
F. Ferrer, 155
P. Finkel, 61
R.D. Finlayson, 61, 189, 272
J. Forker, 258
M.A. Friesel, 61, 189
S.N. Gautrey, 180
B. Georgali, 21
J.D. Gill, 96, 211
V. Godinez, 272
V.P. Gomera, 111
M. Gori, 167
J. Goudiakas, 155
P. Gregson, 239
S. Gross, 239
B. Herrmann, 167
I-1

L. Rippert, 41
C. Rowland, 87, 239
K. Sakamaki, 68
M.W. Scaife, 211
G. Schauritsch, 138
S.J. Schoorlemmer, 180
H.W. Shen, 189
T. Shiotani, 248
A. Skraber, 144
V.L. Sokolov, 111
J.A. Steel, 96, 211
M. Surgeon, 34
P. Svadbik, 29
S. Takashina, 102
M. Takemoto, 51, 102, 286
A. Taylor, 239
P. Tscheliesnig, 138, 167
A. Tsimogiannis, 21, 224
F. Uchida, 102
S. Vahaviolos, 189, 248, 272
H. Vallen, 167, 258, 265
J. Vallen, 265
P.J. Van De Loo, 174
S. Van Huffel, 41
R. Van Nieuwenhove, 293
P. Vionis, 217
L.E. Vlasov, 150
J. von Stebut, 258
J.R. Watson, 232
Z. Weber, 29
M. Wevers, 34, 41
H. Yamada, 51
K. Yoshida, 68
S. Yuyama, 248
F. Zhang, 144
B. Zogala, 15

I-2

Journal of Acoustic Emission
Contents of Volume 18, 2000
Selected papers from “EWGAE 2000, 24th European Conference on
Acoustic Emission Testing”, published by CETIM, Senlis, FranceStudies of the non-linear dynamics of acoustic emission generated in rocks by Z.
Majewska and Z. Mortimer (Institute of Geophysics, University of Mining andMetallurgy - Poland)1

Acoustic emission as result of tensile and shearing processes in stable and unstablefracturing of rocks by J. Pininska (Warsaw University - Poland)

8

Relation between acoustic emission signals sequences induced by thermal loading andthe structure of sedimentary rocks by B. Zogala, and R. Dubiel (University ofSilesia - Poland)

15

Acoustic emission/Acousto-Ultrasonic data fusion for damage evaluation in concreteby A. Tsimogiannis, B. Georgali, and A. Anastassopulos (Envirocoustics S.A. Greece)

21

Concrete Crossbeam Diagnostic by acoustic emission method by Z. Weber, P.
Svadbik, M. Korenska and L. Pazdera (Technical University of Brno - Czech
Republic)

29

Waveform based analysis techniques for the reliable acoustic emission testing ofcomposite structures by M. Surgeon, C. Buelens, M. Wevers, and P. De Meester(Metalogic and Katholieke University Leuven - Belgium)

34

Optical fibres for in situ monitoring the damage development in composites and therelation with acoustic emission measurements by M. Wevers, L. Rippert, and S.
Van Huffel (Katholieke University Leuven - Belgium).

41

Lamb-wave source location of impact on anisotropic plates by H. Yamada, Y
Mizutani, H Nishino, M. Takemoto and K. Ono (Aoyama Gakuen University - Japanand University of Calif., Los Angeles [UCLA] - USA)

51

Characterisation of the damage and fracture mechanisms in Ti3 SiC2 using acoustic
emission by P. Finkel, R.K. Miller, M.A. Friesel, R.D. Finlayson, P.T. Cole, M.W.
Barsoum and T. El-Raghy (Physical Acoustics Corp. and Drexel University - USA)61

Evaluation of martensitic transformation dynamics of Cu-Al-Ni shape memory alloy
single crystals by acoustic emission method by K. Yoshida, S. Kihara, and K.
Sakamaki (University of Tokushima - Japan)

68

The identification of basic fatigue parameters on electroresonance pulsator with help of
acoustic emission technology by P. Mazal, and J. Petras (Brno University ofTechnology - Czech Republic)

75

The phase of contact damage and its description by help of acoustic emission
by J. Dvoracek, J. Pazdera, and L. Petras (Brno University of Technology - Czech
Republic)81

Acoustic emission monitoring of delayed hydride cracking in Zirconium
by A. Barron and C. Rowland (Bodycote and Pancom - United Kingdom)

87

Examination of plate valve behaviour in a small reciprocating compressor using
acoustic emission
by J.D. Gill, R.D. Douglas, Y.S. Neo, R.L. Reuben and J.A.
Steel (Heriot-Watt University - United Kingdom)96

A new method of acoustic emission source location in Pipes using cylindrical guided
waves by H. Nishino, F. Uchida, S. Takashina , M. Takemoto and K. Ono(Aoyama Gakuen University - Japan and University of Calif., LA - USA)102

Acoustic emission method for pressure vessel diagnostics at a refinery by B.S.
Kabanov, V.P. Gomera, V.L. Sokolov, and A.A. Okhotnikov (Kirishinefteorgsintez Russia)

111

Optimisation of acquisition parameters for acoustic emission measurements on smallpressure vessels by F. Rauscher (Vienna University of Technology - Austria)

118

Monitoring of weld's defects evolution submit to static and dynamic loading thanks tothe acoustic emission method by C. Hervé, R. Pensec, and A. Laksimi, (CETIM France)

125

Acoustic emission due to cyclic pressurisation of vessels with partially penetrated
longitudinal seams by M. Bayray (Institute of of pressure vessels and plantTechnology - Austria)

131

Inspection of LPG vessels with AE examination by P. Tscheliesnig, and G.
Schauritsch (TÜV Austria - Austria)

138

Inspection of pressure vessels used in refrigeration and air conditioning systems
by A. Skraber, F. Zhang, M. Cherfaoui, and L. Legin (CETIM - France)

144

The new Russian standards in the field of acoustic emission
by V.I. Ivanov, and L.E. Vlasov (Orgenergoneft - Russia)

150

Using acoustic emission to monitor metal dustingby F. Ferrer, E. Andres, J. Goudiakas, and C. Brun (Elf Atochem - France)

155

Use of acoustic emission to detect localised corrosion philosophy of industrial use,
illustrated with real examples by A. Proust, and J. C. Lenain (Euro PhysicalAcoustics - France)

161

Inspection of flat bottomed storage tanks by acoustical methods. Classification of
corrosion related signals by P. Tscheliesnig, G. Lackner, M. Gori, H. Vallen and B.
Herrmann (TÜV - Germany, CEST - Italy)

167

Screening of tank bottom corrosion with a single point AE detector: AE-Simple byP.J. Van De Loo, and D.A. Kronemeijer (Shell Global Solutions - The Netherlands)

174

Case histories from ten years of testing storage tank floors using acoustic emission
by S.N. Gautrey, P.T. Cole, and S.J. Schoorlemmer, (Physical Acoustics Limited-United Kingdom)180

Acoustic emission detection of damage in reinforced concrete conduit by H.W. Shen,
S. Iyer, M.A. Friesel, F. Mostert, R.D. Finlayson, R.K. Miller, M.F. Carlos and S.
Vahaviolos, (Physical Acoustics Corp. - USA)

189

Comparison of artificial acoustic emission sources as calibration sources for tool wear
monitoring in single-point machining by A. Prateepasen, Y.H.J. Au and B.E. Jones(Brunel University - United Kingdom)

196

Development of an equipment to monitoring and control the quality of resistancewelding (CRAFT Project) by J. Catty, (CETIM - France)

205

A study of small HSDI Diesel engine fuel injection equipment faults using acoustic
emission by J.D. Gill, R.L. Reuben, J.A. Steel, M.W. Scaife and J. Asquith(Heriot-Watt University - United Kingdom)

211

Unsupervised pattern recognition of acoustic emission from full scale testing of a windturbine blade by D. Kouroussis, A. Anastassopoulos, P. Vionis and V. Kolovos(Envirocoustics S.A. - Greece)

217

Acoustic emission proof testing of insulated aerial man lift devices by A.
Anastassopoulos, A. Tsimogianis and D. Kourousis (Envirocoustics S.A. - Greece)

224

BOXMAP - Non-invasive detection of cracks in steel box girders by J.R. Watson,
K.M. Holford, A.W. Davies and P.T. Cole, (Cardiff University, Physical Acoustics
Ltd - United Kingdom)

232

Monitoring failure mechanisms in CFRP orthopaedic implants during fatigue testingby A. Taylor, S. Gross, C. Rowland and P. Gregson (Finsbury, University ofSouthampton, Pancom - United Kingdom)239

Continuous monitoring of rock failure by a remote AE system by T. Shiotani, S.
Yuyama, M. Carlos and S. Vahaviolos (Tobishima Corp. – Japan, PAC - USA)

248

New AE signal conditioner for industrial use by H. Vallen, J. Forker and J. vonStebut, (Vallen Systeme GmbH – Germany and Ecole des Mines - France)258

New software tools for the AE-practitioner
Systeme GmbH - Germany)265

by H. Vallen and J. Vallen, (Vallen

Improved source location methods for pressure vessels by V. Godinez, S.
Vahaviolos, R.D. Finlayson, R.K. Miller, and M.F. Carlos (Physical Acoustics Corp.
- USA)

272

Neural network localization of noisy AE events in dispersive media by M. Blahacek,
Z. Prevorovsky, and J. Krofta (Institute of Thermomechanics - Czech Repubric)

279

Dynamics and damage assessment in impacted cross-ply CFRP plate utilizing thewavaform simulation of Lamb wave acoustic emission by Y. Mizutani, H. Nishino,
M. Takemoto and K. Ono (Aoyama Gakuin University – Japan and UCLA - USA)286

Acoustic emission detection during stress corrosion cracking at elevated pressure andtemperature by R. Van Nieuwenhove, and R.W. Bosch (Centre d’Etude de l’Energie
Nucliaire - Belgium)

293

Detection of pitting corrosion of aluminiurn alloys by acoustic emission technique
H. Idrissi, J. Derenne, and H. Mazille (INSA de Lyon - France)

299

by

Reliability of acoustic emission technique to assess corrosion of reinforced concreteby H. Idrissi, and A. Limam (INSA de Lyon - France)

307

AEWG43 Presentation
Theoretical Treatment of AE in Massive Solid….by M. Ohtsu

S1

Diagnosis of Concrete Structures by AE….by M. Ohtsu

S7

EWGAE 2000 (EWGAE.pdf)
SOMMAIRE -- Content of EWGAE 2000 Proceedings
AVANT-PROPOS
Mohammed CHERFAOUI, Christel RIGAULT
Présentation - EWGAE

i-v
vi
vii

Authors Index (18Auindx.pdf)
Contents of Volume 18, 2000 (18Conts.pdf)

I-1 – I-2
I-3 – I-6

e-mail Addresses of Authors (e-mail.pdf)
EWGAE 2000 Participant List (Senlis.pdf)

Notes for Contributors
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
6531 Boelter Hall, MSE Dept.
University of California
Los Angeles, California 90095-1595 USA
FAX (1) 818-990-1686
e-mail: ono@ucla.edu
Authors of any AE related publications are encouraged tosend a copy for inclusion in the AE Literature section to:
Mr. T.F. Drouillard, Associate Editor - JAE
11791 Spruce Canyon Circle
Golden, Colorado 80403 USA
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.
Compilation of annotated data files will be accepted forinclusion in CD-ROM distribution, so that others can sharein their analysis for research as well as training. ASCII or
widely accepted data formats will be required.
The language of the Journal is English. All papers should
be written concisely and clearly.
2. Page Charges
No page charge is levied. One copy of CD-ROM will be
supplied to the authors free of charge.
3. Manuscript for Review
Manuscripts for review need only to be typed legibly;
preferably, double-spaced on only one side of the page withwide margins. The title should be brief. An abstract of 100200 words is needed for Research and Applications articles.
Except for short communications, descriptive heading
should be used to divide the paper into its component parts.
Use the International System of Units (SI).
References to published literature should be quoted in the
text citing authors and the year of publication. These are to

be grouped together at the end of the paper in alphabeticaland chronological order. Journal references should bearranged as below. Titles for journal or book articles arehelpful for readers, but may be omitted.
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.
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.
Illustrations and tables should be planned to fit a single
page width (178 mm or 7"). For the reviewing processes,
these need not be of high quality, but submit glossy prints
or equivalent electronic files with the final manuscript.
Lines and letters should be legible.
4. Review
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
Board member may seek another independent review. In
case of disputes, the author may request other reviewers.
5. Electronic MediaThis Journal will be primarily distributed electronically by
CD-ROM. In order to expedite processing and minimize
errors, the authors are requested to submit electronic files
or floppy disk copy of the paper. We can read Macintosh
and IBM PC formats. On the INTERNET, you can send an
MS Word file or the text portion to "ono@ucla.edu".
6. Color Illustration
We can process color illustration needed to enhance thetechnical content of an article. With the new format,
authors are encouraged to use them.