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JOURNAL OF
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EMISSION
Vol.36/January-December 2019

Editors: M.A. Hamstad (AEWG) and G. Manthei (EWGAE)

36-00136-001EX
36-00936-03936-05136-071

Receiving Sensitivities of Acoustic Emission Sensors: A data
Compilation
Kanji Ono
Data File for 36-001
Use of Acoustic Emission for Studying Ratcheting Behavior of 304LN StainlessSteel Elbows
C.K. Mukhopadhyay, T.K. Haneef, Suneel K. Gupta,
Vivek Bhasin, S. Vishnuvardhan, G. Raghava and P. Gandhi
Far-field wave propagation signals from pencil lead breaks on the open endof a thick-walled layered cylinder: Part 1 Amplitudes and frequency content
M. A. Hamstad
Far-field Wave Propagation Signals from Pencil Lead Breaks on the Open End
of a Thick-walled Layered Cylinder: Part 2 Modes of wave propagation
M. A. Hamstad
Detection of Defects in Spur Gears by Acoustic Emission Monitoring
Swati Gautam and Naresh Tandon

Proceedings of International Conference on Acoustic Emission - 9
Part I and II

Endorsed by
AEWG
and
EWGAE

Published byAcoustic Emission Group
Encino, CA USA©2019 Acoustic Emission Group

Monitoring Fracture Formation in Additively Manufactured
Anisotropic Rocks
Liyang Jiang1, Hongkyu Yoon2, Antonio Bobet3, Laura J. Pyrak-Nolte1,3
1
2

Department of Physics and Astronomy, Purdue University, West Lafayette, IN;
Geomechanics Department, Sandia National Laboratories, Albuquerque, NM;
3
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN

ABSTRACTThis work explores the effects of oriented layers and texture (oriented minerals) on fracture
evolution in 3D printed gypsum samples. During 3D printing, bassanite (calcium sulfate
hemihydrate) was chemically transformed into gypsum layer by layer, giving rise to anisotropic
texture. Unconfined compressive strength (UCS) tests monitored with six broadband acoustic
emission (AE) transducers were performed with cylindrical specimens: casted gypsum (CG)samples, and 3D printed (3DP) samples with five different orientations of bassanite layer andgypsum texture relative to the loading direction. UCS varied by 150% among the samples,
primarily due to anisotropy in the 3DP samples. The bonds of gypsum crystals between sequential
bassanite layers were stronger than those of gypsum-bassanite, resulting in anisotropic material
properties. Unlike the CG samples, no axial splitting was observed from the 3DP samples, and asingle inclined (relative to loading direction) major crack was observed instead. The fracture
location estimated by AE localization technique was comparable to that observed fromreconstructed X-ray computed tomography images. CG samples failed immediately after reachinga peak load, while 3DP samples exhibited ductile post-peak behavior. Examination of the AE
signal amplitude from post-peak loading revealed that more ductile behavior was associated withmore AE events that occurred over a longer period of time, and the resultant fracture surfaces
were rougher than for narrow distributions. Monitoring of AE signals during failure of rock has thepotential to predict fracture roughness and the mineral and layering patterns.
Keywords: 3D printing, AE, X-ray computed tomography, fracture roughness, UCS

1. INTRODUCTION
Understanding the mechanical behavior of layered rock is vital in fields such as hydraulicfracturing for the exploitation of shale gas and oil reservoirs. The complex response of shale is,in part, due to its anisotropy, which is attributed to the preferred orientation of its mineral grains,pores, cracks, and bedding planes (e.g. Wenk et al., 2007). To determine the effect of anisotropic
layering on rock properties, deformation and failure requires samples that enable repeatable
results. This is challenging when working with natural rock because even rock samples acquired
from the same rock mass can vary in mineralogy, texture and structural features (Johnson &Greenkorn, 1963; Sone & Zoback, 2013). The effect of oriented layers and oriented textural

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features on the unconfined compressive strength (UCS) of an anisotropic rock is studied using
3D printed analog rock-material to overcome the challenges of working on natural samples.
2. SAMPLE FABRICATION
3D printing was used to create analog rock samples. 100 µm thick layers of calcium sulfate hemihydrate (bassanite) powder were bonded with a proprietary water-based binder, which chemicallytransformed the bassanite into gypsum (Singh & Middendorf, 2007). The gypsum texture
contained in a layer was controlled by selecting the direction of binder application and layering.
The gypsum crystals formed bonds between sequential bassanite layers that were stronger than
the gypsum-bassanite bonds, resulting in anisotropic material properties. UCS tests wereconducted on printed cylindrical specimens (50.8 mm in height, 25.4 mm in diameter) withdifferent orientations of bassanite layers and gypsum texture relative to the loading direction(Figure 1).
Non-layered reference samples with no preferred mineral orientation were fabricated by casting
gypsum in a mold. A silicone rubber mold was created from a solid resin sample (3D printed on
a FormLabs 2) with the same dimensions as the 3D printed (3DP) samples. The mold was filledwith mixed gypsum and water, then vibrated to minimize the amount of trapped air, and thencured. The resulting casted gypsum (CG) samples were cylindrical in shape and had the sameheights and diameters as the 3DP samples.
3. EXPERIMENTAL SETUP AND PROCEDURE
All samples were loaded parallel to the long axis of the cylinder with an ELE International Soil
Testing load frame with an S-shaped load cell (2000 lbs capacity). A steel ball and a steel spacer
were place on top of the samples to function as a spherical seat.
AE measurements were recorded with Mistras Express II station together with loading anddisplacement data synced by parametric channels. Broadband transducers (F15α sensors from
Physical Acoustics) were attached to the side of the cylinder samples at 6 different locations. 201200 kHz preamplifiers were used with a 60 dB gain and 27 dB threshold setting for recording.
During monitoring, acoustic sensor transmission tests were also performed to provide velocities
across the samples.
A 3D X-Ray Microscope (Zeiss Xradia 510 Versa) was used to image each sample after the test
if coalescence did not lead to complete failure. The induced fractures were visualized in 3D using
Dragonfly Pro software. If failure resulted in a sample breaking into two pieces, then laser
profilometry was performed to determine the fracture surface roughness.
An aluminum piece was also tested to determine the background noise levels. Data wereanalyzed by Noesis software only for signals with peak frequencies between 200-400 kHz since
lower frequency signals were identified as noise.

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4. RESULTS
The largest values of UCS were observed in samples where both the layering and gypsum texture
were oriented parallel to the loading direction. The lowest UCS was found for samples withbedding layers oriented 45° to the applied load (Figure 1). Unlike the CG samples, no axial
splitting was observed among the 3DP samples. Samples with bedding oriented 45° to the load
failed along the bedding planes, breaking into two pieces with smooth failure surfaces as
determined from laser profilometry. Irrespective of the orientation of the layers and texture, allsamples exhibited a single major crack that spanned the entire length of the sample, inclined tothe direction of loading.

Figure 1. Sketch of 3D printed samples showing orientation of the bassanite layers (blue lines) and oriented
mineral feature (red dashed lines) and the load displacement curves of each type.

Figure 2. Estimated fracture location from AE and the reconstructed X-ray computed tomography images.
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The fracture location estimated by AE localization technique was compared to that observed fromreconstructed X-ray computed tomography images (Figure 2). Improved location methods areneeded to include the velocity anisotropy of the sample.
CG samples failed immediately after reaching a peak load with narrow period of AE signals. 3DP
samples exhibited ductile post-peak behavior. Examination of the AE signal amplitude distribution
in time for post-peak loading revealed that a longer period of AE events correlated with moreductile behaviors and the resultant fracture surfaces were rougher than for narrow AE distributions(Figure 3).

Figure 3. (a) The CG sample failed almost instantly and had the narrowest AE amplitude distribution intime; (b) The H45 sample had smoother fracture surfaces and a relatively narrower AE amplitude
distribution compared to other 3DP samples; (c) The H sample exhibited the most significant ductile postpeak behavior, produced relatively rougher surfaces and a longer period of AE events.

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The results have the potential to be used as an effective tool to study the influence of texture andlayering on sample strength and cracking patterns and monitoring AE signals during failure ofrock may have the potential to predict fracture roughness and the mineral and layering patterns.
5. AKNOWLEDGMENT
Sandia National Laboratories is a multi-mission laboratory managed and operated by National
Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell
International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration
under contract DE-NA0003525.

6. REFERENCE LINKING
Johnson, C.R. and R.A. Greenkorn, Description of gross reservoir heterogeneity by correlation oflithologic and fluid properties from core samples, Hydrological Sciences Journal, 8:3, 52-63,
1963.
Sing, N.B. and B. Middendorf, Calcium sulphate hemihydrate hydration leading to gypsum
crystallization, Progress in Crystal Growth and Characterization of Materials, v. 53, p57-77,
2007.
Sone, H. and M.D. Zoback, Mechanical propertiesof shale-gas reservoir rocks – Part 1: Static anddynamic elastic properties and anisotropy, Geophysics, vol. 78, No. 5, September – October
2013, PD381-D392, 2013.
Wenk, H-R., Lonardelli, I,m Franz, H., Nihei, K. and S. Nakagawa, Preferred orientation andelastic anisotropy of illite-rich shale, Geophysics, vol. 72, no. 2, March-April 2007, p E69-E75,
10.1190/1.2432263.

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Tensile test of 3D printed specimens for demonstrating theperformance of acoustic emission measurement systems
Hironobu Yuki and Tomona Murata
The University of Electro-Communications, Chofu, Japan
ABSTRACT
Quantitative discussion on the performance of measurement systems including both of the characteristics of the instruments and the operational skill based on the reference is necessary forestablishing the traceability of measurement. In this study, an approach to provide the standard
specimen as the reference for acoustic emission (AE) measurement, in which behavior of AE occurrence could be controlled, was examined. Specimens containing regions of different stressconcentration resulted from different size holes were made by a stereolithography type 3D printerand those were subjected to tensile load. It was shown that the AE event in the intended position
could occur for the specimen with different size rhombic holes to change the degree of stress concentration. It was also found that drop-like holes for changing the degree of stress concentration
rapidly are useful to generate AE in the intended position at the intermittently time. As a consequence, the feasibility of providing the standard specimen for the AE method was demonstrated.
Keywords: Standard Specimen, Stereolithography, Hole, Stress Concentration, Crack Extension

1. INTRODUCTION
Artificial acoustic emission (AE) sources such as pencil lead breaks and the pulse generator are
widely used to check the condition of the instruments and perform calibration [1] so that the frequency characteristics and the sensitivity of the system are ensured for successful monitoring.
Those sources, however, cannot sufficiently simulate the dynamic characteristics such as the interval of generation and movement of the source position which are often employed as parameters to
discuss the phenomenon causing AE. Hence, inappropriate settings for the instruments which leadto miss-recording of AE signals, e.g. high threshold level, low sampling rate and long dead time,
are sometimes allowed in operation. Therefore, quantitative discussion on the performance ofmeasurement systems including both of the characteristics of the instruments and the operational
skill based on real AE signals which are regarded as the reference is desirable in order to avoid
such situation and establish the traceability. Development of the standard specimen occurring
intended real AE events is expected for this reason. In the case of AE measurement associatedwith fracture, such specimen is required to generate AE intermittently same as AE characteristics
due to the generation and extension of the crack. AE measurement during the tensile test for the
3D printed specimen with intended defects was attempted since 3D printers could easily make theobject containing defects of arbitrary geometry in the arbitrary position, and the AE signal associated with final fracture was only measured[2]. In this study, AE measurement for the specimens
containing different holes made by a 3D printer was conducted by subjecting tensile load and thefeasibility of providing the standard specimen for AE measurement was examined.

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2. EXPERIMENTAL PROCEDURE
Strip specimens with the single V-shaped notch containing regions of different stress concentration resulted from different size holes, which were aligned on the extensional line of the notch tip,were made by a stereolithography type 3D printer (3D Systems, ProJet 3500 HD Max; resolution:
375×375×790 dpi, lamination pitch: 29 µm) using acrylic ultraviolet curing resin (3D Systems, VisiJet Crystal). AE signals were measured while the specimens were subjected to tensile load toward
the longitudinal direction with a testing machine (Instron, 4505). Four 140 kHz resonant type AE
sensors (Fuji Ceramics, AE144A) were attached to the specimen in surrounding the whole holes
for source location and detected signals were recorded on a digital oscilloscope after amplificationof 40 dB with the external preamplifier (NF Corporation, 9913). A wideband type AE sensor (NF
Corporation, AE-900S-WB) was also attached to the opposite side of the specimen for acquiring
all AE events during the test with a general AE measuring device (Physical Acoustics Corporation,
USB AE Node; built-in preamplifier of 40 dB). In this study, the specimens were laminated toward
the thickness direction in order to prevent the occurrence of delamination due to tensile loading.
Therefore, the generation of AE caused by delamination can be negligible.

3. RESULTS AND DISCUSSION
3.1 Rhombic holes specimen
The specimen with rhombic holes of 3 mm in each side length and the opening angle along the longaxis becomes larger as the distance from the notch increases for changing the degree of stressconcentration as shown in Figure 1 was first used for the experiment. Figure 2(a) demonstrates
the load-displacement diagram during the test obtained by the testing machine. It was shownthat the specimen was broken down at the time immediately after recording the AE signal on theoscilloscope and no loss in load was observed until the final fracture. The difference of the stressconcentration factor around each hole seems small to take place the intermittently crack extension.
The time of AE occurrence is also indicated in this figure. While the oscilloscope only recorded
a single AE event, the AE measuring device acquired several events having a different amplitude
before the final fracture because of the low background noise level. However, these events are notrelated to the growth of the main crack since the load-displacement curve continues to increase
monotonically. Further discussion should be done by obtaining the data which are capable ofsource location for these events. Figure 2(b) shows the results of source location for the data
recorded on the oscilloscope for three same geometry specimens. It was demonstrated that AE
occurred in the region between the notch and the first hole where the maximum stress appears.

Figure 1: Geometry of the specimen with rhombic holes.
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(a) Load-displacement diagram(b) Location of AE source

Figure 2: Results of the specimen with rhombic holes.
Therefore, it was found that although significant AE could be generated in the intended position bychanging the size of the hole, intermittently measurement corresponding to the crack extension isdifficult.
3.2 Drop-like holes specimen
Experiments using two kinds of specimens with drop-like holes, in which the stress concentration
factor around the sharp tip of the hole rapidly changes depending on the distance from the notch,
were next carried out. One, called “type A” as shown in Figure 3(a), has the geometry that the(a) Type A(b) Type B
Figure 3: Geometry of the specimen with drop-like holes.
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opening angle of the sharp tip increases as the distance from the notch increases, and the othercalled “type B” has the geometry that the angle decreases with increasing the distance as shownin Figure 3(b).
Figure 4(a) illustrates the time of AE occurrence during the test for the type A specimen by plotting
marks on the load-displacement diagram. It was shown that AE signals were generated whena loss in load occurred during subjecting tensile load before the breakdown of the specimen sothat it was found that starting and stopping of the crack was repeated. Figure 4(b) shows theresults of source location determined by the AE signals recorded on the oscilloscope. The order ofcrack generation confirmed by visual observation during the test was also indicated in this figure,and it was shown that the positions of AE sources were not aligned straightforward one direction.
On the other hand, the results of the type B specimen were shown in Figure 5. Several losses
in the load were observed and the positions of AE sources were aligned in the order of reasonableness. These results show that AE events occur in the intended position at the intermittently
time by changing the degree of stress concentration rapidly. Therefore, the performance of AE
measurement can be demonstrated whether the specified events are appropriately recorded.

(a) Load-displacement diagram(b) Location of AE source

Figure 4: Results of the specimen with drop-like holes (type A).

(b) Location of AE source(a) Load-displacement diagram

Figure 5: Results of the specimen with drop-like holes (type B).

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4. CONCLUSION
AE measurement of the specimens made by the stereolithography type 3D printer during thetensile test was conducted. It was shown that the AE event in the intended position could occur
for the specimen with different size rhombic holes to change the degree of stress concentration.
It was also found that drop-like holes for changing the degree of stress concentration rapidly areuseful to generate AE in the intended position at the intermittently time. From these results, thefeasibility of providing the standard specimen for the AE method by aligning the arbitrary size holes
in the arbitrary positions using the 3D printer was demonstrated.

REFERENCES[1] The Japanese Society for Non-Destructive Inspection (ed.), Practical Acoustic Emission Testing,Springer Japan, (2016).
[2] Obata, Y., “Prototype tensile specimen with defects by 3D printer for AE measurements”, Meeting Materials of the Research & Technical Committee on Acoustic Emission, the JapaneseSociety for Non-Destructive Inspection, AE-00033, (2014), pp.35–36, (in Japanese).

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Evaluation of Damage Depth Inside Concrete Using Surface
Wave TomographyaNorihiko Oguraa, Yuji Konishia and Tomoki Shiotanib
CORE Institute of Technology Corp. Osaka, Japan; bKyoto University
ABSTRACT

Surface wave tomography using an elastic wave is one of the nondestructive testing methods
used for evaluating internal soundness of concrete structures. This study experimentallyinvestigated the depth of a defect detectable by the surface wave tomography, generating
elastic waves with varied frequencies. The detectable damage depth was successfully
determined for each frequency level of the elastic wave based on the obtained results. It wasalso found that internal damage evaluation layered in the depth direction was possible even witha single elastic wave by applying the wavelet transform and sorting the data by target frequency.
Keywords: surface wave tomography, elastic wave, nondestructive testing, Rayleigh wave,
wavelet transform

1. INTRODUCTION
Surface wave tomography is an applied elastic wave method which is drawing increasing
attention as a means of nondestructive testing for soundness evaluation of concrete structures.
Since this technique does not require the sensors to be placed on the opposing sides of theobject, internal concrete soundness can be evaluated with the sensors arrayed on a single
plane. This makes the surface wave tomography useful, especially in cases where the subject
structure is accessible only from one side. However, the relationship between the frequency of agenerated elastic wave and the detectable depth of a defect has not been identified. In thecurrent study, elastic waves in different frequency bands were generated by using several typesof steel balls to determine the relationship with the detectable damage depth. It was also
investigated if it was possible to determine the depth of a defect with an elastic wave made by asingle type of steel ball, by applying wavelet transform to the received waveforms andexamining the tomographic images obtained for the surface wave propagation velocity at thetarget frequency.

2. SURFACE WAVE TOMOGRAPHY
2.1 Characteristics of the surface wave
The surface wave tomography is a type of elastic wave tomography and focuses on theRayleigh wave which is a component of the elastic wave that propagates along the surface. The
Rayleigh wave is a mixed wave consisting of the P-wave which moves in the direction of wave
propagation and the S-wave which moves in the direction perpendicular to wave propagation. It
is known that the vibration of particles propagates in an elliptical motion at a right angle to theplane along which the wave travels. Although the depth of an influence on the Rayleigh wave
corresponds to the wavelength, one half the wavelength is used as the influence depth in thisstudy to take into account the attenuation with depth. Figure 1 schematically shows the
propagation of the Rayleigh wave.
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2.2 Tomographic analysis
A single array measurement of the elastic wave propagation time only provides an average
propagation velocity on a scan line (from the impact point to the receiving point) for soundness
evaluation based on the calculated velocity. It is therefore impossible to know the location of thedefect when the result indicates damage in the object. With multiple arrays of sensors, incontrast, an estimated distribution of soundness can be obtained by applying matrix operations
to the propagation times measured on multiple scan lines in different directions, and determining
the elastic wave propagation velocity in each cell of an arbitrarily-divided analysis model of astructure by back analysis. For the surface wave tomography in this study, an analysis model ofthe subject area arbitrarily divided into cells was created, and slowness (or reciprocal velocity)
was given as the value for soundness evaluation. Correction of slowness was made by backanalysis so that the theoretical propagation time obtained from slowness in each cell and ray
path length on each scan line would be equal to the measured propagation time. The
simultaneous iterative reconstructive technique (SIRT)1) was used for the back analysis.

3. OUTLINE OF THE EXPERIMENT
3.1 Specimen
An unreinforced concrete specimen with an artificial crack simulating a real structure wasprepared. Figure 2 shows the external dimensions of the specimen. The artificial crack in thespecimen was made by embedding a cloth tape at a depth of 50 mm from the concrete surface.
3.2 Layout of the sensors
Nine (9) receiving sensors were placed in three lines and three rows (500×500 mm) across thecrack, and impact-side sensors were placed at 16 arbitrary points. Figure 3 shows the layout of
the sensors. In this study, 60 kHz resonant acoustic emission sensors were used.
3.3 Calculation of the surface wave propagation velocity
Surface wave propagation velocity is used for the parameter in the surface wave tomography.
Surface wave is known to be characterized by extremely large energy. In this study, the surface
wave arrival time was determined by the maximum or minimum value of the received wave. The
impact time was calculated by using Akaike's Information Criterion (AIC)2).
3.4 Frequency of the generated elastic wave and the influence range
Steel balls were used to apply an impact and generate an elastic wave in this study. The contact
time of a steel ball at the impact can be given by Equation (1) from a previous study3). Based on
the contact time, the centroid frequency and influence range of the surface wave were
calculated on the assumption that a generated surface wave propagation velocity was 2,400m/s and that one period of the surface wave was equal to two times the contact time of the steel
ball. The diameters of the steel balls used were 5 mm (D5), 10 mm (D10) and 20 mm (D20).
Table 1 shows the contact time of each steel ball and the frequency and influence range of thesurface wave.
TC=0.0043D (1)where, TC: contact time (s), and D: steel ball diameter (m)S62

4. EXPERIMENT RESULTS
4.1 Investigation on the impact frequency and the influence range of the surface wave
The influence range of the surface wave was investigated using the elastic wave generated by
an impact of a steel ball. Figures 4 to 6 show the results of tomographic analysis on the crack inthe concrete specimen, using the D5, D10 and D20 steel balls. The crack was expressed almost
correctly by the velocity distribution in the tomographic image with the D5 steel ball, but not in
those with the D10 and D20 steel balls. These results suggest the presence of an area subject
to such phenomena as diffraction and reflection in the surface wave influence range in the depth
direction. The influence range determined using the D5, D10 and D20 steel balls was 51.60,
103.2 and 206.4 mm, respectively, from the concrete surface as shown in Table 1, and it was
likely difficult to detect the defect when the ratio of the influence range to the crack depth (50mm) was about 200% or over.
4.2 Tomography using the wavelet transformIn the tomography performed in Section 4.1, the detectable layer (depth) was limited in relation
to the steel ball diameter. In order to detect the location of a defect or a defect at an unknown
depth, it is necessary to perform measurement using multiple steel balls with different diameters.
For simplification of the measurement, frequency analysis was made by applying the wavelet
transform to the received waveforms. The wavelet transform leaves the time domain information
which is otherwise lost during the process of Fourier transform to obtain the frequency response.
This enables calculating the arrival time of the maximum amplitude at a target frequency. Figure
4 shows an example of the wavelet transform results on a received waveform and the surface
wave arrival time reading. If the tomography using the propagation velocity calculated by thismethod is valid in damage evaluation, it will be possible to obtain internal damage evaluation
layered in the depth direction, using a single waveform.
The wavelet transform was performed using the received waveforms generated by an impact of
the D10 steel ball. The target frequencies were 23.2 kHz respectively corresponding to thetheoretical peak frequencies for the elastic waves generated by using the D5 steel ball. Figures
8 show the tomographic analysis results. The tomography at a target frequency of 23.2 kHz(corresponding to the D5 steel ball) almost correctly expressed the crack.
Consequently, the tomography using the wavelet transform is capable of providing layered
evaluation of a concrete structure.

5. SUMMARY
The findings of this study are summarized as follows:(1) The larger a defect accounting for the influence range of the surface wave, the larger its
influence on the surface wave propagation velocity is, leading to more accurate expressionof the velocity distribution by the surface wave tomography.
(2) With the use of the wavelet transform, the tomography using a single waveform can provide
layered evaluation of a concrete structure.

6. REFERENCES[1]

Gilbert, P.: Iterative methods for the three-dimensional reconstruction of an object fromprojections, J Theor Biol 36, pp.105-117, 1972.

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[2]
[3]

H. Akaike: Markovian representation of stochastic processes and its application to theanalysis of autoregressive moving average processes, Annals of the Institute of Statistical
Mathematics, Vol.26, No.1, pp.363-387, 1974.
Sansalone, M. and Streett, W. B.: Impact Echo, Bullbrier Press, Ithaca, N.Y., pp.30-34,
1997.

Figure 1 Schematic of the propagation of the Rayleigh wave

Figure 2 Specimen

Figure 3 Layout of the sensors and location of the defect

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Table 1 Steel ball diameters and surface wave influence ranges
Steel balldiameter D
(mm)
0.005
0.010
0.020

Contact
time TC
(s)
21.5
43.0
86.0

Frequency
(kHz)
23.3
11.6
5.81

Influence
range
(mm)
51.60
103.2
206.4
2000
1600
1200
800
400
0 (m/s)Figure 4 Tomography on the crack, with the D5 steel ball
2000
1600
1200
800
400
0 (m/s)Figure 5 Tomography on the crack, with the D10 steel ball

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2000
1600
1200
800
400
0 (m/s)Figure 6 Tomography on the crack, with the D20 steel ballTime read at the peak

50

Frequency (kHz)

40
30
20
10

1.1E+0.1
8.5E+00
6.3E+00
Target frequency
band

4.2E+00
2.1E+00
0.0E+0000

5000
Time (s)

10000

Figure 7 Arrival time reading by the wavelet transform
2000
1600
1200
800
400
0 (m/s)Figure 8 Tomography on the crack, with the D10 steel ball, at 23.2 kHz

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Internal Damage Detection of RC Bridge Decks with
Uniformly Distributed Elastic Wave Sources
Hidefumi Takamine1,2, Yuki Ueda1,2, Kazuo Watabe1,2, Katsufumi Hashimoto3,
and Tomoki Shiotani3
1Research & Development Center, Toshiba Corp., Kawasaki, 212-8582, Japan;

2NMEMS Technology Research Organization, Chiyoda-ku, Tokyo, 101-0026, Japan;

3Dept. Civil & Earth Resources Engineering, Kyoto University, Kyoto, 615-8540, Japan

ABSTRACT
In order to inspect the large number of bridges, easy and efficient methods of inspection areneeded. We have developed a new and efficient technique to inspect the RC bridge decks
with Acoustic Emission (AE) monitoring system. As a unique point of our method, we utilized
rain-induced elastic waves, which had been generally considered as noise. We installed AE
sensors on the undersurface of the bridge decks and monitored the rain-induced elastic waves
propagate through the decks. Usually, the rain-induced elastic wave sources are distributed
uniformly on the upper surface of the decks. However, the cracks inside the decks disturb the
propagation of the elastic waves and the distribution observed by the sensors on theundersurface is distorted. Therefore, analyzing the change in uniformity of the distribution of thesources can reveal the existence of the damage inside the bridge deck. Through a test at an inservice highway bridge, we have successfully verified the effectiveness of this advanced
technique. The recognized cracks in the test had been hard to recognize with conventionalpassive Non-Destructive Testing (NDT). Besides, our method is quite efficient that themeasurement is completed in a very short period of time.
In this paper, the results obtained from both test piece experiments and in-service highway
bridge monitoring are shown. Furthermore, the effect of difference in the types of sources to
induce the elastic waves and the sensor position to detect the elastic waves are discussed.
Keywords: acoustic emission, bridge deck, concrete, crack, inspection

1. INTRODUCTION
The maintenance of deteriorating social infrastructures is a serious issue [1]. In order tomaintain the large number of social infrastructures, the inspections should be executed
efficiently. As efficient inspection methods, we have been developing a monitoring system anddiagnostic methods utilizing acoustic emission (AE) sensors [2]. Our system inspects concrete
bridge decks by watching the AE activities, which are caused by the fracturing of materials [3].
In a previous work, we monitored highway bridge decks for about a week. From an analysis of
the AE activities, we successfully diagnosed the deterioration of the RC bridge decks [4-6].
On the other hand, we have developed another novel method to detect internal cracks of thebridge decks [7, 8]. We focused on the rain-induced signals. Those signals are not generated by
the fracturing at the cracks. Therefore, they should be removed as noise for accurate analysis.
However, we discovered that those rain-induced signals contain the information of propagation
path of the elastic waves. Figure 1 shows the schematic diagram of the propagation of rain9th International Conference on Acoustic Emission

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induced elastic waves in a bridge deck. The raindrops hit the surface of the road and generate
elastic waves. The elastic waves propagate through the deck and reach the AE sensors on theundersurface of the deck. Usually, it is assumed that raindrops fall uniformly over the entire roadsurface. Therefore, if the rain-induced elastic wave source locations are calculated from thesensor data, the distribution of the sources should be uniform. However, if a large crack exists inthe deck, the propagation is disturbed and the source density of the damaged area would belower. Consequently, from the calculated distribution of the rain-induced sources, we can detect
the cracks inside the deck. We applied this technique to a test piece with artificial defect insideand confirmed the ability to detect internal cracks. The results of an actual bridge deck are also
shown and compared with the test piece results.
Rain

AE sensor
Figure 1 Propagation of rain-induced elastic waves

2. EXPERIMENTAL SETUP
We carried out experiments on a test piece of RC slab. Figure 2 shows the test piece. The test
piece contains a styrene board simulating a horizontal crack. The styrene board is 600 x 600
mm and 5 mm thick. The board is placed alongside the upper rebar and it is about 140 mm
deep from the undersurface of the slab. Figure 3 shows the sensor arrangement. Nine sensors
are attached to the undersurface of the slab. The distance between sensors is 500 mm.
The frequency range of the sensors is 25 - 70 kHz. In order to simulate the rain, we applied
impacts on the upper surface of the test piece by hammer or water drops. The impacts
were applied uniformly around the styrene area. The hammering was continued for about 2
minutes. The water drops were scattered from 2 or 3 m high for about 2 minutes. Estimatedvolume of each water drop was less than 5 ml.

~3500 mm

~1500

200

Styrene board
600 x 600 mm~140

Figure 2 Test piece of RC slab

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t = 5 mm

AE sensor

500 mm500

Figure 3 Sensor arrangement of test piece

3. RESULTS AND DISCUSSION
3.1 Results of different source types
Figure 4 shows the results of the test piece experiments. The calculated source locations andthe source density distributions are shown. In both hammering results and water drops results,
less wave sources are located inside the square area in the center, which contain styrene board
simulating the internal crack. The results shows that the hammering detected the styrene board
clearer than the water scattering. Since the impacts of the hammer were stronger and more
controllable than the water drops, the elastic waves generated by the hammering were larger inamplitude, more separated each other in timing, and more uniformly distributed than the waves
generated by water scattering. The results indicate that well controlled and strong impacts
would make this method more accurate.

(a) HammerAEsensor[mm](b) Water drops

Styreneboard
600 x 600

Figure 4 Results of different wave sourcesS69

3.2 Results of different sensor arrangements
Figure 5 shows the location analysis results in different sensor arrangements. In botharrangements, the distance between a sensor and the nearest sensor is about 700 mm. In bothcase, the area with defect shows the lower density and the internal crack is recognizable.
However, the clarity of the results is quite different owing to the difference in the sensor
positions. A notable point is the presence of the sensor just under the defect. In the case (a),many of the elastic waves were disturbed to reach the sensor in the center by the defect. On theother hand in the case (b), less waves were disturbed to reach the sensors and their source
positions were able to be located by the calculation. It made the difference in the source density
between the defective area and the sound area smaller. Since the crack position is notapparent before the inspection, the influence of the sensor arrangement has to be taken into
account at the diagnostics.

(a)~700mmAE
sensorStyreneboard
600 x 600

[mm]

(b)

~700mmFigure 5 Results of different sensor arrangements

4. RESULT OF ACTUAL BRIDGE
We applied our method to an RC deck of an in-service highway bridge. Figure 6 shows thephotos of the measured bridge and the sensor arrangement. Sensors were attached in a 3 × 5
configuration on the undersurface of the deck. The frequency range of the sensors is 25 – 80
kHz. The distances between adjacent sensors were 700 or 750 mm. Although small cracks
were found on the undersurface of the deck, no serious large crack was visible. During themeasurement, we found a sudden increase in AE activities due to heavy rain lasting for about
700 seconds. Since the rain lasted for very short period of time, the traffic-induced signals
were negligible compared with the large amount of rain-induced signals. We extracted thosedata and analyzed in detail. After the measurement, the analysis results were verified by the
coring.
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Figure 7 shows the location analysis result of the rain-induced signals and the coring results.
The coring positions are marked with the source distribution. Some of the cores were found tobe cracked and the cracked cores are represented by the cross marks. In the analysis, thecenter area of the deck shows lower density, which indicates the existence of the internal crack.
And we discovered that the cracked cores corresponded to the low source density area. Thus,
the ability of our technique to detect internal cracks was confirmed. In this case, thecracked area was so large that the some sensors were located just under the crack.
Therefore, the internal crack was clearly detected. In the case that the cracks were smaller
than the distance between sensors, the result could be less clear and should be diagnosed
more carefully.

Figure 6 Sensor arrangement in highway bridge deck

C10

C11

C13

C12

C14

[mm]
C10
C12
C13
C11

C16
C14

C15

C15

C17

C16

C17

C18

Figure 7 Analysis and coring results of highway bridge deck

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C18

5. CONCLUSION
We proposed a novel inspection method to detect the internal cracks of the bridge decks. We
attached the AE sensors on the undersurface of the decks and analyzed the elastic waves
propagated from the uniformly distributed sources on the upper surface of the decks. Our
method successfully detected the internal cracks efficiently in the few minutes of measurement.
It is also discovered that the sensor position relative to the crack position affect the source
distribution and the sensor arrangement have to be considered at the diagnostics.

ACKNOWLEGEMENT
This work was supported by the New Energy and Industrial Technology Development
Organization (NEDO) of Japan.
REFERENCES[1] “WHITE PAPER ON LAND, INFRASTRUCTURE, TRANSPORT AND TOURISM IN JAPAN,
2014”, Part 2, Chapter 2, http://www.mlit.go.jp/common/001113562.pdf[2] T. Shiotani, D. G. Aggelis, O. Makishima：”Global monitoring of large concrete structuresusing acoustic emission and ultrasonic techniques: case study”, Journal of Bridge Engineering,Vol. 14, No.3, 188-192, (2009)[3] Y. Ueda, T. Usui, T. Omori, H. Takamine, K. Watabe, T. Shiotani, Proceedings ofConference on Information, Intelligence, and Precision Equipment (IIP2018), the Japan Societyof Mechanical Engineers, (in Japanese)[4] K. Watabe, H. Takamine, T. Nishida, T. Shiotani, “Novel Non-destructive Technique ofInternal Deterioration in Concrete Deck with Elastic Wave Approaches”, Asset Intelligencethrough Integration and Contemporary Vibration Engineering Technologies, 665, (2017).
[5] H. Asaue, T. Shiotani, T. Nishida, K. Watabe, H. Miyata, “Applicability of AE Tomography for
Accurate Damage Evaluation in Actual RC Bridge Deck”, Structural Faults & Repair-2016
Conference[6] C. Granier, T. Shiotani, K. Hashimoto, T. Nishida, “Visualization of internal damage in RC
slab with single side access attenuation tomography”, Journal of AE, vol. 34, pS54-S58, (2017)[7] H. Takamine, K. Watabe, H. Miyata, H. Asaue, T. Nishida, T. Shiotani, “Efficient damage
inspection of deteriorated RC bridge deck with rain-induced elastic wave”, Construction and
Building Materials 162, 908-913, (2018).
[8] K. Hahimoto, T. Shiotani, N Okude, C. Glanier, K. Watabe, H. Takamine, “Simulation
Investigation Of Elastic Wave Velocity and Attenuation Tomography Using Rain-Induced
Excitations”, Proc. of Structural Faults + Repair 2018

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Monitoring Geochemically-induced Fractures in Synthetic Rocks
Chven A. Mitchella, Laura J. Pyrak-NoltebaPurdue University – Department of Earth, Atmospheric and Planetary Sciencesb
Purdue University – Dept of Physics & Astronomy, West Lafayette, IN
ABSTRACT

Volumetric changes in clay have the potential to produce geochemical-geomechanicalinteractions in rock that can lead to induced microfractures. Acoustic emission (AE)monitoring coupled with micro X-ray Computed Tomography was used to investigate
microstructural alterations of synthetic rock caused by volumetric changes in clays during drying.
Synthetic rock samples with and without clays were fabricated from cement and sand using
protocols to produce repeatable behavior. For clay-rich samples, the type and percentage of clay
were varied to identify the effect on micro-crack generation during drying. Curing methods andwater content were also controlled, both of which affect host rock strength. During drying,
acoustic emissions (AE) were monitored. All samples were imaged with a 3D X-Ray Microscope
to examine microstructural changes. Synthetic rock samples with high clay content produced
more AE events than samples with lower clay or no clay content. If the water content wasincreased, less AE was observed during the monitoring period and 3D XRM analysis of thesamples showed that less damage occurred during drying. If clays were localized within the
body of the synthetic core, fractures were observed only in these areas. X-Ray Powder
Diffraction was performed on untreated and treated Montmorillonite clay particles to understandthe chemo- mechanical behavior of the clays. This analysis revealed changes in the interatomic spacing of the clay minerals, which are indications of changes in the lattice volumeof the minerals. The results suggest that hydration or dehydration of clay-rich rock during thewithdrawal or injection of fluids into the subsurface has the potential to induce microstructural
damage from geochemical-geomechanical coupling.
Keywords: AE, chemo-mechanical coupling, induced fractures, synthetic rocks, clay
1. INTRODUCTION
The capacity of a subsurface reservoir to securely store and retain captured fluids overlong periods of time is an extremely important aspect of nuclear waste disposal, Carbon
Capture and Storage, and CO2 Sequestration. This means that the caprock, which overlies
any subsurface reservoir selected for storage, must be a capable unyielding impermeablebarrier against fluid flow or fluid migration. Two vital macro-scale characteristics of good
caprock (seal) layers are continuity and ductility (Downey, 1984). Another importantcharacteristic of a good caprock is some resistance to in-situ chemo-mechanical interactions
in rocks between pore fluids and the mineralogical composition that can alter fracture
geometry and degenerate the integrity of caprock systems.

th

9 International Conference on Acoustic Emission

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For selected subsurface storage reservoir systems the caprock is expected to possess ideal
physical and chemical characteristics to be a barrier to flow. Commonly identified caprocks forCO2 storage (Song and Zhang, 2013; Griffith, 2011), are evaporites (salts and anhydrite) andargillaceous rocks (mudstones, clays, and shales). Shale rocks for the most part are comprisedof feldspathic minerals, carbonates, and a varied amount/type of clay (kaolinite, montmorillonitesmectite, illite or chlorite). An important aspect of shale mineralogy is the type and amount ofclay minerals embedded in the matrix. Clay minerals are phyllosilicates that crystalize into
small-size platy grains, typically less than 10µm (Espinoza and Santamarina, 2012; Mitchell andSoga, 2005; Sposito, 1989). Clay and clay-bearing rocks like shale are extremely water
sensitive (Sone and Zoback, 2014). Clays are known to shrink or swell upon interaction withfluids (Wagner, 2013) and clay swelling or shrinkage depends mainly on the type and mineral
structure of the clay. A clay-rich mineralogy can greatly influence the subsequent hydro-chemomechanical behaviors of shaly caprocks, which in turn affects the mechanical moduli,
microstructure, and geophysical properties of rocks (especially in cases where geochemicalgeomechanical interactions are externalized as discontinuities). The detection of suchgeochemically induced alterations and evolutions in the microstructure of rocks in thesubsurface is a challenge in geosciences. With this in mind and also because shale isconsidered an ideal barrier for geological storage systems, we fabricated synthetic clay-richrocks in an attempt to replicate (with much simplification) the mineralogy and microstructure ofthis interesting rock. These clay-rich geoarchitected rocks were then used to examine the role ofinduced volumetric changes in clays (from chemo-mechanical coupling) on the integrity andfracture generation in synthetic rock during drying.
2. METHODS AND PROCEDURES
Scanning Electron Microscopy (SEM) for Clay Analysis and Characterization
Scanning Electron Microscopy (SEM) was performed to analyze and characterize the materials
used in the experiments. An FEI NOVA nanoSEM Field Emission System with an Everhart
Thornley Detector (ETD) was used at high vacuum to obtain high-magnification and highresolution images of clay, sand and ordinary Portland cement.
X-Ray Powder Diffraction (XRPD) for Clay Analysis and Characterization
Clays were examined in an untreated state and treated states using X-Ray Powder Diffraction toquantify the changes in the inter-atomic distance (d-spacing). Treated clays were prepared byfirst decanting them to separate the clay particles from silt particles. Then the clay particles were
pipetted onto a glass slide and allowed to sediment and dry out overnight in an oven at 30oC.
XRPD was then performed on the treated samples after which all samples were glycolated
which is a process whereby the samples are placed in a desiccating chamber above a 2cm
layer of ethylene glycol to induce swelling of the clay structure. The desiccator was then placedin the oven overnight to enable the ethylene glycol to steam into the clay structure and induce
swelling.

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Fabricating Samples
Clay minerals and raw materials used for the fabrication of geo-architected rock are listed
below.
1.
2.
3.
4.

Montmorillonite(Bentonite): BIM
Montmorillonite: SAK-10
Ordinary Portland Cement: OPC
Ottawa Sand: SCS250

IMERYS Bentonite distributors
Sigma Aldrich (SA) – K10
LAFARGE – Alpena Plant
US Silica - product SIL-CO-SIL Ground
Silica - sand particle topsize @250 um

The same proportions by weight, of the material used to create an archetypal rock (sample 1 inTable 1) was used with the addition of 20% clay, for clay-rich rocks, and samples with 25%
water content (Table 1) and 30% water content (Table 2) were fabricated. All clays were
hydrated before adding to the archetypal mix. All mixtures were deposited into cylindricalsilicone molds with an average diameter of ~38 mm and an average length ~76 mm. The mold
was then placed in a room temperature storage container for a period of 24 hours, after which
the hardened samples were removed from the mold, and subjected to curing at 30oC for 7 days
(unless noted otherwise).
Table 1: Material composition of fabricated samples at 25% water content
Sample 1

Sample 2

Sample 3

Sample 4

CementCementCementCement

Sand

Sand

Sand

Sand

20% SAK-10

20g (g or %) Localized
K10

20% SAK-10 (No curing)

Table 2: Material composition of fabricated samples at 30% water content
Sample A

Sample B

Sample C

Sample D

CementCementCementCement

Sand

Sand

Sand

20 % BIM

20% SAK-10

3D MicroCT imaging
A Zeiss Xradia 510 Versa - 3D X-Ray Microscope (3D XRM) was used to image each sample
after the AE monitoring period. The Versa system maintains an in situ temperature of ~28oC.

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Object Research Systems (ORS) Dragonfly Pro software was used to visualize and quantify theporosity and fracture network generated in the samples.
AE Monitoring
Acoustic-emission (AE) measurements were made to monitor time-dependent crack formationin all samples. The samples were monitored using an array of six (6) Physical Acoustics
Corporation transducers - F15α sensors (with flat frequency response between 20-400kHz).
The sensors were connected via preamplifiers to a Mistra/Physical Acoustics AE measurement
system with a 10MHz sampling frequency. The threshold amplitude was set at 27dB. To
account for background noise, AE on an aluminum sample of similar dimensions was monitored
alongside the geo-architected sample for the sample experimental time window. All transducers
were secured to samples using hot Gorilla Glue. During monitoring, acoustic sensor
transmission tests (AST) were also performed before and after active monitoring to determine
the effect of drying and crack formation on compressional wave speeds. AST is a process bywhich the system allows the transducers to act as both a source and a receiver. The AST data
provides velocities across the sample and between transducers, which are calculated from therespective arrival times.
3. RESULTS
XRPD patterns revealed a shift in the inter-atomic distance of the clay structure. From a driedstate to a swollen state, BIM clay exhibited an increase of 7.62 x 104 um in the inter-atomic
distance (d-spacing). Conversely, no significant change in the d-spacing was observed for the
Sigma Aldrich – Montmorillonite K10 (SAK10) sample. The SAK10 clay under glycolated andoriented conditions had approximately the same d-spacing (9.73 x 104um) as the d-spacing for
the SAK10 clay particles in the untreated state (10.12 x 104um). From SEM imaging, thestructure of the Kaolinite (non-swelling) clays was stacked and platelet-like, while the structureof the Montmorillonite clays consisted of stacked undulating surfaces. Conversely the structureof the sand and the Ordinary Portland cement was prismatic and not stacked.
The microstructures of the synthetic rocks imaged after AE monitoring show that the porosityand fracture network increased with the addition of swelling clays. No microfractures areobserved for the cement only or cement mortar samples. When the clay was localized, fractures
are observed only within the clay region and the sample porosity increased because of debonding of the localized clay volume from the cement mortar host matrix. When 20%
montmorillonite clay is added to a cement mortar mix, a large volume of microfractures areobserved, with the SAK10 montmorillonite clay having a larger increase in porosity and afracture network that expands towards the center of the core.
In Figure 1, the AE recorded for samples 1,2,3 and samples B, C, D (Tables 1 & 2) are shown.
Geoarchitected rocks with 20% distributed clay (Figure 1.c) with a weaker host matrix generated

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a large number of AE that spanned a frequency range of 50-300KHz, the events cluster into twogroups. When the clay is localized, the same clustering behavior of AE is observed but fewer
AE are detected (Figure 1.b). Fewer AE are recorded for a synthetic rock with the same sample
mineralogy as sample 2 but 30% water content (Figure 1.f), and the clustering of AE is not as
distinct. In Figure 1.e, for a synthetic rock of 20% unprocessed BIM and 30% water content,
fewer AE are recorded than that of sample D (Figure 1.f). Lastly, for a synthetic rock with thesame mineralogy as sample 1 (Figure 1.a) but a water content of 30% (Figure 1d), more AE isrecorded with a broader frequency range.

Figure 1: Peak Frequency (kHz.) vs. Amplitude for the recorded AE of samples (a)-(f)4. CONCLUSIONSSEM of the materials used in this study, indicate that the Montmorillonite clay minerals have amuch different structure from that of ordinary Portland cement, Sand, and Kaolinite. XRPD
patterns revealed changes in the inter-atomic spacing of the Montmorillonite clays under
different conditions. These geochemically induced local volumetric changes in swelling clays insynthetic rocks induced fractures and promoted fracture network growth during drying of a hostmatrix in an unpressurized system. The volumetric changes and the drying of the host-rockgenerated acoustic emissions that had distinct clustering by signal frequency that depended on
the fabrication procedure or mineralogy. The presence of swelling clays in a rock promotes
micro-cracking of the host matrix which were observed via 3D MicroCT of the structure after
drying, and reflected by an uptick in number of recorded AE events which increased with

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increasing clay content. When the clay was localized, the measured AE was also attributed tothe de-bonding of the clay from the host rock matrix. AE observed in the samples with no clay
are attributed to the movement of the drying front. Geo-architecting clay-rich rocks provide a
unique opportunity to monitor the changes induced by the chemo-mechanical behavior of clays
and analyze the effect on the microstructure. The chemo-mechanical behavior of clays has thepotential to negatively impact the containment integrity of geological storage systems.
5. REFERENCES
Downey, M.W. 1984. Evaluating seals for hydrocarbon accumulations. AAPG, Vol. 68. No. 11:
1752-1763.
Espinoza, D.N. and J.C. Santamarina, 2012. Clay interaction with liquid and supercritical CO2:
The relevance of electrical and capillary forces. International Journal of Greenhouse Gas
Control, 10: 351-362.
Griffith, C.A, D.A. Dzombak, and G.V. Lowry. 2011. Physical and chemical characteristics ofpotential seal strata in regions considered fro demonstrating geological salinesequestration. Environ Earth Sci., 64: 4,925 - 4,948.
Mitchell, J.K. and K. Soga. 2005. Fundamentals of Soil Behavior, 3rd ed. Wiley.
Sone, H. and M. D. Zoback. 2013. Mechanical properties of shale-gas reservoir rocks - Part 2:
Ductile creep, brittle strength, and their relation to the elastic modulus. Geophysics 78:
5,393-402
Sone H. and M.D. Zoback. 2014. Time-dependent deformation of shale gas reservoir rocks andits long term effect on the in situ state of stress. International Journal of Rock Mechanics& Mining Sciences 69: 120-132. Song J. and D. Zhang. 2013. Comprehensive Review ofcaprock-sealing mechanisms for geologic carbon sequestration, Environ. Sci. Technol.,
47: 9-22.
Song J. and D. Zhang. 2013. Comprehensive Review of caprock-sealing mechanisms forgeologic carbon sequestration, Environ. Sci. Technol., 47: 9-22.
Sposito, G. 1989. The Chemistry of Soils, Oxford University Press, New York. Espinoza, D.N.
and J.C. Santamarina, 2012. Clay interaction with liquid and supercritical CO2: The
relevance of electrical and capillary forces. International Journal of Greenhouse Gas
Control, 10: 351-362.
Wagner, J.F. 2013. Mechanical Properties of Clays and Clay Minerals, Ch. 9: pp. 347-381.
Developments in Clay Science, vol. 5A

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Omnidirectional Horizontal Shear Wave Sensor for Enhanced
Acoustic Emission Data Collection and Analysis
Jason Philtrona, Cody Borigoa, Steven Owensa, Ronnie K. Millerb, and Joseph L. Roseaa
Guidedwave, 450 Rolling Ridge Dr., Bellefonte, PA 16823;b
Independent AE Consultant
ABSTRACTA new, patent-pending, omnidirectional shear wave acoustic emission (AE) sensor has beendeveloped. This sensor provides several advantages over conventional AE sensors, such as
improved and simplified location analysis, reduced sensitivity to environmental noise, andincreased options for source type identification and characterization. Additionally, the active
piezoelectric element is also designed as an ultrasonic source, allowing use in emerging hybrid
AE-guided wave passive/active test applications. Source localization is improved because
threshold-based triggering can occur off of a single mode, the fundamental shear horizontal (SH)mode, with a single velocity: the shear wave velocity. The fundamental SH mode wave velocity is
independent of test structure thickness or frequency, simplifying user input velocity duringlocalization analysis and removing the need for user calibration. This is in contrast to conventional
sensors, which detect multiple Lamb-type modes (e.g., S0 and A0) with different velocities that
depend on frequency and test structure thickness, and where multimode content or inaccurate
input velocity can generate significant error during location analysis. Reduced sensitivity toenvironmental noise is achieved because sources such as rain primarily excite Lamb-type modes
with out-of-plane displacement content, and not SH-type waves. The shear sensor can be usedalone or in tandem with conventional Lamb-type AE sensors for mode content identification.
Identifying relative mode content of AE signals can be advantageous for source type identification,system development for new applications, and research purposes. Additionally, the shear sensors
show promise for use during structural health monitoring applications where source
characterization is desired in addition to accurate source location.
Keywords: shear, localization, characterization, sensors, environmental noise
1. INTRODUCTION
As the stress wave generated by an acoustic emission (AE) source travels to a receiver it travels
as a guided wave (GW). For conventional AE sensors, a piezoelectric disk is often used whichreadily detects Lamb-type GWs. Historically it has been more challenging to fabricate shear-type
guided waves sensors than Lamb-type guided wave sensors, and this is certainly true if
omnidirectional sensing is desired. Previously, some shear sensors have been devised using
wedges to convert wave types. Alternatively, highly-directional shear bars have been used tocreate shear sensitive sensors. Although these directional shear sensors can be used effectively
for 1-D AE problems, such as along a single bar with transducers at each end, generally AE
solutions require omnidirectional transducers. This omnidirectional characteristic is important for
AE’s adaptability for different structures and geometries, a key advantage that AE has over othernondestructive evaluation techniques. See Figure 1.

9th International Conference on Acoustic Emission

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Figure 1. The Omnidirectional Shear Wave AE sensor characteristics

A new shear sensor has been developed that allows omnidirectional sensing of shear horizontal(SH) guided waves. The fundamental SH GW mode is non-dispersive, meaning that the wave
velocity does not change with frequency or structure thickness. Additionally, the wave velocity ofthis mode is equal to the bulk shear wave velocity, so a look-up table can be used to assign thevelocity for location calculations and pencil-lead break calibrations are not necessary.
This paper describes fundamental guided wave mode displacement characteristics and gives
several examples showing the increase in location accuracy, precision, and ease of use with theshear sensors. An experiment with simulated rain is also conducted to demonstrate therobustness to environmental noise. A discussion follows on the use of this new sensor for source
type classification.
2. GUIDED WAVE MODE DISPLACEMENT
2.1 Displacement wave structure
There are two types of guided wave modes that travel in plate structures: Lamb-type and SHtype. Lamb-type modes have displacement along the direction of wave propagation and out-ofplane through the thickness of the plate structure. At relatively low frequencies, the fundamental
symmetric Lamb wave mode primary has displacement along the direction of wave propagation,and the fundamental antisymmetric Lamb wave mode primary has displacement out-of-planethrough the thickness of the plate. As frequency and/or plate thickness is increased, thedisplacement characteristics of Lamb-type waves are generally mixed between these two
directions. In contrast, SH-type modes have displacement perpendicular to wave propagation, inplane with the plate structure as shown in Figure 2. Lamb type waves with dominant displacementin the x-z direction are those obtained with the traditional commercially available AE sensors formany years. The SH type waves with dominant displacement in the y direction is obtained withthis new horizontal shear wave sensor presented in this paper.

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Figure 2. Generalized polarization of guided waves in plates. Lamb-type modes have displacements inthe x- and z-directions, while SH-type waves have displacements in the y-direction.

2.2 Mode dispersion
It is necessary to view a dispersion curve to understand why the fundamental shear horizontal
mode is ideal for AE location analysis. Figure 3 displays phase and group velocities for guided
wave modes in an aluminum plate. As is shown, generally all modes are dispersive, i.e., thevelocity changes as frequency and/or plate thickness changes. However, the one exception tothis is the fundamental shear horizontal (SH0) mode, for which the velocity is independent of
frequency or plate thickness. In addition, the velocity of the SH0 mode is equal to the bulk shear
wave velocity of the material. Note also that this mode has the highest group velocity of all of theSH modes, meaning that it will always be the first arrival. Sensitivity to this mode is useful for
accurate AE location analysis precisely because the velocity will not change for structures of thesame material, but have different thicknesses or for which sensors with different frequency ranges
are used. This is therefore no need for velocity calibration when setting up AE tests, and also theresultant location error from triggering off of multi-mode content is highly reduced.

Figure 3. Phase (above) and group (below) velocity dispersion curves for Lamb-type (left) and SH-type(right) guided wave modes in an aluminum plate. Note that the fundamental SH mode (SH0) is the only
mode which is nondispersive. After [1].

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3. SAMPLE LOCATION DATA
3.1 Description of sample location data
In this section, sample location data is presented for two steel pipes which have different
thicknesses. The location performance achieved by conventional 150 kHz resonant sensors iscompared to the new omnidirectional shear AE sensors. Data is presented for 250 kHz resonant
shear AE sensors on an 8-inch diameter pipe and then 100 kHz resonant shear AE sensors on a
12-inch diameter pipe. In both cases, the shear AE sensors show improved location analysis,
particularly in the case of simulated noise environment where the triggering threshold was raised
from 40 dB to 50 dB. See Figure 4.

Figure 4. A sample result showing the sharp leading edge SH0 mode for accurate triggering in location
analysis.

3.2 Data from 8-inch schedule 40 steel pipe
Two conventional 150 kHz resonant AE sensors (Channels 1 & 2) and two new omnidirectional
250 kHz resonant shear AE sensors (Channels 3 & 4) were placed 10-ft apart on a section of 8inch schedule 40 steel pipe (0.32-in thickness). Three pencil-lead breaks (PLBs) were conductedat the locations along the length of the pipe shown in Figure 5. The PLBs were conducted in small
drilled holes in the pipe, because it is theorized to better simulate true AE events because theevent happens within the structure instead of on the surface, and because it gives a more equal
excitation of mode content between Lamb-type and SH-type modes than for surface-breaking
PLBs.

Figure 5. Schematic of location accuracy test setups on an 8-inch diameter steel pipe. Pencil lead breaks(PLBs) were conducted in drilled holes at the locations noted along the pipe length.

These PLBs were repeated with different threshold settings. First, a 50 dB threshold was usedwith the shear ring sensors, such that the software triggered off of the main arrival of the shear
wave packet. This setup gives excellent results. Simultaneously, a 40 dB threshold was used withtraditional type R15I sensors, which is the threshold level that would typically be used in low noise
environments. This setup gives good to fair results. Finally, a separate set of PLBs was performed
with a 50 dB threshold for the R15I sensors. The 50 dB threshold level simulates what might beused in a higher noise environment, such as when vibration noise from running machinery or

S82minor amounts of noise from environmental sources is present. A higher threshold is typicallyused to eliminate false damage calls due to noise. This setup results in a poor localization
capability for the R15I sensors.

Counts (bin width = 0.5 in)

To visualize the amount of error in the location calculated for each PLB, histogram plots werecreated. Histograms display the number of data points (called counts) within successive fixed
ranges (called bins). Figure 6 shows histograms of the error of each PLB for the three different
sensor and setting configurations. Data points are plotted for each of the three PLB trials at eachlocation (except in the case of the R15I sensor with a 50 dB threshold, where multiple PLBs failed
to localize). The bin width is 0.5 inches, giving bins with the ranges: -0.25 to 0.25”, 0.25 to 0.75”,
0.75 to 1.25”, etc. Bin labels are the value at the center of each bin, which are 0”, 0.5”, and 1” forthe bins listed above.
SR sensor, THD = 50 dB

R15I sensor, THD = 40 dB R15I sensor, THD = 50 dB

20

20

20

18

18

18

16

16

16

14

14

14

12

12

12

10

10

10

8

8

8

6

6

6

4

4

4

2

2

2

0

-10

0

Error (in)

10

0

-10

0

Error (in)

10

0

-10

0

10

Error (in)Figure 6. Histogram displaying 1-D location error data from tests on the 8-inch pipe. The number of counts
is shown for 0.5 inch bins for the three test configurations: shear ring sensor with 50 dB threshold (left,
green), conventional sensor with 40 dB threshold (center, blue), and conventional sensor with 50 dB
threshold (right, red). The higher number of counts near 0 inches for the shear ring sensors indicates amore accurate (better) localization result.

3.3 Data from 12-inch schedule 40 steel pipe
Two conventional (Lamb-mode sensitive) 150 kHz resonant AE sensors (Channels 1 & 2) andtwo new omnidirectional 250 kHz resonant shear AE sensors (Channels 3 & 4) were placed 15-ft
apart on a section of 8-inch schedule 40 steel pipe (0.41-in thickness). Three pencil-lead breaks(PLBs) were conducted at the locations along the length of the pipe shown in Figure 7. Location
error data was processed in the same manner as for the 8-inch pipe, and is shown in Figure 8.

Figure 7. Schematic of location accuracy test setups on a 12-inch diameter steel pipe. Pencil lead breaks(PLBs) were conducted in drilled holes at the locations noted along the pipe length.

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Counts

SR sensor, THD = 50 dB

R15I sensor, THD = 40 dB R15I sensor, THD = 50 dB

16

16

16

14

14

14

12

12

12

10

10

10

8

8

8

6

6

6

4

4

4

2

2

2

0

-10

0

10

Absolute error (in)

0

-10

0

10

Absolute error (in)

0

-10

0

10

Absolute error (in)Figure 8. Histogram displaying data from location tests on the 12-inch pipe. The number of counts is shownfor 0.5 inch bins for the three test configurations: shear ring sensor with 50 dB threshold (left, green),
conventional sensor with 40 dB threshold (center, blue), and conventional sensor with 50 dB threshold(right, red). The higher number of counts near 0 inches for the shear ring sensors indicates a more accurate(better) localization result.

3.4 Data Analysis
To interpret the plots, consider two extremes. First, if the localization calculation had nearly zeroerror, all of the data points would be in the 0” bin and a single bar would be shown. This case ismost like the shear ring data, which has the least amount of error, and in fact about two-thirds ofthe data points are in this bin for both pipe tests. Alternatively, if localization calculations wereimperfect, and worked sometimes but were incorrect at other times, we would expect to have avariety of error values and data would be spread out into many different bins. This case is most
like the conventional sensor with a 50 dB threshold, which has the most amount of error and only
one data point is in the 0” bin for both pipe tests. The data from the conventional sensor with a 40
dB threshold is in between these two cases, with data packed generally closer to the 0” bin as
opposed to being dispersed.
The data shown in the histograms in Figure 8 can be expressed numerically as well. The average(mean) absolute localization error for the shear ring sensor, 40 dB threshold traditional sensors,and 50 dB threshold traditional sensors in the 12 inch pipe test above are 0.26”, 3.6”, and 10.5”,
respectively. This metric succinctly describes the large amount of error that occurs for theconventional sensor.
It is clear that shear ring sensors show a distinct increase in localization accuracy when comparedwith conventional sensors, especially in a simulated noise environment, as represented by the 50
dB threshold example. Additionally, the shear ring sensors locate the PLBs very well on both pipe
sizes, which have different thicknesses (0.32” and 0.41”). The conventional R15 sensors don’t
perform equally on both pipe sizes because the guided wave modes detected have velocities thatchange with the thickness of the pipe. In fact, velocities of modes detected using conventional AE
sensors can range from 120,000 in/sec to 240,000 in/sec for carbon steel. PLBs must be
performed by the user on each test structure in order to correctly pick the velocity to use with theconventional R15 sensor for a given detection threshold on that structure. This is in contrast withthe shear ring sensors, which detect the shear horizontal guided wave mode; this mode always
travels at the shear wave velocity (128,000 in/sec for carbon steel).
The advantage of using a constant velocity for location algorithms cannot be overstated. User
definition of the wave velocity experimentally is a common cause of error and confusion in the
setup and analysis of conventional AE sensors. This error would add additional error to the data
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collected above and was not considered in the error comparison in this experiment. Additionally,in structures where the thickness changes from one point to another, conventional AE sensor
source location will have difficulties while the shear ring sensors will not. Use of the shear-type
mode was a key to commercial success in other guided wave applications, for example in longrange pipeline inspection and phased array plate inspection, and its benefits are being realized
for this application as well.
4. ENVIRONMENTAL NOISE
An experiment with simulated rain was also conducted to demonstrate the robustness toenvironmental noise. The sensor setup illustrated in Figure 5 was used and a garden hose wasused to spray water onto the pipe in the region between the sensors to simulate moderate rain.
The thresholds used were those that corresponded with highest location accuracy, which were
50 dB for the shear sensors and 40 dB for the conventional sensors. After 3 seconds of spray,
1328 hits were recorded by the conventional sensors and 0 hits were recorded by the shear
sensors. This demonstrates the ability of the shear sensors to continue accurate location analysis
for cases where noise from environmental or mechanical sources may be present. It can also be
noted that the shear sensors did detect the water drop signals when the threshold was lowered
considerably. The water drop signals detected by the shear sensors had amplitudes
approximately 20 dB lower than for conventional sensors.
5. SOURCE TYPE CLASSIFICATIONThe shear sensor also opens new opportunities for use in source classification and direct
experimental measurement to further develop theoretical wave models. When used in conjunction
with conventional sensors, all guided wave modes existing within the structure can be detected.
This can allow for improved source classification, if information is known about the relative mode
content generated by different types of sources (cracks of different orientations and severities,
corrosion, fretting, etc.). Additionally, since the mode content from both Lamb-type and SH-type
waves can detected, validation of new theoretical models and finite element modeling results canbe achieved.
6. CONCLUDING REMARKSThe new omnidirectional shear wave sensor has great potential for enhanced acoustic emission
data collection and analysis. It is proposed to use this sensor in combination with the traditional
Lamb-type sensor to achieve a number of possible strong benefits. A brief list of benefits
follows. See Table 1.

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Table 1. Potential benefits of using the Omnidirectional Horizontal Shear Wave Sensor for Improved
Acoustic Emission data collection and analysis.

•
•
•
•
•

•
•

Simple calibration and setup.
Improved location analysis.
Significant reduction in sensitivity to water loading and other environmental conditions.
May find defects earlier or possible those not even seen by conventional Lamb-type AE
probes.
Source identification may be possible when used with conventional Lamb-type sensors(utilizing pattern recognition, machine learning, AI, in providing a substantially rich physically
based feature vector).
Sensor can be used in passive, then active mode for improved defect characterization.
Greater possibility of using a variety of different and efficient multi-element, multi-mode, andfrequency transducers.

7. REFERENCES
1. J.L. Rose, Ultrasonic Guided Waves in Solid Media, Cambridge University Press, Cambridge,
UK, 2014.

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Characterization of Fiber Break by Quantitative Acoustic
Emission in a Model Carbon/Epoxy Composite
Zeina Hamama), Nathalie Godina), Claudio Fuscoa), Thomas Monnierb)a) Univ Lyon, INSA de Lyon, MATEIS UMR 5510, F-69621 Villeurbanne, France.
b) Univ Lyon, INSA-Lyon, LVA EA677, F-69621, Villeurbanne, France.

ABSTRACTIn this work, a modelling approach in order to predict the characteristics of the AE signal due tofiber break is developped. For this purpose, we model AE using the Finite Element Method andwe compare the simulation with experimental results of Single Fiber Fragmentation Test
(SFFT). The SFFT enables to preferentially produce fiber break. It is carried out on dogboneshaped specimens made from a long carbon fiber T700 embedded in an epoxy resin. In order togather information on a wider frequency bandwidth, we use two different types of transducer.
The analysis of the detected signals shows a strong dependency of the distance between
transducer and source on the frequency content of the signals. In this case, the high frequency
content of the signal associated to fiber breakage is not validated for every AE signature. For
the numerical simulations, the entire geometry of the specimen is considered. We model thefiber breakage by separating the nodes belonging to the faces of the fracture. The numerical
out-of-plane velocities are collected on the specimen surface. We consider six type of sensors,a point-contact sensor and five resonant sensors, whose transfer function were experimentally
determined by the reciprocity method. After being validated, the FE model is used to investigate
the effects of different parameters on the signal, such as the thickness of the specimen, theposition of the fiber, the position of the failure, the nature of the matrix and the sensors
response. The results of this contribution highlight the limitations of the conventional source
identification procedures. Furthermore, the parametric study allows generating training data formachine learning algorithms. With the numerical approach, it may be possible to enlarge thetraining set with numerical signals.
Keywords: AE modelling, Fiber break, FE model, descriptors, sensors effect.

1. INTRODUCTION
The Acoustic Emission (AE) technique is widely used to detect failures in composite material.
The different types of failures such as fiber breakage, interface debonding, matrix cracking anddelamination are identified thanks to their AE signature [1-4]. In most of the studies dealing with
failures in polymer matrix composites, higher frequency and higher amplitude are assigned tofiber breaks as mode of failure and lower frequency and amplitude are assigned to matrix
cracking. Despite their effectiveness, these methods of analysis are still considered as
qualitative methods. Besides, in many cases, the results of the analysis cannot be generalized
to other studies of composite failure [5-7]. In fact, the AE signals depend not only on the type of
failure but also on parameters like the type of sensor, geometry of the sample and the distance
between source and sensor. All these parameters affect the signal recorded by AE system [810]. In this context, in various works, AE signals are calculated by analytical approaches or by
finite element simulations [11-20] in order to understand the effects of transformations due to

9th International Conference on Acoustic Emission

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propagation medium and the recording system. In this study, we focus on the identification of
the features of fiber break from AE signals using Finite Element Method. For this purpose weuse a model material consisting of an epoxy matrix containing a single brittle carbon fiber for our
experimental tests and numerical simulations. The modeling of fiber breaks as AE source inpolymer matrix composite material is the main objective of this work. In the experimental part, aSingle Fiber Fragmentation Test (SFFT) is carried out on a carbon fiber embedded in the epoxy,in order to produce preferential fiber break. Two types of resonant sensors are used.
Concerning the modeling part, the entire geometry of the specimen is modelled using Finite
Elements Method (FEM) by means of Abaqus software and a dynamic calculation is performed.
The fiber breaks are created in different positions on the specimen. The model has beenvalidated by comparison with experimental data. Three type of sensors are considered (pointcontact and resonant sensors) in order to understand and to quantify the sensor effects on thesignal waveform.

2. EXPEREMENTAL PROCEDURE AND NUMERICAL MODEL
2.1 Experimental procedure
The specimen is made of a single long carbon fiber T700 embedded in epoxy/amine matrix. The
dimensions of the specimen are shown in Figure 1. The SFFT is a tensile test applied on single
fiber composite in the axial direction, in order to create fiber breakage. Usually the debonding
occurs at the same time as the fiber breaks, but in our case, because of a high interfacial
strength, debonding does not occur. The SFFT is carried out using the tensile test machine(type: MTS, capacity 5 kN), at room temperature and with a deformation rate of 0.5 mm/min.
The AE monitoring is carried out by means of multiple sensors using a PCI-2 data acquisition
system. Tensile tests are performed with two kinds of sensors (nano 30 and PicoHF sensors)
located at the same position on each face of the specimen 40 mm apart. These two sensors
display a good sensitivity in different frequency ranges, 200 to 900 kHz for nano 30 sensor and
500-1850 kHz for Pico HF sensor (MISTRAS Group data). In all cases, medium viscosity
vacuum grease is used as coupling agent. Each sensor is connected to a preamplifier (gain 40
dB, type 20 H) and AE signals are recorded by a PCI-2 acquisition system (MISTRAS Group).
Each AE signal waveform is digitized and recorded, with a sampling rate of 2 MSPS or 5 MSPS.
The acquisition threshold is set to 35 dB and the acquisition parameters are equal to 25 s, 50
s and 1000 s for the peak definition time (PDT), the hit definition time (HDT) and the hit
lockout time (HLT). All recorded signals are filtered in the bandwidth [20 - 1200] kHz.
The analysis of the experimental results is based on the calculation of descriptors as defined in[1, 21]. In our work, the main descriptors analyzed are amplitude, energy, frequency centroid(FC) and peak frequency (PF). Moreover, the time frequency graphs are obtained according tosmoothed pseudo Wigner-Ville distribution.

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a)

b)

Figure 1: a) Specimen with sensors positions and fiber break positions (Pi) for the simulation
and b) surface of the sensors.
2.2 AE model
The single fiber fragmentation is modeled using FEM by means of Abaqus® software (6.14-5)
with dynamic calculation. We modeled the entire geometry of the specimen. The geometry ismeshed using tetrahedral elements (C3D4) mainly, the element size varies between 0.01 mm
and 0.2 mm and the calculation step time is 0.1 µs. The fiber is located in the middle of thespecimen. Epoxy is modelled as an elastoplastic material with damping properties. The
viscoelasticity of the matrix is taken into account by the Rayleigh parameters ( α= 50 and β= 106). The fiber breakage is modelled by instantaneously separating the nodes forming fracture
faces and creating a wave. During the modelling of AE signals, it was assumed that the failure
process occurs without change in its rate. The numerical signals are collected on the specimen
surface, at sensor positions. Three types of sensor are considered in the modeling part: one
point-contact sensor and two resonant sensors. The first one is considered as perfect sensor,
which accurately preserves the detected signal, namely the out-of-plane velocity calculated on
the node corresponding to the sensor position (figure 1b). The resonant sensors are taken into
account by their transfer function, which was experimentally determined in our laboratory by thereciprocity method [21-24]. First, we calculate the out-of-plane velocity corresponding to thesensor surface contact and then we convolute it with the sensor transfer function. Simulated
signals are then filtered between 1 kHz and 1 MHz by a Butterworth bandpass filter, which is thefrequency range of interest in AE.
3. RESULTS AND DISCUSSION
In this section, first we compare the experimental and numerical results in order to validate theFEM model. Five fiber breaks are modeled at five different positions (denoted Pi) shown in the
figure 1. Each break is simulated for 150 µs, which is sufficient to attenuate the signal. We
calculated signals with sensor effects for the two types of sensors (nano30 and picoHF). Then,the simulated an experimental signals are compared. This comparison is carried out for signalsof five fiber breaks at different positions, collected at sensor C1 position (figure 2). A good
agreement is observed between the simulation and the experiment results, allowing us tovalidate the FEM model.

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Figure 2 : Comparison between the simulated signal and the experimental signal due to a fiber
break at two positions denoted P1 and P5 (P1 is the nearest position and P5 the farthest
position from the sensor).
In the model, the signal detected by perfect point-contact sensor, is the out of plane velocity of
the node at sensor position (Figure 1-b). The advantage of studying this kind of sensor is thatthe signal is not altered by the sensitivity of the sensor or its aperture effect of the surface
contact. Figure 3 shows the time-frequency transforms for the several fiber breaks (P1 to P5).
The frequency content of the signals is dominated by high frequency but it decreases farther
away from the source.

Figure 3 : Time-frequency results obtained with the pseudo Wigner-Ville distribution for threesource positions for signals recorded by perfect point-contact sensor denoted punctual (P1 isthe nearest position and P5 the farthest position from the sensor).
For one fiber break, signals are calculated at nodes on the line from the position P1 to the endof the specimen along the gauge length. It is equivalent to different sensor positons, therefore,
different distance source / sensor. The use of 2D Fourier Transform (Figure 4) allows tovisualize the modal content of propagating waves in the sample (figure 4a) and to visualize theeffect of the sensors (figure 4b). For point-contact sensor, the frequency content is significantly
higher comparing to signals detected by resonant sensors.

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a)

b)

c)
Figure 4 : 2DFFT (wavenumber k versus frequency f) of the numerical signals obtained for thefiber break at position P1, with a perfect sensor (a) with a picoHF sensor (b) and with a nano30
sensor (c).
4. CONCLUSION
The objective of this work is to build a quantitative relationship between the fiber break assource of Acoustic Emission and the detected signal by unravelling the effect of the different
stages of the AE acquisition chain. The advantage of simulating perfect point-contact sensor isto get signals not altered by the sensor effects. Therefore, the frequency content proves that thefiber breaks release waves with very high frequency content in single fiber composite specimen.
Contrary to signals recorded by cylindrical sensor, the frequency reached with this type of
sensor, for remotes sources, is still considered as high frequency. The results of thiscontribution highlight the limitations of the conventional source identification procedures.
Modeling approaches that simulate “primitive” AE sources can be useful for (1) optimizing
sensor selection and placement, (2) quantifying the wave propagation effects (3) generate
training data for machine learning algorithms. With the numerical approach, it may be possible
to enlarge a training set.
5. REFERENCES[1] Godin N., Reynaud P. and Fantozzi G., Acoustic Emission and Durability of Composite Materials, John
Wiley & Sons, 2018.

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[2] Anastassopoulos A. and Philippidis T., Clustering methodology for the evaluation of acoustic emission
from composites, Journal of Acoustic Emission, vol. 13, pp. 11-12, 1995.
[3] Sause M., In situ monitoring of fiber-reinforced composites: Theory, basic concepts, methods, andapplications. Springer series in materials science 242, 2016.
[4] Gutkin R., Green C., Vangrattanachai S., Pinho S., Robinson P. and Curtis P., on acoustic emission
for failure investigation in CFRP: Pattern recognition and peak frequency analyses, Mechanical Systems
and Signal Processing, vol. 25, n° 14, pp. 1393-1407, 2011.
[5] Oz F. E., Ersoy N. and Lomov S. V., Do high frequency acoustic emission events always represent
fibre failure in CFRP laminates?, Composites Part A: Applied Science and Manufacturing, vol. 103, pp.
230-235, 2017.
[6] Maillet E., Morscher G. N., Pujar V. V. and Lemanski J. R., Feasibility and limitations of damage
identification in composite materials using acoustic emission, Composites Part A: Applied Science andManufacturing, vol. 75, pp. 77-83, 2015.
[7] Godin N., Reynaud P., Fantozzi G., Challenges and limitations in the identification of acoustic
emission signature of damage mechanisms in composites materials. Appl. Sci., 8(8), 1267, 2018[8] Aggelis D., Shiotani T., Papacharalampopoulos A. and Polyzos D., The influence of propagation pathon elastic waves as measured by acoustic emission parameters, Structural Health Monitoring, vol. 11, no.
3, pp. 359-366, 2011.
[9] Carpinteri A., Lacidogna G., Accornero F., Mpalaskas A., Matikas T. and Aggelis D., Influence ofdamage in the acoustic emission parameters, Cement and Concrete Composites, vol. 44, pp. 9-16, 2013.
[10] Kharrat M., Placet V., Ramasso E., and Boubakar L., Influence of damage accumulation under
fatigue loading on the AE-based health assessment of composite material: Wave distortion and AEfeatures evolution as a function of damage level. Composites Part A: Applied Science and Manufacturing,
109, pp. 615-627, 2018[11] Scruby C., Wadley H. and Hill J., Dynamic elastic displacements at the surface of an elastic halfspace due to defect sources, Journal of Physics D-Applied Physics, vol. 16, pp. 1069-1083, 1983.
[12] Ohtsu M, Ono K, A generalized theory of acoustic emission and source representations of acoustic
emission. J. acoustic Emission 1986; 5:124-133, 1986
[13] Giordano M., Condelli L., Nicolais L., Acoustic emission wave propagation in a viscoelastic plate,Composites Science and Technology 59, 1735-1743, 1999.
[14] Sause M.G.R. and Horn S., Simulation of acoustic emission in planar carbon fiber reinforced plastic
specimens, Journal of Nondestructive Evaluation, 29 (2), 123-142, 2010.
[15] Hamstad M., O'Gallagher A. and Gary J., Effects of lateral plate dimensions on acoustic emission
signals from dipole sources, Journal of Acoustic Emission, vol. 19, pp. 258-274, 2001.
[16] Wilcox P., Lee C., Scholey J., Friswell M. I., Wisnom M. and Drinkwater B., Progress Towards a
Forward Model of the Complete Acoustic Emission Process, Advanced Materials Research , Vols. 13-14,
pp. 69-76, 2006.
[17] Cuadra J., Vanniamparambil P., Servansky D., Bartoli I. and Kontsos A., Acoustic emission source
modeling using a data-driven approach, Journal of Sound and Vibration, vol. 341, pp. 222-236, 2015.
[18] Prosser W.H., Hamstad M.A., Gary J. and O’Gallagher A., Finite Element and Plate Theory Modelingof Acoustic Emission Waveforms, Journal of Nondestructive Evaluation, 18 (3), 83-90, 1999.
[19] Sause M. G. and Richler S., Finite element modelling of cracks as acoustic emission sources,
Journal of nondestructive evaluation, vol. 34, no. 1, p. 4, 2015.
[20] Le Gall T., Monnier T., Fusco C., Godin N. and Hebaz S.E. , Towards quantitative acoustic emission
by finite element modelling : contribution of modal analysis and identification of pertinent descriptors,
Appl. Sci., 8(12), 2557; 2018[21] Morizet N., Godin N., Tang J., Maillet E., Fregonese M. and Normand B., Classification of acoustic
emission signals using wavelets and Random Forests: Application to localized corrosion, Mechanical
Systems and Signal Processing, vol. 70, pp. 1026-1037, 2016.
[22] Ono K., Through-Transmission Characteristics of AE Sensor Couplants, Journal of Acoustic
Emission, vol. 34, 2017.
[23] Goujon L. and Baboux J.-C., Behaviour of acoustic emission sensors using broadband calibration
techniques, Measurement Science and Technology, vol. 14, no. 7, p. 903, 2003.
[24] Monnier T., Seydou D., Godin N. and Zhang F., Primary calibration of acoustic emission sensors by
the method of reciprocity, theoretical and experimental considerations, Journal of Acoustic Emission, vol.
30, pp. 152-166, 2012.

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Damage Identification in Composites throughAcoustic Emission Monitoring
Neha Chandaranaa, Emmanuel Ramassob, Constantinos Soutisc, Matthieu Gresila
i-Composites Lab, School of Materials, The University of Manchester, Manchester, UK;
Department of Applied Mechanics, FEMTO-ST, Université Bourgogne Franche-Comté,
Besançon, France; cAerospace Research Institute, The University of Manchester, Manchester,
UK.
a

b

ABSTRACTIn this work, three carbon/glass hybrid composite tubes are instrumented with eight piezoelectricwafer active sensors (PWAS), used as passive receivers of acoustic emission (AE) signals. A
three point bending quasi-static loading is conducted, either in a single cycle until failure or
incrementally through multiple loading/unloading cycles. In the first instance, AE signal features
such as maximum amplitude, peak frequency, signal duration, and energy are used to distinguishbetween different damage mechanisms such as matrix cracks, delamination, and fibre breakage.
The group velocity of the longitudinal modes – L(0,1) and L(0,2) – is obtained experimentally in apitch-catch configuration between the PWAS network using the time of flight (ToF). The time ofarrival (ToA) method is then used to calculate damage source locations from the received AE
signals. To involve more signal features for data classification, an unsupervised clustering
algorithm is applied to the datasets. Optimisation of the number of clusters is completed by
maximising the robustness and minimising the uncertainty of the final result. It is found that thetemporal evolution of clusters indicate the ability to distinguish between the initiation and growthof damage, as well as identifying non-damage related signals caused by extraneous noise or
related to the test set-up.
Keywords: Composites, normalised mutual information, pattern recognition, piezoelectric
sensors, unsupervised clustering

1. INTRODUCTION
Composite materials have become attractive for use in structural applications over recent years,
since their properties can be tailored according to needs, but assessment of damage remains achallenge. Periodic non-destructive inspections (NDI) of components can give an insight into theirperformance but the complexity of these techniques often results in significant down-time andincreased labour costs. The cost of inspection in aerospace composites, for example, canrepresent up to a third of the lifecycle costs [1]. Since composite materials allow for the integrationof sensors, with negligible effect on their mechanical properties, permanent structural healthmonitoring (SHM) systems have sparked a great deal of interest [1]. Piezoelectric transducers are
particularly used for their low cost, small size, durability, and low power consumption [2].
In this work, quasi-static three point bending of carbon/glass hybrid composite tubes has been
completed. By monitoring acoustic emission (AE) signals during loading, it is possible to estimate
damage source locations and speculate about different damage modes [3], [4] via signal analysis.
The effort is in distinguishing between matrix cracking, delamination, and fibre breakage. An

9th International Conference on Acoustic Emission

S91unsupervised pattern recognition algorithm – an extension of [5] – has been applied to AE data
to understand the initiation and progression of damage when the data is separated into clusters.

2. MATERIALS AND METHODS
2.1 Instrumentation of composite tubes
The composite tubes (60.3 mm internal diameter and 1.6 mm thick) presented in this work are a
hybrid of unidirectional (UD) carbon fibres oriented in the axial (0°) direction and UD glass fibres
in the hoop (90°) direction (supplied by Easy Composites Ltd.). The lay-up order of fibers is [0,90, 0, 90, 0]. Each specimen was cut to a length of 1 meter prior to instrumentation with sensors.
Three specimens were tested, each instrumented with piezoelectric wafer active sensors (PWAS)[6] supplied by PI ceramic – PIC255 with 10 mm diameter and 0.5 mm thickness [7]. Eight PWAS
were surface mounted on each specimen for AE monitoring during loading. On specimen 1, 32
PWAS were also bonded for pitch-catch excitation of guided waves. The arrangement of PWAS
is shown in Figure 1.

Figure 1. Schematic showing the approximate positions of PWAS used for AE (green) and guided waves(blue) on specimen 3.

2.2 Experimental set-up
The experimental set-up for flexural three point bending is shown in Figure 2. Three specimens
were tested in total: specimen 1 was first subjected to a low velocity impact with approximately 5
J (resulting in an axial crack of approximately 7 cm length [8]), followed by three point bending
over five cycles of loading/unloading; specimens 2 and 3 were subjected to bending until failure.
The low frequency cycling of specimen 1 was introduced to encourage the progression of damage
through repeated loading. The load vs. time curves are shown in Figure 3.

Figure 2. Experimental set-up for three point loading of specimen 1.

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Figure 3. (a) Load vs. time for all cycles on specimen 3; (b) Load vs. extension for specimens 2 and 3.

2.3 AE data acquisition and processing
During loading, AE activity was recorded via eight channels on a PCI-2 based acquisition system
supplied by Mistras. Discrete AE waveforms (hits) were recorded using the ‘AEwin’ software
package. The threshold amplitude was set to 55 dB to eliminate sources of extraneous noise, andto account for the 20 dB of pre-amplifier gain per channel. The sampling rate was set at 10 MHz(5 MHz on 2 channels due to hardware limitation) and AE timing parameters set to: peak definition
time (PDT) = 200 µs, hit definition time (HDT) = 800 µs, hit lockout time (HLT) = 1 ms, maximum
signal duration = 100 ms. Using subsets of 4 from a total of 17 features (as extracted through‘AEwin’) per AE hit an unsupervised pattern recognition algorithm (based on the GustafsonKessel algorithm [9]) was applied to the full dataset for each specimen. For specimen 1, the fivecycles are concatenated in time to form one large dataset. The optimal number of clusters foreach dataset is found by maximizing the normalized mutual information criterion (NMI) [10].

3. RESULTS AND DISCUSSION
The time of arrival (ToA) method available within ‘AEwin’ is used to calculate AE source locations
in order to track the growth of damage in the specimens during each loading cycle of specimen 1(Figure 4). The presence of signals during earlier cycles of loading correlates well with the damage
locations observed in the final cycle of loading, suggesting that it may be possible to estimate thefailure location before reaching 70% of the breaking strength of the specimen.

Figure 4 (to be continued). Calculated damage locations during each loading of specimen 1: (a) cycle 1(blue ˅), (b) cycle 2 (green ˄), (c) cycle 3 (purple ˃), (d) cycle 4 (lime green ˂), (e) cycle 5 (turquoise □).

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Figure 4. Estimated damage locations during loading of specimen 1: (a) cycle 1 (blue ˅), (b) cycle 2(green ˄), (c) cycle 3 (purple ˃), (d) cycle 4 (lime green ˂), (e) cycle 5 (turquoise □). Dashed lines indicate
locations of the through-thickness final failure cracks. Red arrows indicate the top of the specimen.

Application of the unsupervised clustering algorithm on AE hits from specimen 1 results inseparation of the data into 9 clusters. The initiation and evolution of signals within each cluster,over the five cycles of loading are shown in Figure 5. A noteworthy observation is that, though thedata are separated into these 9 clusters, all clusters do not initiate at the beginning. In the present
work, a criterion related to the onset time of different clusters ensures that they are always spreadout in time. The reasoning being that damage in composites tends to be decomposed into different
phases with cascades during loading. Based on this, it is possible to develop a classification ofdamage types, for each cluster. It is argued that AE data belonging to cluster 1 are likely to arise
due to friction between the specimen and test equipment: this cluster represents the largest groupof data points, and data are acquired almost continuously during loading. Similarly, the data inclusters 2 and 3 does not result in a significant change in cumulated energy. AE data belonging
to clusters 8 and 9 are more likely related to the final failure of the specimen: an abrupt increasein energy is observed, indicating high energy, catastrophic damage events during these last few
seconds of loading. The final failure of the specimen took the form of two longitudinal cracks oneither side of the tube – a compression-type failure rather than true bending. This is due to thehigh proportion of axial fibres greatly enhancing the flexural stiffness of the tube. Clusters 4-7,

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therefore, likely represent damage signals arising from a combination of mechanisms such asdelamination, axial matrix cracking, and breakage of the hoop-oriented glass fibrils and/or fibres.

Figure 5. Number of AE hits recorded by all channels, separated into clusters, for specimen 3.

When the clustering algorithm is applied to specimens 2 and 3, both tested in a single cycle until
failure, the data is separated into 8 and 15 clusters, respectively. The behaviour of clusters inthese two specimens is shown in Figure 6. Although the specimens are assumed to be almost
identical, the AE signals recorded during loading can be affected by small variations in: material
properties, specimen geometry, sensor placement, sensor bonding, positioning with respect tothe machine, and noise in the environment. Number of clusters aside, the most obvious difference
when comparing the plots is the behaviour of clusters 2 and 4 on specimen 2. The exponential
increase in signals at around 400 s likely result from extraneous noise since the change incumulated energy is negligible. The envelope of each damage profile provides an assessment of
uncertainty related to the final clustering result.

Figure 6. AE hits from all channels separated into clusters for (a) specimen 2 and (b) specimen 3.

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4. CONCLUDING REMARKSIn the present work, the potential for the use of acoustic emission is demonstrated as a tool for
detecting, localising, and classifying damage mechanisms arising during quasi-static loading. The
use of an unsupervised clustering algorithm eliminates any operator bias and reduces the time
taken to obtain a final result. Further work is required to develop a means of linking data clusters
to individual damage modes in the composite, by means of destructive and non-destructive
validation via, for example, x-ray computed tomography. Identifying the location, size and type ofdamage is of great importance in the effort to estimate residual life and long term performance ofthe composite tube [11].

5. ACKNOWLEDGEMENTS
The authors acknowledge the funding and technical support from BP through the BP International
Centre for Advanced Materials (BP-ICAM) and financial support from EPSRC (EP/L01680X)
through the Materials for Demanding Environments Centre for Doctoral Training which made thisresearch possible. The authors also acknowledge the EIPHI Graduate School (contract “ANR-17EURE-0002” and FEMTO-ST Institute for both funding and technical input to support thecollaboration with The University of Manchester.

6. REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]

K. Diamanti and C. Soutis, “Structural health monitoring techniques for aircraft composite
structures,” Prog. Aerosp. Sci., vol. 46, no. 8, pp. 342–352, 2010.
N. Chandarana, D. M. Sanchez, C. Soutis, and M. Gresil, “Early damage detection incomposites during fabrication and mechanical testing,” Materials (Basel)., vol. 10, no. 7,
2017.
N. Godin, S. Huguet, and R. Gaertner, “Integration of the Kohonen’s self-organising map
and k-means algorithm for the segmentation of the AE data collected during tensile tests
on cross-ply composites,” NDT E Int., vol. 38, no. 4, pp. 299–309, 2005.
G. Kotsikos, J. T. Evans, A. G. Gibson, and J. Hale, “Use of acoustic emission tocharacterize corrosion fatigue damage accumulation in glass fiber reinforced polyester
laminates,” Polym. Compos., vol. 20, no. 5, pp. 689–696, 1999.
E. Ramasso, V. Placet, and M. L. Boubakar, “Unsupervised Consensus Clustering of
Acoustic Emission Time-Series for Robust Damage Sequence Estimation in
Composites,” IEEE Trans. Instrum. Meas., vol. 64, no. 12, pp. 3297–3307, 2015.
V. Giurgiutiu, Structural Health Monitoring with Piezoelectric Wafer Active Sensors, 1st
ed. Academic Press, 2008.
PI Ceramic, “Piezoelectric Ceramic Products.” [Online]. Available:
http://www.piceramic.com/download/PI_Piezoelectric_Ceramic_Products_CAT125E.pdf.
N. Chandarana, H. Lansiaux, and M. Gresil, “Characterisation of Damaged Tubular
Composites by Acoustic Emission, Thermal Diffusivity Mapping and TSR-RGB Projection
Technique,” Appl. Compos. Mater., vol. 24, no. 2, pp. 525–551, 2017.
D. E. Gustafson and W. C. Kessel, “Fuzzy clustering with a fuzzy covariance matrix,”
Proc. IEEE Conf. Decis. Control, pp. 761–766, 1979.
A. L. N. Fred and A. K. Jain, “Combining multiple clusterings using evidence
accumulation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 6, pp. 835–850, 2005.
C. Soutis and P. W. R. Beaumont, Eds., Multi-scale modelling of composite materials: theart of predictive damage modelling, 1st ed. Cambridge: Elsevier, 2005.

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Overview of Acoustic Emission in Monitoring
Powder Technology
Eric Serris, Ana Cameirao, Frédéric Gruy, Jean-Michel Herri, Gérard Thomas
Mines Saint-Etienne, SPIN EMSE, CNRS UMR 5307, LGF, CS 62362,
F-42023 St Etienne, France
ABSTRACT
Acoustic emission has been successfully applied for monitoring a large variety of industrial
processes. In this overview of several studies of application of acoustic emission in powder
technology, we try to develop this non-destructive technique in some processes with powder.
Acoustic emission during powder compaction allows us to understand the behavior of brittle
materials and quantify the fragmentation of such powders. With viscoelastic powder, thecompaction is different and we recorded weak acoustic emission. For crystallization processes,
we have an information of the beginning of nucleation of crystals before all other experimental online techniques. We can have also other information of crystal growth, crystal size and if we havere-dissolution of the product. Another process with powder is a slurry transport in a flow loop. The
first experiments show also the first moments of crystallization but we can have also informationof flow pattern of the slurry and the former emulsion state. To finish, we will discuss on a beginning
fundamental work on acoustic emission of particle impact on several surfaces. This work will help
us to better characterize all these processes with powders.
Keywords: monitoring, flow loop, crystallization, powder, compaction
1. INTRODUCTION
We show that acoustic emission (AE) is a very attractive non-intrusive technique for monitoring
crystallization processes. AE has been successfully applied in many fields of material sciences[1] and it was also used in the pharmaceutical industry for monitoring various chemicalengineering processes [2] including fluidized bed granulation [3], fluidized bed coating [4], powder
compaction [5-6], etc. However, few studies deal with the use of AE to monitoring crystallization
processes [7-11]. In this paper, we will give an overview of acoustic emission use in several
processes in powder technology.
2. EXPERIMENTAL RESULTS
2.1 Compaction of powders
All acoustic emission is recorded with a µ30 sensor from Mistras group with no filter in frequency.
The sensor is fixed with coupling grease on the wall of the die. Aspirin AC 360 is a crystallized
aspirin provided by Rhône Poulenc Rorer. The granulometry of grains is 360 µm and the grains
look like sticks. AE tests are realized following the same compaction procedure. The pressure
rising up to 300 MPa is achieved with a punch velocity of 1 mm min-1. This pressure is held for 1
min, then a pressure-drop followed with a punch velocity of 10 mm min-1. The results are
represented in Figure 1 with the curve of cumulative event counts as a function of axial stress.
Two sections are evident on the curve variations: before and after an axial stress of 100 MPa [6].

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Figure 1 : Cumulative event counts vs. axial stress for aspirin AC 360.
Before 100 MPa, high AE rates were recorded, and a lot of bursts can be observed on theoscilloscope screen. After 100 up to 300 MPa, no AE is recorded. AE is also undetectable during
the isobaric stage duration of 1 min, but not during pressure drop.
Specific surface area and porosity have been determined on tablet obtained for various values ofmaximal axial stress (punch velocity of 1 mm min-1 up to maximum stress, 1-min duration isobaric
step and 10 mm min-1 pressure drop). Figure 2 presents the results obtained concerning specificsurface area.

Figure 2 : Specific surface area of aspirin AC 360 vs. compaction pressure.
The specific area first increases up to 60 MPa, then it decreases. The only phenomenon that may
lead to a specific area increase is grain fragmentation. Thus, we can relate this fact to the AE
registered during the same time. We have to keep in mind that intergranular or intragranular
cracking may occur during compact ejection and, as a consequence, may induce errors in specificarea measurements. Nevertheless, we assume that these phenomena do not occur for thepharmaceutical products used in this work, as suggested by direct observations. Figure 3
presents the results obtained concerning relaxed porosity. The porosity decreases continuously
during the rise of compaction pressure, but very strongly during the first stage up to 100 MPa. We
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consider that plastic and viscoplastic deformations are responsible for this porosity drop. This
deformation is also responsible for the specific area diminution after a compaction pressure of 60
MPa by the way of an interface grain cling. Thus, for the studied aspirin, this deformation may
occur, but it is not detected by AE.

Figure 3: Relaxed porosity of aspirin AC 360 vs. compaction pressure.
As for the aspirin AC 360, we can conclude that grain fragmentation and rearrangement arecharacterized by AE emitted during compaction. This AE is represented by several bursts on theoscilloscope screen and by increasing rates of cumulative event counts. Furthermore, viscoplastic
deformation of aspirin grains is not followed by any noticeable AE.
2.2 Crystallization of powders

Two sensors: a WD and a F15 provided by Mistras whose center frequency are respectively of
450 kHz and 150 kHz are used in this work [12]. They are fixed directly on the vessel with couplinggrease.
The first experiment on citric acid is recorded after dissolving a large amount of powder (2 kg) in
1 L of pure water. We show the evolution of AE amplitude during three hours after the introductionof 500 g of citric acid in order to reach a supersaturated state. This evolution is plotted in Figure
4. Two periods (from 0 to 1000 s and from 5000 to 6000 s) of acoustic activity is observed. The
first one is the dissolution and the particles impacts of the powders. The other one is not obvious.
Normally nothing changes during this period of dissolution.

Figure 4 : Amplitude of AE (dB) versus time(s).
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In order to have an idea of what may happen during this period, let’s have a look on the evolutionof centroid frequency versus time.

Figure 5 : Centroid frequency (kHz) versus time (s).
The centroid frequency of dissolution phenomena is generally under 200 kHz but for the second
part of AE activity the frequency is above 200 kHz. In oxalate ammonium crystallization in Gherras
work [11], it is generally near 250 kHz for nucleation and/or growth of particles. Considering that
we are above the supersaturation stage, we have a crystallization of citric acid during this period.
2.3 Monitoring flow loop during hydrates formation
The acoustic emission sensor (Mistras, R15).was located at the beginning of the flow loop
horizontal section. Coupling grease was used between the loop wall and the sensor for improving
the transmitting efficiency of acoustic signals. The main experimental results are the acoustic
energy together with the pressure drop and the cumulated chord length for droplets, crystallized
droplets and agglomerates. Results are given in Figure 6.
Before the formation of hydrates, experiments show a regular oscillation in the absolute energy
characteristic of the state of the dispersion of the droplets in the continuous phase, corresponding
to a polydispersity population of droplets around 10 µm. When methane was injected, the absoluteenergy increased and return to the initial level before the beginning of crystallization. The hightransfer of gas into the oil phase seems to affect the attenuation of the signal in case of continuous
oil phase. The R15 sensor detected the beginning of crystallization by a steep AE event increase.
Even if some oscillations are observed, the tendency of the results is to increase and remain
approximately constant until the end of the experiment. The absolute energy seems mainly
correlated with the amount of hydrates and the size of the particles/agglomerates, and lesser but
still correlated with the continuous phase.
The AE sensor proved to be envisaged to measure the hydrates fraction along the pipe and also
to detect changes in the state of the liquid-liquid dispersion together with the solid, during
emulsification and crystallization and even plug and rheological study.

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Figure 6 : Energy, chord length and pressure drop during time for experiment at 40 % water cut
with oil phase at 400 L.h-1.
3. CONCLUSIONS
Acoustic emission can be useful to monitor industrial processes with powder. The laboratory
experiments have provided interesting results. In order to generalize acoustic emission in theseprocesses, we must go further in understanding particle impacts. Interesting works has been done
recently [14-16], but we need to have different modelling, taking into account all kind of powders
and all kind of surfaces. The use of a DEM (Discrete Element Method) approach coupled withcomputational fluid dynamics must be useful and help us characterize the velocities and theimpacts of particles with different flow regimes.
4. REFERENCE
1. A. Marec, J. Thomas, R. El Guerjouma, Damage characterization of polymer based
composite materials: Multivariable analysis and wavelet transform for clustering acoustic
emission data, Mech. Syst. & Signal Process., vol 22, 2008, 1441-64.
2. J.W.R. Boyd, J. Varley, The uses of passive measurement of acoustic emissions fromchemical engineering processes, Chemical Engineering Science, vol. 56, 2001, 17491767.
3. E.M. Hansuld, L. Briens, J.A. McCann, A. Sayani, Audible acoustics in highshear wet
granulation: Application of frequency filtering, International Journal of Pharmaceutics, vol.
378, 2009, 37-44.

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4. K. Naelapää, P. Veski, J.G. Pedersen, D. Anov, P. Jørgensen, H.G. Kristensen, P.
Bertelsen, Acoustic monitoring of a fluidized bed coating process, International Journal of
Pharmaceutics, vol. 332, 2007, 90-97.
5. J. Salonen, K. Salmi, A. Hakanen, E. Laine, et K. Linsaari, “Monitoring the acoustic activityof a pharmaceutical powder during roller compaction,” International Journal of
Pharmaceutics, vol. 153, 1997, 257-261.
6. E. Serris, L. Perier-Camby, G. Thomas, M. Desfontaines, G. Fantozzi, Acoustic emissionof pharmaceutical powders during compaction, Powder Technology, vol 128(2-3), 2002,
296-299.
7. E.L. Lube, A.T. Zlatkin, In-process monitoring of crystal perfection during melt growth,
Journal of Crystal Growth, vol. 98, 1989, 817-826.
8. T.Sawada, Y.Gohshi, C.Abe, K.Furuya, Acoutics Emission from Phase Transition of Some
Chemicals. Analytical Chemistry , vol 57, 1985, 1743-1745.
9. T. Chotard, A. Smith, N. Codet, M. De Baillencourt, D. Fargeot and C. Gault, Effect ofmalic and citric acid on the crystallisation of gypsum investigated by coupled acoustic
emission and electrical conductivity techniques, J. of Materials Science, vol 41(21), 2005,
7210-7217.
10. D. Betteridge, M.T. Joslin, T. Lilley, Acoustic Emissions from Chemical Reactions,
Analytical Chemistry. Vol 53, 1981, 1064-1073.
11. N. Gherras, E. Serris, G. Fevotte, Monitoring Industrial Pharmaceutical Crystallization
Processes Using Acoustic Emission in Pure and Impure Media, International Journal ofpharmaceutics vol 439(1-2), 2012, 109-119.
12. E. Serris, A. Cameirao, F. Gruy, Monitoring industrial crystallization using acoustic
emission. In Proceedings of the 32nd European Conference on Acoustic Emission Testing
Prague, Prague, Czech Republic, 7–9 September 2016.
13. A. Cameirao, E. Serris, A. Melchuna, J.M. Herri, P. Glenat, Monitoring gas hydrate
formation and transport in a flow loop with acoustic emission, Journal of Natural Gas
Science and Engineering vol 55, 2018, 331-336
14. C. Ruiz-Carcel, A. Starr, E. Nsugbe, Estimation of powder mass flow rate in screw feeder
using acoustic emission, Powder technology, 336, 2018, 122-130
15. M.G. Droubi, R.L. Reuben, J.I. Steel, Flow noise identification using acoustic emission
energy decomposition for sand monitoring in flow pipeline, Applied acoustics, 131, 2018,
5-15
16. Y. Hu, L. Wang, X. Huang, X. Qian, L. Gao, Y. Yan, On-line sizing of pneumaticallyconveyed particles through acoustic emission detection and signal analysis, IEEE
transactions on instrumentation and measurement, 64(5), 2015,1100-1109

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AE Measurement during Friction Stir Welding of
Flame-resistant Magnesium Alloy with Simulated
Inclusions

Kazuki Takahashia, Takayuki Shiraiwaa, Manabu Enokia, Kaita Itob, Eitaro Yukutakeca

The University of Tokyo; bNational Institute for Materials Science;cIndustrial Technology Innovation center of Ibaraki Prefecture
ABSTRACTFriction stir welding (FSW) is a solid state joining process. By inserting the tool with the pinat the tip into the material, friction heat is generated and it softens the material. The material
plastically flows around the tool and the specimens are joined. Due to low heat input during
FSW, the grain coarsening is suppressed. It was shown that higher strength at joints can beobtained compared with the conventional welding process in Al alloy and conventional Mg alloy.
Recently, in order to reduce the environmental impact and improve the transportation efficiencyof train body, flame-resistant Mg alloy was developed. In order to make railway vehicles, FSW isapplicable to the joining of long plate of flame-resistant Mg alloy. To monitor the FSW process offlame-resistant Mg alloy, wireless AE streaming system was developed in our group. Although
AE signals during FSW process was considered to be related to the defects in the welding zone,
the relationship between the AE events and the defects is not clear. In this study, we introduced
the defects by mixing the simulated inclusions and measured the AE signals by wireless AE
streaming system. In the case of specimens with the simulated inclusions, higher AE RMS
voltage was obtained compared with that without the simulated inclusions.
Keywords: Friction Stir Welding; Acoustic Emission; Flame-resistant Mg Alloy; Joining
Technology; Process Monitoring
1. INTRODUCTION
Friction stir welding (FSW) is a solid-state joining process [1]. This process takes place
below the melting temperature of the material, and the grain coarsening and tensile strength
reduction of the joint can be suppressed as compared with the conventional welding process.
However, defect formation depends on the welding parameters such as rotation speed, traverse
speed, tool geometry and so on. It is expected to apply FSW to the calcium-added flameresistant magnesium alloy in order to reduce weight of railway vehicles, but it is more difficult tojoin the flame-resistant magnesium alloy by FSW than aluminum alloys and conventional
magnesium alloys [2]. In order to evaluate the reliability of the joint and to obtain the appropriate
welding parameters, it is necessary to detect the defect formation during FSW of the flameresistant magnesium alloy. About 25 m FSW is needed to assemble railway vehicles but
conventional wired AE monitoring needs dozens of AE sensors for this process. It is notpractical way to use many AE sensors with complicated wire. Moreover, it is not allowed toattach the AE sensors directly on the material. In our previous studies, we developed thewireless continuous AE measurement device moving with the FSW tool in order to measure theAE signals generated during FSW of the flame-resistant magnesium alloy [3]. This device madeit possible to measure the AE signals during FSW of long plate samples with a small number of
AE sensors as compared with conventional AE measurement method. In addition to this, AE
signals during FSW of flame-resistant Mg alloy were measured by wireless CWM and tensileth

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test and defects observation were conducted [4]. However, the relationship between AE signals
and defects in the welding zone were not clear. In this study, simulated inclusions wereintroduced in the AZ31 Mg alloy and FSW experiment was conducted.

2. EXPERIMENTAL PROCEDURE
2.1 FSW experiment
The material used in this study was AZ31 (Al 3 %, Zn 1 %) magnesium alloy sheets. As
shown in Figure 1(a), the dimensions of the plates were 70 mm width, 200 mm length and 2 mm
thickness. The center on the long side of two plates was welded along the butt line. The tool had
a 20 mm diameter shoulder and screwed pin. The tool pin had 1.9 mm height and 6 mmdiameter. During joining, the tool rotated clockwise with tilt angle of 3° to promote material flow.
Alumina having an average particle size of 500 m (JIS R6001, F36)was used as simulated
inclusions in order to investigate the relationship between the inclusions in the material and AE
signals during FSW process. It was introduced in the butt line of the plates with changing theamount and the area. Figure 1(b) shows the simulated inclusions and the cross section of plate.
In this FSW experiment, the tool rotation rate was set at 1000 rpm and the welding speed
was set at 10 mm/s.

(b)

3° FSW tool
movinginsertSimulated inclusions
removeWelding part
Ch.4

Ch.3

Ch.2

120 mm
Ch.1

70 mm

70 mm

200 mmFigure 1: (a)The dimensions of the specimen and the position of AE sensors, (b)AZ31 plate withsimulated inclusions
2.2 AE measurement
The position of AE sensors is shown in Figure 1(a). Four of AE sensors (type AE254SMH177, Fuji Ceramics Corporation, Japan) were attached on the jig moving with tool.
Movable AE sensor jig had a mechanism for canceling the advance angle of tool and thepistons making the contact between AE sensors and specimen surface stronger. The
neodymium magnets were attached on the AE sensors and back plate in order to strengthen
adhesion. Figure 2 shows the wireless CWM (Continuous Wave Memory) system.
STEMlab125-14 (Red Pitaya) was used as a wireless CWM device [4]. STEMlab125-14 was

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powered by Li-ion mobile battery in order to isolate the wireless CWM system electrically and to
decrease the influence of electrical noise.
The waveforms were recorded to hard disc drives by the wireless CWM with about 4 MHz(divide 125 MHz by 32) of sampling frequency, 14-bit resolution and ±1 V range. A high passfilter with 50 kHz of cut-off frequency was applied by internal software of CWM system to reducenoise caused by vibration. AE events were detected with 10 mV threshold voltage.

Li-ion Battery

STEMlab 125-14
STEMlab125-14Li-ionBattery

AE sensors

STEMlab125-14AE sensors
Figure 2: Wireless CWM system
3. RESULTS
Figure 3 shows RMS voltage during FSW process. Since FSW process is not considered to
be a steady state immediately after start and just before termination, RMS voltage of this partwas ignored. In the case that simulated inclusions were introduced at the position from 0 to 60
mm of joining line, high RMS voltage was obtained in the first half of FSW (Figure 3(a)). In thecase that simulated inclusions were introduced at the position from 60 to 120 mm, high RMS
voltage was obtained in the latter half of FSW (Figure 3(b)). On the other hand, lower RMS
voltage was obtained in the case that simulated inclusions were not introduced (Figure 3(c)).
As shown in Figure 3, RMS voltage increased while joining the area where simulated
inclusions were introduced. Therefore, it is supposed that inclusions in the material are one of
the causes of AE during FSW of flame-resistant magnesium alloy.
4. CONCLUSIONSIn this study, simulated inclusions were introduced in AZ31. AE signals were measured withwireless CWM during FSW process. The relationship between AE signals and simulated
inclusions were studied. It is considered that simulated inclusions introduced in the material
induced higher RMS voltage.

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(a)

RMS Voltage / mV

10
8
6
4

2
0

0

RMS Voltage / mV

2

4

2

4

2

4

(b)

10

6

8

10

12

6

8

10

12

6

8

10

12

Time / s

8
6
4
2
0

0

10

RMS Voltage / mV

Ch. 1
Ch. 2

(c)

Time / s

8
6
4
2
0

0

Time / sFigure 3: RMS voltage during FSW process (a) simulated inclusions introduced from 0 to 60 mm,(b) simulated inclusions introduced from 60 to 120 mm (c) no simulated inclusions

5. REFERENCE LINKING[1] W. M. Thomas et al., (1991). “Investigate of weld defects in similar and dissimilar friction stir
welded joints of aluminum alloys of AA7075 and AA6061 by X-ray CT radiography”,
International Patent Application PCT/GB92/02203 and GB Patent Application 9125978.8,
UK Patent Office, London, December 6.
[2] Akiyama et al., (2000). “Development of flame-resistant magnesium alloy” Materia, Vol.39,
pp.72-74.
[3] M. Katayama, K. Ito, H. Fujii and M. Enoki, (2017). “Wireless AE measurement for FSW oflong size steel plate”, CAMP-ISIJ, Vol.30, pp.306.
[4] K. Ito and M. Enoki, (2017). “Development of independently operated sensor node with
continuous wireless transmission of AE waveform”, Proceedings of the 21st Acoustic
Emission Conference, pp.113-116.

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Structural Health Monitoring of Crack Initiation and Growth inMooring Chains using FEA Methods for Acoustic Emission
CharacterisationÁngela Anguloa,b, Han Yanga,b, Jialin Tanga,b, Ali Khadimallaha and Slim Souaa,
a TWI Ltd, Condition and Structural Health Monitoring, Integrity Management Group.

Granta Park, Great Abington, Cambridge, CB21 6AL, United Kingdom;b Brunel University London, Kingston Lane, Uxbridge, Middlesex, UB8 3PH, United

Kingdom;

ABSTRACT
Mooring chains for floating offshore installations, typically designed for a 25 year service life, areloaded in fatigue in seawater environments. It is crucial that the life of such structures is
extended by regularly replacing old components and performing periodic in-service inspections.
Although traditional methods can be successful at detecting and assessing fatigue cracks, thehazardous nature of operation calls for remote techniques that can be applied continuously toidentify damage initiation and progress. Appropriate replacement plans must enhance current
strategies by implementing real-time data retrofit.
Acoustic Emission (AE) has been shown to be a promising technique to detect cracks andfatigue damage in mooring chains. The present paper describes an AE-based methodology
combined with Finite Element Analysis (FEA) for crack initiation and growth for Structural Health
Monitoring (SHM) of mooring chains.
Keywords: acoustic emission, mooring chains, structural health monitoring, cracking, finite
element analysis.
1. INTRODUCTION
One of the major problems in the design of offshore equipment is fatigue damage accumulation.
Although this topic has been extensively studied in the literature, theoretically, numerically andexperimentally [1] [2] [3] [4] [5] [6] [7], the available inspection and monitoring technologies
developed to date have not been able to fully overcome the severe environmental challenges
associated with offshore service activities. Furthermore, in addition to the structural challenges
that onshore structures experience, offshore assets withstand harsh marine environments as aresult of severe storms, highly corroding sea water, sea quakes and cyclic wave loading [8] [9].
Due to the increasing demand for structural retrofit into conventional inspection strategies,
Structural Health Monitoring (SHM) is of interest to a wide range of industries. AE is a nondestructive monitoring technique that is widely used. One of the benefits of its application is thereal-time assessment of the structural condition of industrial assets. The primary goal of thisstudy is to investigate the applicability of AE approach for crack initiation and propagation
detection and localization on a mooring chain [10]. The Finite Element Analysis (FEA) main
objective is to simulate the dynamic response from an incremental crack growth. This isachieved by developing a FEA method for the implementation in a non-symmetric geometry.
Previous modelling work [10] has shown indication of the active damage regions in loaded
mooring chains. Because of the inherent uncertainties present in any SHM technique, any
monitoring technology should be used as part of a full mooring chain structural integrity
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assessment. In addition to the continuous data output, a risk-based integrity management
strategy may also be included based on data from periodic inspections, numerical modellingshowing stress distributions or crack propagation and historic and current operational
conditions.
Research backgroundPrevious research by the authors [10] [11], show the results of a FEA model that simulates
instantaneous crack growth in a mooring chain link. The method used was successfully
implemented in 2D in a long plate for a range of crack depths, all growing instantaneously fromthe un-cracked state. It was also successfully implemented in a 3D mooring chain link.
Additional work presented in [12], showed a new method to simulate incremental crack growthusing cohesive zones. For such model, many parameters are required which are not readily
available for materials. The method is complex and presents a more suitable approach topredicting crack growth. The generation of realistic AE signals was achieved, whereas theprediction of realistic crack growth was not possible. As a result, the method was notrecommended for this application due its complexity. It was therefore recommended to
implement a pre-defined incremental crack growth approach and to analyze the dynamic
response of the AE generated.
Preliminary methods
Preliminary methods [10] [12] consisted of simulating the crack growth using known andincremental changes in crack length. This is a straightforward method to apply for symmetricgeometry as boundary conditions can be removed. However, this method can be complex for
non-symmetric geometries, as the seam used to define the crack for the instantaneous crack
growth cannot be altered over time for incremental crack propagation. Similarly, ties cannot be
adjusted over time.
Preliminary method one: defines the contact between un-cracked surfaces and incrementally
deletes the contact to grow the crack. The results were not satisfactory as the contact definition
in Explicit (using ABAQUS) returned numerical errors generating undesired waves.
Preliminary method two: defines a blunt crack that is two elements wide (symmetric) anddeletes elements to grow the crack. The results proved the approach can be very complex to
implement in Explicit, involving VUMAT subroutine. It was therefore implemented using theStandard method.
2. APPROACH
Method description
The selected approach corresponds to the incremental crack growth method. The methodology
artificially deletes elements by reducing their stiffness (Young’s Modulus) to ~0. This method
simulates the crack growth and is controlled by defining temperature-dependent material
properties. The crack grows by assigning an instantaneous temperature change with each crack
growth increment (i.e. the temperature is used as an arbitrary variable to change the stiffness
with time).
Model description
2D and 3D Finite Element (FE) linear elastic modelling have been implemented using ABAQUS
software. Both studies are carried out on a carbon steel material (see Table 1).

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Table 1 Material properties

Temperature
(°C)
0
20

Young's Modulus
(GPa)
~0
207

Density(Kg/m3)7830
7830

Poisson's Ratio
0.3
0.3

For the 2D model, where 2D linear elements are used, the dimensions of the plate are 0.1m ×
0.1m. A 500N tensile force is applied to both ends through reference point coupling in the U1
direction (indicated by the red arrow, Figure 1)

Figure 1 Model description of the 2D Square plate

The reference points are fixed in U2, U3 (displacements along y and z, respectively), UR1, UR2
and UR3 (rotations about x, y and z, respectively). The central-base point is fixed in U2 toprevent rigid body motion of the model. The mesh size of 1 mm and a time step of 80 ns wereused. The output node is situated halfway between the crack and the end face.
The crack growth method can be described as follows: four steps are set up to simulate thecrack opening, the load is applied in the ‘initial crack’ step smoothly and then held constantly
during growth. Such procedure avoids generating waves. The load function according to thesmooth step method defined in ABAQUS software is as follows.
;
Where a is the crack length and t is time. In the initial step, the temperature of the whole model
was 20 °C. During the initial crack step, the temperature of the elements which belong to the‘initial crack’ region was changed to 0 °C. This resulted in a Young’s modulus value of ~0,
therefore simulating the crack opening. Elastic waves (acoustic emissions) are generated as theresult of this sudden release of energy.
In order to study the influence of different crack sizes on the generated acoustic emissions,
three more steps after the ‘initial crack’ step were added. The sizes of the three cracks are:
;

;

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;

To further validate the modelling method, a 3D model is implemented. The 3D and 2D models
were based on the same crack length and width (3D model thickness was 5mm), as well as thesame crack propagation method and parameters. Linear hexahedron elements were used.
3. RESULTS
The manifestation of AE is represented by vertical displacements. The recorded waveform at
the output point is shown in Figure 2. The top-left and top-right plots of Figure 2 are thedisplacements from the output points of the 2D and 3D models, respectively. From 0s to 0.01s,
the load is applied smoothly during the ‘initial crack’ step. The crack growth step 1 ( ), crack
growth step 2 ( ) and crack growth step 3 ( ) occur from 0.01s to 0.02s, 0.02s to 0.03s and
0.03s to 0.04s, respectively.

Figure 2 Time and frequency domain of displacements in the 2D model (left) in 3D model (right)

In the time domain, similarities can be observed between the 2D and 3D models. The average
amplitudes of the waves increased with the crack length increase for both models in the time
domain. However, during crack growth, the wave amplitude obtained from the 2D model was
two orders of magnitude lower than the corresponding wave of the 3D model, situated between
2×10-7 and 4×10-7.
In order to investigate the signatures of the crack growth in its frequency domain, Fouriertransform was used during data processing. For both 2D and 3D models, three distinct peaks
for each crack release can be seen (increasing with growing crack in the 2D model) and thefrequency decreased as the crack length increased. For the 3D model, the largest peak
amplitude corresponds to the second crack step.
4. SUMMARY
The current method consisted of reducing the elements stiffness at the crack tip region tosimulate the crack growth. The results show that the average amplitude of the AE waves

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increased with the crack growth. It can also be seen that the frequency of the AE signals
generated from the crack propagation decreased as the crack length increased.
Additional development and sensitivity analysis are required for the current method. The
influence of different parameters used in the modelling such as crack growth release time, crack
growth size and initial crack size will be further investigated. Cyclic loading response will also bemodelled. Furthermore, it is very important to validate the FE model with experiment results. AE
monitoring of crack opening experiments which are similar to the numerical modelling cases will
be conducted to get more convinced data for the application of the current method.
5. REFERENCES[1].

H. Agerskov and N. Pedersen, "Fatigue life of offshore steel structures under stochastic
loading," Journal of Structural Engineering, vol. 118, no. 8, pp. 2101-2117, 1992.
[2]. H. Alawi, A. Ragab and M. Shaban, "Corrosion fatigue crack growth of steels in various
environments," Journal of Engineering Materials and Technology (ASME), vol. 111, no. 1,
pp. 40-45, 1989.
[3]. K. Komai, M. Noguchi and H. Okamoto, "Growth characteristics of surface fatigue cracksof high-tensile strength steel in synthetic seawater," JSME International Journal, vol. 31,
no. 3, pp. 613-618, 1988.
[4]. K. Komai, K. Minoshima, S. Kinoshita and G. Kim, "Corrosion fatigue crack initiation ofhigh-tensile-strength steels in synthetic seawater," JSME International Journal, vol. 31, no.
3, pp. 606-612, 1988.
[5]. J. S. Monsalve-Giraldo, C. M. S. Dantas and L. V. S. Sagrilo, "Probabilistic fatigue
analysis of marine structures using the univariate dimension-reduction method," Marine
Structures, vol. 50, pp. 189-204, 2016.
[6]. Y. Low, "A variance reduction technique for long-term fatigue analysis of offshore
structures using Monte Carlo simulation," Engineering Structures, vol. 128, pp. 283-295,
2016.
[7]. Y.-H. Zhang and M. Dore, "Fatigue crack growth assessment using BS 7910:2013 Background andrecommended developments," International Journal of Pressure Vessels
and Piping, vol. 168, pp. 79-86, 2018.
[8]. T. Moan, "Life cycle structural integrity management of offshore structures," Structure andInfrastructure Engineering, vol. 14, no. 7, pp. 911-927, 2018.
[9]. T. Thorpe, P. Scott, A. Rance and D. Silverster, "Corrosion fatigue of BS 4360:50D
structural steel in seawater," Internation Journal of Fatigue, vol. 5, no. 3, pp. 123-133,
1983.
[10]. A. Angulo, C. Mares, T. Gan and S. Soua, "Finite element analysis of crack growth forstructural health monitoring of mooring chains using ultrasonic guided waves and acoustic
emission," J. Procedia Structural Integrity, pp. 5, 217–224, 2017.
[11]. Angulo, G. Edwards, T. Gan and S. Soua, "Mooring integrity management: novel
approaches towards in situ monitoring," in In Structural Health Monitoring-Measurement
Methods and Practical Applications, London, Intechopen, 2017, p. Chap. 5.
[12]. Angulo, C. Mares, T. Gan and S. Soua, "Crack Initiation and Growth Acoustic Emission
Analysis with Finite Element Analysis for Structural Health Monitoring of Mooring Chains,"
in Proceedings of the Eleventh International Workshop on Structural Health Monitoring,
Stanford, 2017.

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Characterization of Microdamage of Flexible Solar Cell under
Fatigue Load using AE and LT Techniques
Takahiro MATSUEDAa*, Shuichi WAKAYAMAa, Kosuke NAKAHARAb,
Akihiro TAKANObaTokyo Metropolitan University; bF-WAVE Company, Limited

ABSTRACT
Flexible solar cells in service are subjected to external load such as thermal expansion
mismatch between solar cells and the base. It is considered that microdamage are initiated andaccumulated due to those external loads. Some mode of microdamages causing shunt resultsin the electrical degradation of flexible solar cells. In our previous study, it was revealed thatelectrical degradation of solar cell is caused by microdamage parallel to the loading direction
due to the buckling around through-hole under static tensile loading. The purpose of the present
study is to reveal the mechanisms of microdamage initiation and accumulation, and theircontribution to the electrical degradation during fatigue fracture.In the present study, cyclic
tensile tests performed with the 0.8% and 1.2% maximum strains. Microdamages initiation weredetected by the acoustic emission (AE) techniques. The electrical degradation of solar cell was
evaluated by monitoring electrical power calculated from Current-Voltage (I-V) curve.
Furthermore, microdamages contributing to the electrical degradation were identified by the
Lock-in thermography (LT). For the cyclic tensile tests, the first AE signal was detected at thethrough-holes. Temperature around hthrough-oles increased and electrical power decreased in
low cycles. Then AE signals resulted by microdamage initiation were detected each cycles andelectrical power was gradually decrease with the increase in the number of cycles until the test
stop. It was then indicated that microdamage both of initiating extremely low cycle and highcycle contributed to the electrical degradation. Consequently, it was suggested that the AE, LT
and I-V curve monitoring enable to characterize the electrical degradation during fatigue fracture.
Keywords: Flexible solar cell, Fatigue, Microdamage, Lock-in thermography
1. INTRODUCTION
Since flexible solar cells have excellent flexibility and lightness, they have been widely used
as one of the sustainable energy systems. Flexible solar cells in service are subjected toexternal load such as thermal expansion mismatch between solar cells and the base. It isconsidered that microdamage are initiated and accumulated by those external loads. Some
mode of microdamages leads the electrical degradation of flexible solar cells [1-5]. In order tounderstand the mechanism of electrical performance degradation of flexible solar cell, these twoprocesses should be clarified.
In our previous study, it was revealed that electrical degradation of solar cell is caused by
microdamage parallel to the loading direction due to the buckling around through-hole under
static tensile loading [6]. On the other hand, the electrical degradation behavior under cyclicloading has been unclear yet and there are only a few studies [7] in spite of flexible solar cell inservice may be subjected to fatigue load due to mismatch of thermal expansion. The purpose of
the present study is to detect the microdamages, sensitively and identify the microdamages
contributing to the electrical performance degradation during fatigue fracture. Cyclic tensile tests
were carried out for this purpose. Microdamage during the cyclic tensile tests were detected and9th International Conference on Acoustic Emission

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characterized using acoustic emission (AE), lock-in thermography (LT) technique [8-10]and
monitoring electrical performance.
2. SPECIMEN AND EXPERIMENTAL PROCEDURE
2.1 SPECIMEN PREPARATIONIn the present study, the commercially available a-Si/a-SiGe flexible solar cell modules, FWAVE were employed as specimens. Figure 1 shows the illustration of the flexible solar module.
Specimen in this study has laminated structure which consists of 7 layers of thin films;
PE/ITO/a-Si/a-SiGe/metal electrode/film substrate/back electrode. The flexible solar modules
are separated by patterning lines. There are two types of through-holes in this flexible solar
module. The one provides the electrical connections between back electrode and ITO and theother provides the electrical connections between metal electrode and back electrode toconnect unit cells in series [11]. The flexible solar module was cut into the specimen of one unit
cell with the dimension of 207 × 35 × 0.17 mm. The two conductive tapes were attached on theback electrode in order to monitor electrical power generation of a unit cell.

Figure 1: Schematic illustration of the flexible solar cell used in present study.
2.2 Experimental Procedure
2.2.1 Test Condition of Cyclic Tensile Fatigue Test
Figure 2 shows the schematic illustration of tensile fatigue testing system. The tensile fatigue
tests of the flexible solar cells were performed with the gage length of 200mm. The mechanicalcyclic load was applied by fatigue testing machine (SAGINOMIYA SEISAKUSHO, INC, FT-101600). Maximum strains were 0.8% and 1.2%. The stress ratio was 0.1.
2.2.2 Measurement Conditions of AE Signals and electrical performance
In order to detect microdamage caused by fatigue load, AE technique was used during test.
Four resonance model AE sensors (PAC, PICO; resonant frequency 400kHz) were attached ona through-hole, a patterning line, a smooth surface and an edge of specimen by elastic bondand these sensors were called as Ch.1 ~ Ch.4, respectively. The AE signal was amplified by
40dB and the threshold level was 20 μV. The band pass filter was selected at a range from 20
to 1200 kHz. The AE signals were recorded and proceeded by the AE analyzer (Vallen SystemGmbH, AMSYS-5).
For monitoring electrical performance, the specimen was irradiated by solar simulator (Peccell
technology, PEC-L01) during the test. Current and voltage generated by flexible solar cell were
measured by the source meter (Keithley Instruments Inc., 2400).

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Figure.2: Schematic illustration of cyclic tensile fatigue test system.
2.2.3 Measurement Conditions of LT
For the detection of the shunts which contributed electrical performance degradation in a
specimen, fatigue test was suspended and the increase in temperature modulation resulted
from shunt was detected using LT technique. IR thermal images were recorded during themonotonic tensile tests of the flexible solar cells. The damage contributing to the electricalperformance degradation was excited by the periodical input bias. The temperature at the local
area around damage was increased due to the shunt. The excited signals were discriminatedfrom back ground signals by the digital lock-in processing. For the temperature history at eachpixel, the frequency component synchronized with the input bias was extracted through the fast
Fourier transform (FFT). The LT image was obtained by repeating the same processes for all
pixels. It is worth noting that the damage contributing to the electrical performance degradation
can be distinguished from the others by LT technique in principle. The sinusoidal voltage withthe amplitude of 2 V and the frequency 1 Hz was applied to the unit cell, and then current andvoltage were measured by the source meter.
3. RESULTS AND DISCUSSION
3.1 Fatigue test with the 0.8% maximum strain
For identification of microdamage contributing to electrical performance degradation of theflexible solar cell under fatigue load, it is necessary to detect and characterize microdamages.
Figure 3 shows the Load, cumulative AE energy, the maximum electrical power normalized by
one before test, Pmax is plotted as a function of the cycle and LT images resulted from 0.8%
maximum strain fatigue test.
As described Fig. 3, the first AE signal was detected at the through-hole. And then the
cumulative AE energies at the other sensors were increased in order of patterning line, the edgeof specimen and the smooth surface. Increase of AE energy was temporary stopped until 1700
cycles. And then AE energy at through-hole and smooth surface were rapidly increased. AE
energy at through-hole and smooth surface were increased each cycle until test stop.

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Pmax had been decreasing from 2000 cycles to test stop. It is indicated that the flexible solar
cell should be used to consider the electrical performance degradation in high cycle fatigue
under 0.8% cyclic strain.
As shown in LT images, until 1000 cycles, the increase in temperature was not observed. The
temperature around several through-holes increased continuously from 2000 cycles and thenthe region of temperature increasing had been expanding as the increase in cycles until test
stop.

Figure 3: Cumulative AE energy, Pmax and LT images of 0.8% strain fatigue test.
3.2 Fatigue test with the 1.2% maximum strain
Figure 4 shows the Load, cumulative AE energy, Pmax is plotted as a function of the cycle andLT images resulted from 1.2% maximum strain fatigue test. As described in the figure, the firstAE signal was detected at the through-hole and the cumulative AE energy at through-hole was
rapidly increased immediately similarly to 0.8 % maximum strain fatigue test. And then the
cumulative AE energy at the other sensors were also rapidly increased in order of the smoothsurface, patterning line and the edge of specimen, but AE generation around the through-hole
was significantly active compared to 0.8 % maximum strain fatigue test. It was indicated thatmicrodamages initiated around through-holes, intensively. Cumulative AE energies at eachchannel were rapidly increasing at extremely low cycles and then generally continue to be theconstant value.
Pmax had been decreasing until 200 cycles and Pmax at 200 cycles was reduced to 30% before
test. And then Pmax was remaining flat to the end of this test. It is indicated that the performancedegradation of the flexible solar cell under 1.2% cyclic strains was rapidly proceeding in
extremely low cycles.
As shown in LT images, the temperature around several through-holes increased
continuously from 100 cycles.

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Figure 4: Cumulative AE energy, Pmax and LT images of 1.2% strain fatigue test.
3.3 Contribution of crack closure to performance degradation process
Increase in temperature under fatigue load was observed in bands. On the other hand, theincrease in the temperature around a through-hole was only observed under monotonic tensile
load [6]. Crack tip under fatigue load was subjected crack closure. This crack closure initiatedAE signals and shunts which contributing the increase in temperature of specimen surface
under fatigue load.

4. Conclusions
In present study, the cyclic tensile fatigue test of the flexible solar cell was carried out in order
to identify the microdamage contributing to the electrical degradation during fatigue fracture andfollowing conclusions were obtained.
1. The microdamages initiated under cyclic tensile load were well detected using AE technique.
In the both 0.8 % and 1.2 % maximum strain fatigue tests, the first microdamage initiatedaround the through-hole was earlier than the others.
2. It was revealed that the increase in temperature around the through-hole was earlier than theothers under cyclic tensile load using LT technique. This tendency was coincided on that of AE
detection.
3. Crack closure at crack tip under fatigue load contributed to performance degradation.

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5.

References[1]IEC 61215, (2005).
“Crystalline Silicon Terrestrial Photovoltaic Modules – Design
Qualification and Type Approval: Edition 2”, IEC.
[2] Wohlgemuth, J. H., (2008). “The effect of cell thickness on module reliability”, Proceeding of
33rd IEEE Photovoltaic Specialists Conference, doi: 10.1109/PVSC.2008.4922770.
[3] Pingel, S., Zemen, Y., Geipel, T. and Berghold, J., (2009). “Mechanical stability of solar cells
within solar panels” Proceeding 24th European Photovoltaic Solar Energy Conference andExhibition, pp.3459-3463.
[4]Köntges, M., Kunze, I., Kajari-Schröder, S., Breitenmoser, X. and Bjørneklett, B., (2011). “Therisk of power loss in crystalline silicon based photovoltaic modules due to micro-cracks”, Solar
Energy Materials & Solar Cells. Vol. 95, issue 4, pp. 1131–1137.
[5]Jones, R., Johnson, R., Jordan, W., Wagner, S., Yang, J., and Guha, S., (2002). “Effects ofmechanical strain on the performance of amorphous silicon triple-junction solar cells”,
Conference record of 29th IEEE photovoltaic conference, pp.1214-1217.
[6]Tazawa, H., Sakai, T., and Wakayama, S., (2013). “Characterization of damage in a-Si/aSiGe flexible solar cells under mechanical strain by AE technique and lock-in thermography”,
Proceeding 28th European Photovoltaic Solar Energy Conference and Exhibition, 3CV.1.33, CDROM, 4 pages.
[7]Jason Maung, K., Thomas Hahn, H., Ju, Y.S., (2010). “Multifunctional integration of thin-film
silicon solar cells on carbon-fiber-reinforced epoxy composites”, Solar Energy, Vol. 84, pp. 450–
458.
[8]Huth, S., Breitenstein, O., Huber, A., Dantz, D., Lambert, U. and Altmann, F., (2002). “Lock-in
IR-thermography - A novel tool for material and device characterization”, Solid State
Phenomena, Vol. 82-84, pp.741-746.
[9]Straube, H. J., Wagner, M., Schneider, J., and Breitenstein, O., (2011), “Quantitativeevaluation of loss mechanisms in thin film solar cells using lock-in thermography”, Journal ofApplied Physics, Vol. 110, doi:10.1063/1.3651397.
[10]Breitenstein, O., Rakotoniaina, J. P., Al Rifai, M. H. and Werner, M., (2004). “Shunt Types in
Crystalline Silicon Solar Cells”, Progress in Photovoltaic: Research and Applications, Vol. 12, pp.
529-538.
[11] Takano, A., Kamoshita, T.,(2004), “Light-Weight and Large-Area Solar Cell Production
Technology”, Japanese Journal of Applied Phisics, Vol. 43, Part 1, No. 12.

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Application of Non-Contact Laser Impact with Acoustic
Emission Analysis and Tomography Technique for Damage
Detection in Concrete
Tomoki Shiotani1, Katsufumi Hashimoto1, Masaharu Nishikino2
and Katsuhiro Mikami2
1

Dept. Civil & Earth Resources Engineering, Kyoto University, Kyoto, 615-8540, Japan
National Institutes for Quantum and Radiological Science and Technology, Kansai
Photon Science Institute, Kyoto, 619-0215, Japan

2

ABSTRACTTomography techniques have been developed based on acoustic emission and elastic-wave
propagation to visualize the internal defects in concrete. For instance, the tomography results
can reasonably estimate the wave velocity distribution in consideration of P-wave propagation
detours, which are supposed to be altered by the disturbance such as internal defects. As forthe excitation of elastic waves on the surface of targeted structure, it is generally known that
contact time of a steel sphere ball drop depends on the diameter of the ball, and the frequency
component generated by the ball impact is derived by the contact time. In this study, to realize
remote inspection works, the elastic wave is induced onto the concrete surface by the impact
laser system employing high-power pulse, followed by investigating the influence of laser
intensity and spot size. To implement the prompt and reasonable laser remote sensing system
to damage detection in concrete, high speed scanning of laser pulse irradiation is introduced on
the surface of concrete. Amplitude and frequency dependence are analyzed between the raypaths of the propagated elastic waves, calculating P-wave and surface wave propagation timewith amplitude attenuation. The applicability of laser-induced elastic wave for the technique isthus investigated when internal defects of concrete is visualized by means of tomography
technique.
Keywords: acoustic emission, laser impact, concrete, p-wave, surface wave, tomography

1. INTRODUCTION
To induce the elastic wave and the vibration into concrete, the impact laser system is
employed for high-power pulse, which irradiates the concrete surface to generate vibrations thatcan excite elastic waves on the structural surface of interest. Amplitude and frequency
dependence are analyzed among the ray paths of the propagated elastic waves, calculating Pwave and surface wave travel time with amplitude attenuation. According to visualization ofinternal defect in concrete by means of tomography technique, the applicability of laser-induced
elastic wave for the technique is consequently investigated.
In order to determine the velocity distributions by tomography, the following analytical steps aretaken. The arrival time at each sensor was determined with an Akaike Information Criterion(AIC) picker [2,3]. For the digitized wave record xk of length N, the AIC value is defined as
AIC(k ) = k ´ log{var(x[1, k ])}+ ( N - k - 1) * log{var(x[k + 1, N ])}9th International Conference on Acoustic Emission

(1)

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where var(x[1, k]) indicates the variance between x1 and xk, and var(x[k, N]) is the variance between
xk and xN.

The point where the AIC value is minimized with the least-square method corresponds to the
optimal separation point of two stationary time series, and thus the arrival time is reasonably
determined as the phase onset by the AIC picker. This is because noise could be represented
by lower AIC values, whereas wave signals generally show higher AIC values. From thedetermined arrival time, the velocity of the elastic wave is calculated. The observed propagation
time Tobs is obtained by Eq. (2).

Tobs = To - Ts(2)

where Ts is the time of excitation and To is the arrival time [4].

Figure 1 Slowness for calculation of propagation time
In the elastic-wave tomography algorithm, the reciprocal of the velocity is referred to as the“slowness,” which is given as an initial parameter into each element as shown in Fig. 1. The
theoretical propagation time Tcal is derived from the total of the propagation time calculated by
the slowness and the distance in each element (refer to Eq. (3)). The difference between theobserved propagation time (Tobs) and the theoretical propagation time (Tcal) is obtained by Eq.
(4).
Tcal =

ås *l
j

(3)

j

j

DT = Tobs - Tcal

(4)where lj is the length crossing each element and sj is the slowness of each element.
Then, the slowness in each element is revised in order to reduce the difference between theobserved propagation time and the theoretical propagation time. The amount of the slowness
correction is calculated by Eq. (5). Consequently, the revised slowness is obtained by Eq. (6).
DTi * li1
éêLii
é Ds1 ù êêTi * li 2
DêDs 2úê ú=ê
Liê ! ú ê i!
ê ú ê
ë Dsj û ê
DTi * lij
ê
ê
Li
ë i

å

å l ùú

å

å

å

å

i1

ú
ú
li 2 úiúúúlij úúiûi(5)s¢j = sj + Dsj(6)

where Li is the total distance of wave propagation through the i-element.

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The iteration procedure based on Eq. (5) and Eq. (6) is used to determine the optimal slowness
and eventually the velocity in each element corresponding to the observed propagation times ofmultiple paths over the structure. Thus, the velocity distribution is determined.
In order to determine the ray path more accurately, the ray-trace algorithm is applied, taking
detours of elastic waves due to reflection and diffraction into account. In particular, the arrival
time of each wave is obtained by using a 3D ray-trace algorithm extended from a 2D ray-trace,
which was proposed in the past research [5]. The slowness of each element is corrected fromthe difference between the first travel time observed and that computed in the 3D element,
because the algorithm enables the use of 3D finite elements for meshing the target space.
2. EXPERIMENTAL SETUP
High-speed laser system has been employed as a remote sensing technique to detect defects
inside a concrete specimen [6]. In the proposed measurement setup, irradiated laser pulses
enable to generate vibration and detected by the system, which is compatible to that obtainedby using the conventional hammering method. In this study the laser system is thus introduced
to generate elastic waves which propagate in concrete for the aforementioned tomography
computation in order to visualize defect in concrete.
Beam focus and energy density were controlled from 2.79 J/cm2 to 82.5 J/cm2 at laser
irradiated location on the surface of specimen. Impact energy can be altered by 50/ 150/ 250 mJ
and frequency was set at 0.2 Hz. Laser beam was irradiated on the surface of specimen withfocused and de-focused condition as shown in Fig. 2.
A simply supported concrete specimen was targeted, 300 mm height x 300 mm width x 100
mm thickness with 10 mm thickness styrofoam simulating a defect, embedded at 5 mm (85 mm)
depth. In Fig. 3 the specimen and experimental overview are depicted, where sensor of #1 to #4
are placed on other side of laser irradiation surface to detect through-the-thickness wave,
namely P-wave and surface wave is detected by the sensor #5 set on the same surface of theirradiation. Two types of 30 kHz and 60 kHz resonant frequencys’ AE sensors were arrayed at
almost same positions on the both side of the concrete surfaces. Each distance between thesensors from #1- #4 was 60 mm. A sampling rate of waveform record was set at 10 MHz.
3. RESULTS AND DISCUSSION
Fig. 4 shows typical waveforms detected by AE sensor #2 and #4 respectively. The laser
excitations were made by focused and defocused laser impact of 250 mJ irradiated at A and B.
As shown in Fig. 3, the propagation path from A to #2 is through the concrete with defect andthat from B to #4 is through the intact concrete. P-wave arrival times determined with AIC are

Figure 2 Impact laser irradiation condition

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Figure 3 Experimental overview and condition

noted on each graph in Fig. 4. As the waveforms are very similar and the arrival time is almost
compatible for both cases, the beam focusing condition such as 14 J/cm2 in the case of
defocused and 82.5 J/cm2 in the case of focused (as found in Fig. 2), appears not influence on
the arrival time neither do source identification. Accordingly, attenuation of amplitude and Pwave arrival time delay are confirmed when the defect disturbs the propagation-path, compared
the results with A to #2 path and those with B to #4 path. In addition to the previous excitation
points A and B, another point C is added and P-wave arrival time differences between paths
among A-C and sensor #1-4 are picked up and the velocity distribution is finally obtained asshown in Fig. 5. Note, laser impact of irradiation of 250 mJ energy and focused beam condition
are used for the measurement to implement the tomography analysis.

Figure 4 Detected waveforms with 250mJ laser energy

In the result of wave velocity tomography, it is generally recognized that the lower elastic-wave
velocity appears the more serious deterioration, and styrofoam (simulated defect) embedded
location is thus assumed. As in Fig. 5, the tomography result clearly indicates the width ofdefect; however, as for the depth, the result does not provide the required graphic resolution. In
addition, the bottom area gives low velocity (2500 to 3000 m/sec), instead of intact condition.
Those errors might be obtained due to poor ray path density than of other areas, resulting inless information of propagation paths into the computation process such as P-wave arrival timeand so for the velocity. As for the surface wave, Fig. 6 shows the waveforms and FFTspectrums at sensor #5 when the excitation is generated at B with 250mJ energy. The results of
30kHz and 60kHz resonant frequency detected at almost the same location are demonstrated

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Figure 5 Wave path and velocity distribution

as in Fig 6a and b, respectively. Although the prominent peak frequencies are different
depending on the sensors’ frequency response, arrival times for the both cases appear
compatible. The time-frequency spectrums with wavelet transform are shown in Fig. 7. It is
obvious that times showing frequency peak are different in the specific frequency adapted. The
arrival time of the peak for about 25 kHz, e.g., is about 160-170 µs resulting in 900 m/s of wave
velocity having wavelength of 3.6 cm. It is reported that the peculiar frequencys’ dispersion i.e.,
the frequency providing different wave velocity to others suggests the existence of anomaly
inside of materials of interest, and therefore the internal damage might be characterized by thesurface wave dispersion. [7][8].

Figure 6 Waveforms and FFT spectrum at No.5 when irradiated at B

4. CONCLUSION
Impact laser system was introduced for high-power pulse, which irradiates the concretesurface to generate vibrations. Non-contact elastic wave excitations by acoustic emission due tothe laser impact was induced and wave velocity tomography was applied for detecting defects inconcrete. Amplitude and P-wave arrival time were analyzed between the ray paths of the
propagated elastic waves through concrete w/wo simulated defect. Since laser-induced elastic
wave for the technique was sufficiently applicable to AE excitation, leading to induce elastic
waves in concrete, visualization of internal defect by means of tomography technique was
successfully conducted. In addition, it was inferred that internal damage might be suggested by
the dispersive behavior of surface wave, implying the potential of surface wave tomography

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Figure 7 Time-Frequency spectrum with wavelet transformenabling visualization of the internal damage based on one-side access monitoring using laser
measurement techniques.

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Seismic Tomography for Existing Concrete Structures”, Proceedings of Second International
Operational Analysis Conference, Vol. 2, pp. 595-600.
[2] H. Akaike, (1974). “Markovian representation of stochastic processes and its application
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[3] H. Zhang, C. Thurber, C. Rowe, (2003). “Automatic P-Wave Arrival Detection andPicking with Multiscale Wavelet Analysis for Single-Component Recordings”, Bulletinof the Seismological Society of America, Vol. 93, No. 5, pp. 1904-1912.
[4] S. Osawa, T. Shiotani, H. Kitora, Y. Momiyama, (2014). “Damage Visualizationof Imperfectly-Grouted Sheath in PC Structures”, 31st Conference of the European Working
Group on Acoustic Emission”, DGZfP, Berlin, Germany,
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[5] K. Sassa, Y. Ashida, T. Kozawa M. Yamada, (1989). “Improvement in the Accuracyof Seismic Tomography by use of an Effective Ray-Tracing Algorithm”, MMIJ/IMM
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[6] S. Kurahashi, K. Mikami, T. Kitamura, N. Hasegawa, H. Okada, S. Kondo, M. Nishikino,
T. Kawachi, Y. Shimada, (2018). “Demonstration of 25-Hz-inspectionspeed laser remotesensing for internal concrete defects”, Journal of Applied Remote Sensing, Vol. 12,
No. 1, doi: 10.1117/1.JRS.12.015009.
[7] Wen-Hsiang Tsai, Yu-Ting Liu, Yiching Lin, Yu-Feng Lin (2014), “Detecting the Depth
of Concrete Deterioration Using Rayleigh Wave Dispersion Based on Time-Frequency
Analysis”, Proceeding of 11th European Conference on Non-Destructive Testing(ECNDT2014), https://www.ndt.net/events/ECNDT2014/app/content/Paper/411_Lin_Rev1.pdf[8] N. Okude, T. Nishida, T. Shiotani, (2016). “A Basic Study of Elastic-Wave Based
Inspection technique for Evaluating Rebar-Concrete Interface Conditions”, Proceedingsof IAES-23, IIIAE2016 Kyoto and ICAE-8, pp. 551-554.

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Damage Quantification of RC decks by Means of RainInduced AE Excitations and Source Location Bias
Tomoki Shiotani1, Katsufumi Hashimoto1, Kazuo Watabe2, 3
and Hidefumi Takamine2, 3
1 Dept. Civil & Earth Resources Engineering, Kyoto University, Kyoto, 615-8540, Japan
2 Research & Development Center, Toshiba Corp., Kawasaki, 212-8582, Japan

3 NMEMS Technology Research Organization, Chiyoda-ku, Tokyo, 101-0026, Japan

ABSTRACT
It is very important nowadays to establish economic and appropriate management systems forageing concrete infrastructures, in order to guarantee the performance and safety withidentifying severe deterioration through their service life. As it is imperative for the damage
evaluation of infrastructures to establish an efficient non-destructive testing (NDT), an acoustic
emission (AE) tomography technique has been developed. The AE tomography estimates wave
velocity distribution, which is supposed to be decreased as the damage progresses, inside thereinforced concrete. The AE tomography combines an iterative AE source location algorithmwith travel-time tomography to produce a visualization of the elastic wave velocity. However,since the computation for the elastic wave ray-trace algorithm considering all potential detoursof elastic waves takes up much time, and in case that only a few AE signals are detected, AE
tomography technique does not always work efficiently. In this paper, AE signals induced byrain droplets on surface of reinforced concrete (RC) deck are utilized as elastic waves’
excitations, and wave velocity and attenuation tomography assuming linear ray paths are
performed in conjunction with AE source locations. Accordingly, in consideration of AE source
location bias caused by sensor array such as sensor distance and coordination, rain-induced
elastic waves lead to thousands of AE events after in-situ measurement for a few minutes.
Consequently, the tomography results show accurate and time-saving analysis to detect thecracked area and quantify the damage in the RC deck.
Keywords: acoustic emission, rain, RC deck, crack, tomography, source location
1. INTRODUCTION
Utilizing AE monitoring, an efficient method for inspection of RC bridge decks is developed byusing rain-induced AE signals, although the rain signals are considered as noise and removed
in terms of AE signal detections from RC structures in service. When raindrops hit the road,elastic waves are generated on the surface and travel through the deck to AE sensors attachedon the undersurface of the deck. Since it is assumed that raindrops fall uniformly over the entire
road top surface, the AE source locations should show a uniform distribution where the deck is
not damaged. On the other hand, the source distribution in cracked areas appears to be sparseor vacant and estimated density of the elastic wave sources by location analysis is expected toreveal the existence of cracks inside. Also, the computation technique of source location is
introduced in order to provide an appropriate diagnosis system for damage quantification of RC
decks by means of the rain-induced AE excitations.
The present authors have studied AE tomography techniques based on elastic-waves tovisualize the internal defects in concrete. With AE tomography, it can reasonably be assumed
that lower elastic-wave velocity and higher attenuation rate correspond to heavier deterioration.

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The AE tomography estimates wave velocity and attenuation rate distribution which is supposed
to be decreased as the damage progresses in the targeted structure. It combines an iterative
AE source location algorithm with travel-time tomography to produce a visualization of thedamage property. However, AE source location results based on the conventional AE source
location algorithm are biased for most events, because of the sensor arrangements. The source
location bias gives a certain error to the tomography results as a function of AE source
numbers, locations and elastic wave ray-path density. This study investigates and provides anappropriate diagnosis system for damage quantification of RC decks by means of the raininduced AE excitations and the tomography technique.
2. ATTENUATION AND WAVE VELOCITY TOMOGRAPHY PROCEDURE
Assuming that the propagation path of P-wave is a straight line, the tomography analysis via
the attenuation-rate and the wave-velocity distribution was carried out in order to identify thehorizontal cracks in RC slab. The source locations of all AE events are used as input, where aconstant wave velocity (m/s) and attenuation-rate (dB/m) across the tested member is assumed
for calculation. The amplitude and the excitation time at the source of the considered AE event
is estimated on the basis of the arrival times and the amplitudes recorded by the sensors. The
attenuation rates and wave velocities along all straight ray-paths between the source and thereceiving sensors are computed.
The algorithm for AE source location is based on the Inglada’s method, which is used inseismic engineering for locating the epicenter of earthquakes [1,2]. By assuming a constantwave velocity inside the tested specimen, the source location of an AE event is determined fromthe arrival times of their associated elastic waves at the locations of several sensors. In thispaper, 3000 m/sec was set as the constant value in consideration of averaged speed in intact
and deteriorated concrete.
For each AE event, the peak amplitude of the elastic wave at the source is unknown.
Consequently, it can be approximated to calculate wave velocity and attenuation rate along theconsidered wave paths. In the tomography algorithm, the arrival time and the peak amplitude of
the signal recorded by each sensor is plotted as a function of the distance between the source
and the sensors. The linear regression between the arrival time and the peak amplitude of theelastic wave and the distance from the source is computed. The AE dispatched time and thepeak amplitude at the source is referred to as equal to the value for the case that the distance isequal to zero.
Tomography computation is carried out based on elastic wave parameters with the velocity
and the attenuation rate in this paper. In the tomography based on the attenuation, theanalyzed area of interest must be divided into mesh elements characterized by their own wave
velocities and attenuation rates. Then, a first estimate on distribution of the wave velocities andthe attenuation rates is to be provided as input. By comparing those values along each wave
path to its calculated value of the assumed distribution, the SIRT algorithm can lead to proper
distribution of the wave velocities and the attenuation rates. The measured travel time andattenuation rate along each ray path is estimated from Eq. (1).
(1)
where, ARmeasured,i: measured average attenuation rate along the wave path from the source tothe ith sensor, Asource: estimated peak amplitude of the elastic wave associated to the considered
AE event at its source, Areceiver,i: peak amplitude of the elastic wave measured at the ith sensor,
Ni: mesh number of elements crossed by the wave path from the source to the ith sensor anddi,j: length of the wave path from the source to the ith sensor in the jth element.

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Second, the attenuation rate along each wave path based on distribution of the attenuation
rates in the mesh elements is computed by Eq. (2).
(2)
where, ARcalculated,i: calculated average attenuation rate along the wave path from the source tothe ith sensor (dB/m), ARj: attenuation rate in the jth element (dB/m) and M: mesh number of
elements crossed by the ray path from the source to the ith sensor.
Afterwards, the difference between the measured and the calculated attenuation rates iscalculated for each wave path by using Eq. (3).
(3)
In a similar manner to Eq. (1), the differences of the attenuation rates on all the wave paths
are estimated by Eq. (4).
(4)
where N: the number of wave paths crossing the jth element. The attenuation rate in eachelement is then updated with Eq. (5).
(5)
The procedure from Eq. (2) through Eq. (5) is repeated until the convergence is reached. As
for wave velocity as the parameter of SIRT algorithm, the same manner as attenuation rate isapplied for computing wave velocity distribution in the area of interest and analyzed.
3. RAIN-INDUCED AE DETECTION AT EXISTING RC BRIDGE DECK
A real RC bridge deck was selected as study targets which has deterioration such as rebar
corrosion, breaking by salt damage, and deck fatigue. AE measurement was carried out in the
RC bridge deck. 15 AE sensors are set on the bottom side of RC bridge decks. Resonancefrequency of AE sensor is 30 kHz. AE sensors arrangement on the RC deck is shown in Fig. 1.
Thickness of both RC bridge decks is 235 mm. Large crack with water leakage trace wasconfirmed. Threshold value of LUCY (location uncertainty) is set on 300 mm as half spacing of
two adjacent sensors in this study. LUCY means source location accuracy and is the root-meansquare of the difference between calculated and observed distances between source andsensor [3].
The data was extracted during the rain peak and analyzed the source locations. Fig. 2 shows
the AE source location results of the measured panel and the horizontal cracks located at 150
mm or less from the surface of the deck (mostly in the top layer) by visual inspection of thecored samples. Source locations considered to have low reliability has been filtered. The
extracted data for only 700 seconds indicated enough amounts of AE sources for analysis are
identified. In the figures, relatively low-density areas surrounded by dashed lines can be seen in
the panel. Red line shows the sensor arrayed area. This low-density area is suspected to beheavily deteriorated, since AE is generated on the road surface and it travels through the deckand detected by the AE sensors attached on the bottom surface of the deck. Since raindrops hit
all over the road surface uniformly, the AE source locations should show uniform distribution in
the case of a sound deck. On the other hand, if severe damage like large horizontal crack is

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Figure 1 Sensor arrangements for AE measurement on the real RC bridge decks

Figure 2 Result of AE source location analysis

Figure 3 Wave velocity and attenuation rate distribution
inside the deck, as depicted in the figure, the AE generated at the road surface is attenuated,diffracted, or blocked by the crack. Consequently, AE sensors right under the crack receive
fewer AE or don’t receive rain-induced AE at all, and the source distribution under the crack
would be sparse or vacant. Therefore, by calculating the AE source locations, the density of the
AE sources would reveal the existence of the crack inside the deck. Since all AE sources
generated by raindrops are in the plane of the road surface, the source locations can be
estimated easily. Additionally, heavy rain generates large amount of hits on the road in a shorttime, and it realize the inspections with great efficiency.
4. RAIN-INDUCED AE TOMOGRAPHY WITH WAVE VELOCIY AND ATTENUATION RATE
As expected, a sufficient number of rain-induced AE events respecting the quality criteria has
been found as shown in Fig. 2. The wave velocity distribution by means of AE tomography, as
explained above, in this panel has been successfully computed from these data and the results

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Figure 4 Wave velocity and attenuation rate distribution (d=√2)

Figure 5 Wave velocity and attenuation rate distribution (d=2)
for the top layer of the deck, where the cracks were located, are presented in Fig. 3 for wave
velocity tomography and attenuation rate tomography respectively.
The relatively low-density areas in the panel surrounded by dashed lines in Fig. 2 is supposed
to be an extended horizontal crack [4]. The horizontal cracks were located at 150 mm or lessfrom the surface of the deck (mostly in the top layer) by visual inspection of the cored samples.
Therefore, it is reasonably understood that wave velocities and attenuation rate in top layer are
lower than bottom layer. After all, in the tomography results, the soundness of concrete and thepresence of horizontal crack roughly correspond to the wave velocity and the attenuation rate
distribution. The wave velocity and attenuation rate inside the core samples presenting large
horizontal cracks are generally lower than 2700 m/s and more than 20 dB/m at least over acertain part of their depth and the wave velocity inside the un-cracked core samples is generally
higher than 2700m/s over their entire depth. The tomography computation was re-executed inconsideration of sensor array coordination with different sensor distance. Longer sensor
distance causes more source location bias, since AE hits are missed due to attenuation in thetravel path.
While setting the sensor distance ratio at d=1 as shown in Fig. 1, tomography results obtainedby the sensor arrangements with d=√2 and d=2 are displayed in Fig. 4 and Fig. 5 with source
location results. As the result, source locations are biased only in the enclosed central area withthe 4 nearest sensors as shown in Fig. 6 (d=√2) and Fig. 7 (d=2). It is quantitatively indicatedthat longer sensor distance (less sensors) gives less ray path density, which directly means lessdata amount in the tomography computation, changing sensor distance from d=1 to d=2, they
are not exactly corresponding with the original result (d=1). The threshold values for wave
velocity of 2700 m/s and attenuation rate of 20 dB/m might lead to misjudge the presence ofhorizontal crack, however, it can be assumed in the tomograms that the middle part of targetedarea is damaged. On the other hand, those findings and accuracy on damage detection in the
results cannot be achieved when d=2 for velocity distribution. Consequently, it is concluded inthis paper that a limitation to perform an adequate resolution of tomography results followed by
AE measurement depends on the sensor array.
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Figure 6 Source location result and ray-path density distribution (d=√2)

Figure 7 Source location result and ray-path density distribution (d=√2)
5. CONCLUSION
The elastic waves generated by the rain hitting the pavement surface can be recorded andused for reconstructing the wave velocity and the attenuation rate distribution inside RC slab.
This method presents the advantage that a large number of AE events can be recorded in avery short period of time. Also, the computation technique assuming linear ray-paths enables tomake the computation time drastically shorter than conventional ray-trace algorithm since thecalculation process is undoubtedly simpler based on linear ray-paths without the detours. It is
additionally believed that wave velocity and attenuation rate distribution can be calculated withhundreds or thousands of the rain-induced AE events more precisely than those results withusing a few internal AE events such as secondary AE induced by traffic loading. Further
investigation is needed to identify the appropriate sensor array in consideration of source
location bias and the threshold value of elastic wave parameters, wave velocity and attenuation
rate, to judge the damage in targeted structure when the proposed tomography technique isapplied.
REFERENCES[1] Salinas, V., Vargas, Y., Ruzzante, J. And Gaete, L., Localization algorithm for acoustic
emission, Physics Procedia, Vol. 3, pp 863-871, 2010.
[2] Ge, M., Analysis of source location algorithms, J. Acoustic Emission, Vol. 21, pp 14-28,
2003.
[3] Hamstad, M.A., Acoustic emission source location in a thick steel plate using Lamb modes,
J. Acoustic Emission, Vol. 25, pp. 194-214, 2007.
[4] Takamine, H., Watabe, K., Miyata, H., Asaue, H., Nishida, T., Shiotani, T., Efficient Damage
Inspection of Deteriorated RC Bridge Deck with rain-induced AE Activity, Proceedingsof IAES-23, IIIAE2016 ßKyoto and ICAE-8, pp.231-236, 2016.

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Development of Prestressed Concrete Bridge Tendon
Damage Diagnosis System using Acoustic Emission
TechniqueDong-Woo Seoa, Ga-Young Kimb, Ki-Tae Parkb, Dong-Hyun Kimb, Yoon-Gi Hongba

Korea Institute of Civil Engineering and Building Technology; bRectuson Co., Ltd.
ABSTRACTIn 2016, the prestressed cable (tendon) of the overpass bridge in Seoul was ruptured due tocorrosion, resulting in severe traffic congestion, inconvenience and massive economic loss. It isestimated that the prestressed concrete type bridges will reach 1.8 billion USD, which is 50% of
the annual budget in whole new bridge construction market in South Korea. Therefore, theefficient and economical inspection and monitoring technology to manage the tendon of theprestressed concrete bridge. The collapse of such structures can be prevented in advance, it isconsidered that the structural health can be greatly improved. The purpose of this study is todesign the acoustic emission monitoring system and to determine optimal sensor type, quantity
and installation location. In order to construct a low-cost acoustic emission monitoring system
for tendon safety management of aging prestressed concrete bridges, both indoor and outdoor
experiments were conducted to collect acoustic emission signals and to analyze its
characteristics with several tendon damage scenarios. The final goal was to establish a low-cost
system that installs the minimum sensor and detect the tendon damage remotely to themaintenance manager.
Keywords: Prestressed concrete bridge, tendon damage, non-destructive test, inspection,
monitoring.

1. INTRODUCTION
In Korea, since the introduction of prestressed concrete (hereafter called “PSC”) bridges, in the
1960s, 38% of the total bridge construction are PSC types. In particular, the number of old PSC
structures over 30 years since the 1970 's "compression construction" is increasing rapidly [1].
In 2016, the tendon of a PSC type bridge, Jungungcheon overpass bridge, in the inner ring roadof the Seoul Metropolitan City was damaged by corrosion, resulting in sever traffic congestion,
civil unrest, inconvenience, and huge economic loss. It is estimated that the prestressed
concrete type bridges such as the Jungungcheon overpass bridges will reach 2 trillion won(Korean currency), which is 50% of the 4 trillion won per year in the entire bridge construction
market in Korea [2-3]. Therefore, the efficient and economical inspection and monitoring
technology to manage the PCS bridge tendons are necessary. The collapse of the structure canbe prevented in advance and the integrity of the structure can be greatly improved.
Currently, various non-destructive methods for detecting PSC bridge tendon damage are in
research and development, but the field utilization is low due to low reliability and high cost.
Acoustic Emission (AE) technique, which is a kind of nondestructive testing method, detects
sound waves generated by artificial impact or steel material breakage with AE sensor anddetects the sound source [4-5]. The AE technique is applicable to evaluate the degree ofcorrosion and fracture of stranded wire for the purpose of evaluating the safety condition of
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bridges. The Korea Concrete Institute [6] conducted a study using the AE technique for theinvestigation of tender failure of PSC bridges and the safety evaluation of bridges. This report
describes the progression of tendon failure and corrosion, tracking the location of fracture oftensions due to the speed of sound waves, and establishing long-term monitoring systemtechnology. Currently, it is evaluated as the most effective method applicable to PSC bridges
worldwide. Therefore, in this study, the feasibility of applying the acoustic emission technique forthe bridge cable safety management was experimentally examined.

2. EXPERIMENTS AND RESULTS
2.1 Laboratory test: signals from a strand
In this study, to determine the optimum type of AE sensor, tensile tests were carried out tomeasure the AE signal characteristics against the physical behavior until the moment of ultimate
failure by applying a tensile force to the strand specimens (corrosion and mechanical damage)used in actual bridges. Tensile tests were carried out using a 100 ton UTM (Universal Testing
Machine) of Korea Institute of Civil Engineering and Building Technology (KICT).
Two types of AE sensor are applied with 60 kHz and 150 kHz resonant type. The test
specimens were carried out in two levels of corrosion condition and mechanical damage. The
mechanical damage was applied to one wire of strand by using a grinder.
Table 1. Specimen description

Table 2. Sensor types

Case
Note
Corrosion 1
Mass loss rate approximately 7.0%
Corrosion 2
Mass loss rate approximately 1.5%
Mechanical damage Cross section loss on a wire by grinder

Channel Resonant frequency
1
60 kHz2
150 kHz

Table 3. Specimen description: RMS trend
Specification
RMS trend

Resonance frequency
Case 160 kHzResultAmplification ratio

Case 2

Case 3

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40 dB

Table 4. Specimen description: energy trend
Energy trend

Case 1Case 2

Case 3

Experimental results show that the 60 kHz resonance sensor has superior sensitivity
characteristics than the 150 kHz resonance sensor. In cases of corrosion (case 1 and 2), signal
generation was confirmed at the loading point, but the signal was stabilized as tensile
progressed and no special signal was detected until just before fracture. This is because thestranded wire with severe progress of corrosion is not separated from the cross section(inadequate signal generation from the AE point of view) and is only increased to just before thebreak. In the case of mechanical damage, the first specimen, which had a relatively large
number of strand breaks, showed a significant signal in the tensile load applied just before
fracture.
2.2 Field test: signals through concrete surface from tendon
Field test was conducted to PSC girder type Hongjecheon bridge in Seoul to confirm installation
condition of on-site sensors, and installed AE test equipment to confirm signal response
characteristics under real conditions. The Hongjecheon bridge is a PSC girder bridge of internaltension type. Since it is impossible to access to the actual tendon, the sensor is installed at theposition close to the location of the tendon on the inner concrete surface.

Fig. 1 Field measurement and sensor installation

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The AE system is based on a portable PC that can be moved as shown in the figure above,making it easy to access the site. The AE sensor was installed on a concrete surface with ametal plate and then mounted on a magnetic holder.
Table 5. Specimen description
Channel
1, 4
2, 5
3, 6

Amplification ratio
40 dB
40 dB
40 dB

Resonant frequency
60 kHz150 kHz
Wideband(100~900 kHz)The sensor was installed at a distance of 10 m to the left and right from the AE system, and thesignal was generated by hitting the concrete surface with a hammer while changing the distancefrom the sensor by 1 m. The sensor was installed with six 60 kHz resonance type, 150 kHz
resonance type, and wide band type (100 ~ 900 kHz) one by one on the right and left sides,
respectively.

Fig. 2 Test results
It has been confirmed that there is no additional environmental noise other than 25 dB. It was
confirmed that the signal difference between the AE sensor and the background noise was 150
kHz resonant type and the signal attenuation was considerably large because the signal was
not detected at the position apart from 3 m or more. Further tests will be carried out by
diversifying the collection conditions and test methods based on the 150 kHz resonance sensor.

3. CONCLUSIONS
Considering both concrete and internal tendons, which constitute the prestressed concrete
bridge, the acoustic emission sensors were selected considering the characteristics of concrete

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and tensile members (i.e., tendons). The results show that the 150 kHz resonance type sensor
is the best for the concrete, and the 60 kHz resonance type sensor is the most sensitive for the
direct measurement to the tendon system.
The sensor installation location was selected based on the on-site survey results, and it wasplanned that the monitoring would be 12 channels for the inner tension type and 12 channels forthe outer tension type based on one box girder. However, there is a need for additional researchas to how to arrange and install the sensors to check the condition of the tendon system.
Table 6. AE Sensor Specifications for direct measurement from a strand
PhysicalCase Material : Stainless Steelspecification
Face Material : Ceramic
Shock Limit : 500g
Connector Locations : Side
Resonant frequency
Pre-amplifier
Calibration sheet

150 kHz
Integral Pre-Amplifier 40dB

Table 7. AE Sensor Specifications for measurements through concrete surface
PhysicalCase Material : Stainless Steelspecification
Face Material : Ceramic
Shock Limit : 500g
Connector Locations : Side
Resonant frequency
Pre-amplifier
Calibration sheet

60 kHzIntegral Pre-Amplifier 40dB

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4. ACKNOWLEDGEMENTS
This research was supported by the internal projects (20190115-001) supported by the Korea
Institute of Civil Engineering and Building Technology.

5. REFERENCE LINKING
1. Korea Infrastructure Safety and Technology Corporation, “Cable inspection and damage
cases of cable supporting bridges”, 2016.
2. Fallis, G. and Peeler, M., “Non destructive detection of fractures in prestressed and posttensioned cables,” PTI Journal, 2009.
3. DaSilva, M., Javidi, S., Yakel, A. and Azizinami, A., “Nondestructive method to detectcorrosion of steel element in concrete, The Nebraska Department of Roads”, USA, 2009.
4. Federal Highway Administration, “Corrosion monitoring research of New York City bridge,”
FHWA Publication No.: FHWA-HRT-14-024, December, 2013.
5. Colony, C., Rogers, D., Nims, D. and Bradley, J., “Investigation, Proposed Rehabilitation andApplication of a Corrosion Sensor to the Anthony Wayne Bridge Suspension Cables,”International Cable Supported Bridge Operator’s Conference, Halifax, Canada, 2016.
6. Korea Concrete Institute, “Development of acoustic emission techniques for preventing
sudden collapse of prestressed concrete bridges,” Final report, pp.14-43, 2006.

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Development of Structural Health Monitoring Technology forInternal Tendon of PSC Bridge Using Acoustic Emission
Technology
Dong-Hyun Kima, Yoon-Gi Honga, Dong-Woo Seob, Ki-Tae Parkb
aRECTUSON. Co., LTD., 51756 12-1, Gaposinhangnam-ro, Masanhappo-gu,
Changwon-si, Gyeongsangnam-do, Korea.
bKorea Institute of Civil Engineering and Building Technology, 283, Goyangdae-ro,ilsanseo-gu, Goyang-si, Gyeonggi-do, 10223, Korea.
ABSTRACTThe necessity of safety diagnosis of structures has recently been emphasized due to the aging
of public PSC bridges over 20 years. Tendons consist of external tendon and internal tendon
type. External tendon types are already undergoing inspection and repair through visualinspection and general non-destructive methods in case of problems. However, internal tendontype PSC bridges are buried in the concrete filled with grout to prevent corrosion of stranded
strand, and after the severe damage caused by corrosion, there is no inspection method thatcan be confirmed. Acoustic emission technology has attracted attention recently as atechnology capable of diagnosing defects of large structures and locating defects. The acoustic
emission technique was used to detect the failure state of the internal tendon of the PSC bridge
and to analyze the defect signal transmitted through the tendon to find the degree of damage
and the location of the defect. The purpose of this study is to evaluate the integrity of the tendon
by distinguishing the general signal from the concrete and the defect signal from the internaltendon. Average frequency and RA value were used as parameters for fault diagnosis andanalysis. In addition, RTRI and Ib Value were applied for the evaluation of defectiveness andIntegrity. Finally, in this study, it was confirmed that diagnosis of internal tendon of PSC
structure is possible by analyzing the acoustic emission characteristic signal generated in the
PSC structure at the time of load application.
Keywords: Acoustic emission, PSC bridge, internal tendon, Structural Health Monitoring.

1. INTRODUCTION
Prestressed concrete (PSC) has been widely applied to the construction of structures such as
overpasses in the form of supporting the upper structure by pre-stressing the concrete using thestranded material. Since the late 1960s, corrosion and rupture of tensional materials haveoccurred in many European and North American countries and have been continuously
maintained for structures constructed using the PSC method. [1-2]Recently, there have been many researches on the detection of early defects to secure diagnosis
technology and to prevent the breakage of tensions due to the social issue of securing the safetyof these structures. [3]The purpose of this study is to propose the health monitoring method of the tendon by applying
acoustic emission technology to determine whether the PSC girder bridge is damaged. The
following researches were carried out to achieve this research objective. First, the correlation
analysis between the average frequency and the RA value is proposed as the acoustic emission
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parameter for the PSC structure diagnosis. [4-6] Second, In order to determine the presence orabsence of defects, RTRI [7] and Ib Value [8-9] were used to evaluate the acoustic emission
parameters for PSC structure diagnosis. Finally, it was confirmed that the integrity evaluation of
the internal tendon is possible by analyzing the acoustic emission characteristic signal issued at
the time of load application by preparing the normal and defect samples of the tendon for thehealth monitoring of the PSC structure.

2. EXPERIMENTAL SETUP
2.1 Experiment of PSC structure defect detection
Load test was carried out for PSC structural specimens. PSC structural test specimens are shown to haveinternal PSC type, and a total of three specimens were tested for two damaged PSC beams and one normal
PSC beams. This confirmed the possibility of detection of already damaged tendons. The left side of Fig. 1
is a photograph of the site at the time of the experiment.

Fig.1 PSC structure defect detection experiment (left) and on site installed PSC structure (right)
Detailed specifications of the PSC structural specimen are as follows. Experiments were carried out on
PSC beams 1 and 2. Table 1 shows the specifications of the PSC structure test specimen.

Name

Table.1 Specification of PSC Structure
Specification
Placement of tendons
LengthWidth
Hight
NormalDamageTotal

Type ofdamage

PSC-1

6,350

300

600

2

0

2

-

PSC-2

6,350

300

600

0

2

2

Mechanicaldamage

PSC-3

6,350

300

600

0

2

2

Corrosion

The right side of Fig. 1 shows the on-site condition of the PSC structure test specimens. The PSC beamthat performed the experiment is the left two. The AE sensor uses a total of 8 channels, the 60 kHz
resonance type for the four channels installed in the anchorage, and the 150 kHz resonance type for thefour channels attached to the concrete surface. Table 2 shows the sensor information used in theexperiment.

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Table.2 Specification of AE sensor
ChannelAmplification Ratio

Resonance Frequency

Installation Location

1ch, 3ch, 5ch, 7ch

40dB

60kHz

Anchorage

2ch, 4ch, 6ch, 8ch

40dB

150kHzConcrete Surfaces

As shown in left side of Fig. 2, the AE signal was measured while gradually increasing the load on the PSC
beam. The reference weight of 1Ton was stacked on the PSC beam one by one, and the reference weight
was stacked up to 4 for one PSC beam. AE Activity observation and signal acquisition during maintenancetime were performed with a load holding time of about 10 minutes for each reference weight increase. The
left side of Fig. 2 shows the installation of a reference weight on the specimen for load application.

Fig.2 Load cycle for experiment (left) and Installation of reference weight for load application (right)

2.2 AE Activity

Fig.3 Cumulative Duration (from 1 ton to 4 ton)

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Fig. 3 is a graph showing cumulative durations measured during each holding time (1 ton to 4 ton). It canbe seen that the signal persistence of the sensor channel installed in the relatively defect PSC test specimen
is larger. Exceptionally, the signal persistence of channel 3 tends to be noticeably large among the sensors
installed in the normal PSC test specimen, but the same signal does not tend to appear in channel 1
installed on the opposite side.

2.3 Average Frequency vs RA Value

Fig.4 Average Frequency vs RA Value
Fig. 4 shows the correlation between the average frequency and the RA value of the measured signal. The
results of the sensor installed on the tendon anchorage - 1ch & 3ch (normal), 5ch & 7ch (defective) - andthe sensor installed on the concrete surface - 2ch & 4ch (normal), 6ch & 8ch (defective) - are distinguishedfrom each other. This tendency is judged by the secondary AE activity [10] that occurs when the load isapplied to the strand where the fracture occurs, which is prominent in the defective PSC specimen.
Secondary AE activities are mainly characterized by shear cracks.

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2.4 RTRI Value

Fig.5 RTRI (left) and Ib Value (right) analysis
The left side of Fig. 5 shows the result of analyzing only the RTRI of the sensor installed on the tendon
anchorage separately. When the defective PSC specimen was compared with the normal PSC specimen,
it was confirmed that the RTRI was smaller and the tendency was decreased as the load increased.
However, in the case of a 2-Ton load, exceptions such as 1 channel, which are smaller than those of normal
specimens, were also confirmed.

2.5 Ib ValueIb Value was confirmed only in 5ch and 7ch installed on the defective PSC anchorage for 1Tonload and 3Ton load as shown in the right side of Fig.5. This is because the strand in the tendon
is partially destroyed and it is judged as a signal that the instability of the structure is proactively
displayed. Further experiments are needed to determine if there is reproducibility on the same a1,
a2 constant criteria.
3. RESULTS
In this study, the integrity evaluation using the acoustic emission technology was performed forthe safety diagnosis of the internal tendon of the PSC structure, and the following conclusions
were drawn. Acoustic emission activity analysis showed that cumulative duration signals were
measured largely in defective PSC specimens. However, the channel (3ch) in which the larger
signal was generated in the normal PSC specimen appeared, and it was judged that it is difficult
to distinguish the normal and defective only by the simple signal amplitude. RA value and averagefrequency shows the difference in the signal characteristics between the channel installed in theanchorage and concrete. RTRI Value As a result, RTRI of defective PSC test specimen was low
only for the channel installed on the anchorage area. However, since there is an exceptionalsignal channel (1ch), it is necessary to establish a standard for repeated testing. Ib Value analysis,
signals were observed in some load sections (1, 3Ton) only for the channels installed on theanchorage, which is considered to be a preceding signal for the structural anomaly. Since theresults of this study are analyzed by one experiment, it is necessary to confirm the reproducibilityof the same phenomenon through repeated experiments and to conduct a follow - up study on
establishment of clear criteria through data accumulation.

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4. REFERENCES[1] Laura Anania, Antonio Badala, Giuseppe D’Agata, Damage and collapse mode of existing post
tensioned precast concrete bridge: The case of Petrulla viaduct, Engineering Structure, Vol.162,
pp.226-244 (2018)[2] Wei-Hua Hu, De-Hui Tang, Jun Teng, Samir Said, Rolf. G. Rohrmann, Structural Health
Monitoring of a Prestressed Concrete Bridge Based on Statistical Pattern Recognition ofContinuous Dynamic Measurements over 14 years, MDPI/Sensors, Vol.18, 4117 (2018)[3] Pablo M. Paez, Beradi Sensale, Improved prediction of long-term prestress loss in unbonded
prestressed concrete members, Engineering Structure, Vol.174, pp.111-125 (2018)[4] Arash Behnia, Hwa Kian Chai, Tomoki Shiotani, Advanced structural health monitoring of
concrete structures with the aid of acoustic emission, Construction and Building Material, Vol.65,
pp.282-302 (2014)[5] Hisham A. Elfergani, Rhys Pullin, Karen M. Holford, Damage assessment of corrosion in
prestressed concrete by acoustic emission, Construction and Building Material, Vol.40, pp.925933 (2013)[6] Christian U. Grosse, Masayasu Ohtsu, Acoustic Emission Testing, Part B Application, Springer,
Dordrecht, pp. 211-238 (2008)[7] Xiu Luoa, Hiroshi Hayaa, Tomoaki Inabaa, Tomoki Shiotanib, Yasuhiro Nakanishib, Damage
evaluation of railway structures by using train-induced AE, Construction and Building Material,
Vol.18, pp.215-223 (2004)[8] Mohamed K. ElBatanouny, Paul H. Ziehl, Aaron Larosche, Jesé Mangual, Fabio Matta,
Antonio Nanni, Acoustic emission monitoring for assessment of prestressed concrete beams,
Construction and Building Material, Vol.58, pp.46-53 (2014)[9] T.Shiotani, Z.Li, S.Yuyama, M.Ohtsu, Application of the AE improved b-value to quantitativeevaluation of fracture process in concrete-materials, Acoustic Emission Group, Journal of
Acoustic Emission, Vol.19, No.5~6, pp.235-246 (2001)[10] Xiu Luoa, Hiroshi Hayaa, Tomoaki Inabab, Tomoki Shiotanic, Seismic diagnosis of railway
substructures by using secondary acoustic emission, Soil Dynamics and Earthquake Engineering,
Vol.26, pp.1101-1110 (2006)

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Use of Acoustic Emission in Combination with Machine
Learning for Monitoring of Gas-liquid Mixing in Stirred Tanks
G. Forte1,2, F. Alberini2, M.J.H. Simmons2, E.H. Stitt1
1

2

Johnson Matthey Technology Centre, Billingham, TS23 4LB, UK
School of Chemical Engineering, University of Birmingham, Edgbaston, B15 2TT, UK

ABSTRACT

Operations involving gas-liquid agitated vessels are common in the biochemical and chemicalindustry where ensuring good interphase contact is essential. In this work, acoustic emission(AE), using a piezoelectric sensor, was applied to evaluate gas-liquid mixing regime within twophase (gas-liquid) and three-phase (gas-solid-liquid) mixtures in a 3 L stirred tank equipped witha Rushton Turbine and a ring sparger. As gas bubbles move upward through the vessel, theycollapse, break or coalesce generating sound waves transmitted to the wall and to the acoustic
transmitter. By varying impeller speed and gas flow rate, the acoustic spectrum was obtained foreach flow regime (ungassed condition, loaded and complete dispersion) and analysed using
machine learning to assess predictive capability. A logistic regression algorithm was applied tothe acquired dataset which was divided into a training set, used to train the machine in recognisingfeatures in the spectrum corresponding to each condition, and a testing set, unseen by themachine, used to evaluate the accuracy of regime prediction. The obtained results show that thesystem successfully recognises acoustic spectrum corresponding to the different regimes with anaccuracy higher than 90% both in the presence and absence of suspended particles.
The conducted study demonstrates the capability of AE to identify (as long as accurate training isprovided) the flow regime within a two or three-phase vessel, with potential to be applied at large
scale for process monitoring.
Keywords: Acoustic Emission; Gas-liquid mixing; stirred tank; machine learning.

1. INTRODUCTION
Improving process monitoring is a common need within the process industry, including chemical,
food, biochemical and pharmaceuticals [1]. Desirable features for any in-process measurement
are for it to be non-invasive, suitable for on-line installation and with sufficient temporal resolution
to give a “real time” response, in order to avoid delays in intervening with control measures.
Among several techniques responding to such identikit, Acoustic Emission (AE) is a data-richtechnique with high potential for on-line monitoring.
The AE in a process will often be a combination of numerous acoustic events all propagating to
the sensor via different paths. Although most AE research has focused on fault detection(leakage, failure,..) [1], more recent studies have also investigated AE as a means of monitoring
physico-chemical changes within processes involving powder and fluids [2-4]. Applications in
multiphase mixing are also reported in the literature. While the first known reference on gas-liquid

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flow dates from the 1920s [5], more recently Addali et al. [6] have used AE energy information topredict gas void fraction in a two-phase (air-water) slug flow.
Gas-liquid reactors are very common operating units in biochemical and chemical industry, whereensuring appropriate interphase contact and gas dispersion is critical. Different regimes can beachieved when gas is sparged into stirred tanks: namely as flooded, loaded, completely
dispersed, and gas recirculation [7] depending on gas flow rate, tank design and fluids properties.
In this work AE data is acquired for loaded and completely dispersed regimes as well as inungassed condition (to simulate accidental shut down of air feed) in a 3 L stirred tank equippedwith a Rushton Disc Turbine. A machine learning supervised algorithm, logistic regression, isapplied to identify features in the acoustic spectrum that can be used to recognise the different
conditions, having already shown its potential in other applications [9]. Machine learning (ML)
has recently seen increasing interest and application in chemical and manufacturing industry as
the techniques are able to deal with large data sets and provide reliable statistical analysis which
are both essential as industry embraces the challenges of smart manufacturing and Industry 4.0.

2. MATERIAL AND METHODS
2.1 Stirred tank configuration
A schematic of the stirred vessel is shown in Figure 1. The diameter of the 3 L tank (T) was 0.14m, and it was equipped with four baffles with width, B = T/10 and a ring-shape gas sparger witheight 0.5 mm orifices placed equally around the ring, located at the bottom of the tank. A stainlesssteel six blade Rushton Disc Turbine (RDT6) with diameter 0.056 m (D/T= 2/5) was used forstirring. The used liquid was an aqueous solution of Nickel nitrate hexahydrate (99,99% Sigma
Aldrich®) and its level was set to 0.21 m (H/T=3/2).

Figure 1. Schematic of the stirred vessel
The airflow (ranging between 5-10 L min-1, equivalent to 1.5 and 3 vvm respectively) was fed via
the ring sparger. During the different experiments the Reynolds number was kept between 15,000
and 60,000 (turbulent regime). The impeller speed (300-1300 rpm) and the gas flow rate wereS149

changed in order to set the required operating regime (loading or recirculation) and then the same
conditions of impeller speed were recorded with no air fed to the system (to mimic failure of gas
feed unit).
Stainless steel particles were used for the three-phase experiments. The particles, used atconcentrations of 3, 4 and 5 % w/w had a density ρs = 8000 kg m-3 and size between 0.177 and
0.420 mm.
2.2 Acoustic emission
The approach taken for design of the apparatus follows existing practice in the literature [10]. A
piezoelectric sensor (Vallen Systeme GmbH, Icking, Germany), with resonance frequency of 375
kHz and diameter of 20.3 mm, was attached to the tank in Figure 1 using a silicone based vacuum
grease. The sensor was attached to a preamplifier (40 dB gain, Vallen Systeme) and an
oscilloscope (5243A Pico® Technology Limited) connected to a laptop (PicoScope® 6 software)
was used to record data. Measurements at each condition were taken for 0.2 s with a sampling
rate of 750 kHz, ensuring coverage of the resonance frequency of the sensor in the spectrum(Nyquist rule). The sensor was placed on the vessel outside wall at a height C corresponding to
the impeller region, C = 0.046 m (T/3).
2.3 Data processing and machine learning
The time-domain signals were processed using the Fast Fourier Transform (FFT) function; thespectrum was filtered removing signal below 5 kHz and the 1000 most variable frequency peaks
were selected as features characterising each condition before being fed to the machine learningalgorithm (logistic regression). A schematic of the data processing is reported in Figure 2.

Figure 2. Scheme of the data processing
The logistic regression algorithm can be defined as a binomial regression, where the output is a
probability, ℎ𝜃 , of a condition being verified, in the specific case the belonging of the signal to oneof the three conditions. Logistic regression makes use of a logistic function, also known assigmoid function:𝑖𝑇𝑖ℎ𝜃𝑖 (𝑥) = 1/(1 + 𝑒− 𝜃 𝑥 )

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(1)

where ℎ𝑖𝜃 (𝑥) is the obtained probability, that the spectrum 𝑥 belongs to a given condition 𝑖 basedon the parameter 𝜃𝑖 . For each condition, 𝑖, a parameter 𝜃𝑖 is obtained in the learning process,consisting of an optimisation run on the parameter using the gradient descent strategy. The
training dataset consisted of 210 spectra (70 for each regime).
Two test datasets are acquired, one at the three different regime conditions with the bi-phasic
mixture, and a second one in which solids are added to the mixture. The presence of solids atsuch concentration does not alter the gas-liquid mixing regime [11], but can represent a
disturbance on the acoustic signal due to impacts between particles and the wall and interparticle
contact.
The task is addressed as a classification problem: once the training process is finalised, themachine is challenged: the system is given an unseen spectrum and it evaluates whether it
corresponds to loading, recirculation or ungassed condition.

3. RESULTS
A dataset composed of 100 spectra was fed to the trained algorithm to be classified. To each ofthem, a predicted class (ungassed, loading or recirculation) is assigned. In Figure 3, the resultsof the test are reported in a prediction map for the biphasic case.

Figure 3. Prediction map for gas-liquid case
In Figure 3, all the coloured points represent cases correctly predicted by the system, while thewhite squares are the cases predicted in error. The first five spectra are wrongly classified asloading instead of ungassed and therefore are contained in the loading prediction region on themap. A few others are not classified correctly by the algorithm; however, the overall accuracy of
prediction is 90%.

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In the second part of the study, stainless-steel particles are added at different concentrations andthe acoustic signal is acquired for each of the three regimes. The obtained results are summarisedin Figure 4.

Figure 4. Prediction map for gas-liquid-solid caseIn this case, the dataset comprised of 330 spectra and the accuracy increases to 92.3%, with aneven distribution of errors across the different conditions. The presence of the solids does notaffect the classification algorithms despite the fact that the training was carried out solely for twophase gas-liquid flow conditions. The reason for this can be found in the influence of the solids
on different frequencies from the ones characterising the air bubble disruption and impacts.
This finding allows extension of the methodology used for two-phase to applications where solids
are critical to the process, as in cases of heterogeneous reaction. It is indeed possible to monitor
dispersion of gas regardless of suspension of solids with high accuracy.

4. CONCLUSIONSIn this work, Acoustic Emission was used in combination with a logistic regression machinelearning algorithm to identify gas-liquid mixing regimes in a stirred tank. The machine was trained
to recognise ungassed, loading and recirculation regimes using a training dataset, consisting of
the acoustic spectrum acquired under gas-liquid flow conditions. The system was thenchallenged to recognise such regimes under both two-phase and three-phase conditions(obtained adding stainless steel particles), achieving an accuracy equal to and above 90%
respectively. The conducted study aims to propose AE as a potential diagnostic and condition
monitoring technique for fluid mixing applications in combination with the use of machine learningalgorithms.

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REFERENCES[1] J. W.R. Boyd and J. Varley, ‘The uses of passive measurement of acoustic emissions fromchemical engineering processes’, Chemical Engineering Science, vol. 56, no. 5, pp. 1749–
1767, 2001.
[2] C. Aldrich and D. A. Theron, ‘Acoustic estimation of the particle size distributions of sulphideores in a laboratory ball mill’, J. S. Afr. Inst. Min. Metall., vol. 100, no. 4, pp. 243–248, 2000.
[3] K. H. Esbensen et al., ‘Acoustic chemometrics—from noise to information’, Chemometrics
and Intelligent Laboratory Systems, vol. 44, no. 1–2, pp. 61–76, 1998.
[4] A. Crouter and L. Briens, ‘Passive acoustic emissions from particulates in a V-blender’, Drug
Dev. Ind. Pharm., vol. 41, no. 11, pp. 1809–1818, 2015.
[5] W. H. Bragg, ‘The world of sound’, (Bell, London), pp 69-74, 1921
[6] A. Addali, S. Al-lababidi, H. Yeung, D. Mba, and F. Khan, ‘Acoustic Emission and Gas-Phase
Measurements in Two-Phase Flow’, Proceedings of the Institution of Mechanical Engineers,
Part E: Journal of Process Mechanical Engineering, vol. 224, no. 4, pp. 281–290, Nov. 2010.
[7] A.W Nienow. M.M.C.G. Warmoeskerken, J. M. Smith, and M. Konno, ‘On the flooding/loadingtransition and the complete dispersal condition in aerated vessels agitated by a Rushtonturbine’, in 5th European Conference on Mixing, Wurtsburg, West Germany, pp. 143–154,
1985.
[8] J.M. Hilbe, ‘Logistic Regression Models’ Chapman & Hall/CRC Press, 2009.
[9] T. Wuest, D. Weimer, C. Irgens, K. Thoben, ‘Machine learning in manufacturing: advantages,
challenges and applications’, Production & Manufacturing Research, vol. 4, no. 1, pp. 23–45,
2016.
[10] A. Nordon, R. J. H. Waddell, L. J. Bellamy, A. Gachagan, D. McNab, D. Littlejohn G. Hayward,‘Monitoring of a heterogeneous reaction by acoustic emission’, The Royal Society ofChemistry: the Analyst, vol.129, pp.463-467, 2004.
[11] C.M. Chapman, A. W. Nienow, M. Cooke, J. C.Middleton, J. C., ‘Particle-gas-liquid mixing instirred vessels. Part IV: Mass transfer and final conclusions’, Chem Eng Res Des, vol. 61,
no. 3, pp. 182–185, 1983b.

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Acoustic Emission in Process of Thermal Decomposition of
Potassium Hydrogen Carbonate Crystals
Shavkat Azimov, Vladislav Petukhov and Abdukholik Lakaev
Umarov Physical-technical Institute, Academy of Sciences of Tajikistan, Dushanbe,
Tajikistan
ABSTRACT
Acoustic emission was investigated in process of potassium hydrogen carbonate (PHC)
thermal decomposition. Different literature sources indicate very wide range of temperatures for
its thermal destruction start (from 100 to 400°C). Study of AE generated at this process has foundto be helpful for more exact determination the temperature of its beginning.
Heated transparent PHC crystals without losing their shape turn into opaque potassium
carbonate (PC), while carbon dioxide (CO2) and water evaporate. Temperature of the thermal
degradation onset ~ 130-150°С was determined in result of various samples tests: single PHC
crystals, monolayer of dozen loosed crystals and tablets pressed from thousands of crystals.
Data of PHC powder thermal decomposition obtained by thermogravimetric anddifferential thermal analyses found out a good compliance with AE data concerning thetemperature range of the process. An X-ray diffraction analysis used to identify the initialsubstance and products of PHC thermal decomposition.
Tests with tablets of PHC revealed two separate temperature regions of AE activity that
do not intersect. 1st region of 60-90°C determined as an “elastic unloading range”. AE caused bymechanical relaxation of crystals releasing the elastic energy stored in the process of tablet
pressing. 2nd region of 130-200°C related directly to the thermal decomposition of PHC.
Kaiser effect was detected at the process of mechanical relaxation during the cyclicheating/cooling of PHC tablets in the temperature range of 60-90°C.
In result of the study, the AE technique can be recommended to control the regeneration
process of CO2 absorbers based on a composite from PC precipitated on an aluminum oxide γAl2O3 porous matrix, which is converted to PHC during the sorption of CO2. The temperature limitsof the regeneration process, which results in the reverse transformation of PHC into PC, can bemonitored by the AE activity accompanying this process.
Keywords: acoustic emission; thermal decomposition; potassium hydrogen carbonate;
potassium carbonate, carbon dioxide, crystals.
INTRODUCTION
Potassium hydrogen carbonate also named as potassium bicarbonate or carbonic acidmonopotassium salt
is an inorganic compound, colorless monoclinic crystals. It is widelyused in various fields, e.g. as a food additive, corrosion inhibitor, and an ingredient of fire
extinguishers. It is used as well as a fertilizer in agriculture, and having fungicidal properties, isused in organic farming for the control of powdery mildew and apple scab [1]. PHC appears as acomponent of the regeneration cycle of an absorber of atmospheric carbon dioxide based on apotassium carbonate (K2CO3) composite deposited on the porous γ-Al2O3 matrix [2]. Thermal
destruction of PHC proceeds according to the formula: 2KHCO3 → K2CO3 + CO2 + H2O(1)

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The literature indicates different temperature limits of the process at that: from 100ºC to 400ºС.
The purpose of this work was using the AE method to clarify the temperature of the onset ofthermal decomposition of PHC. As well to compare the obtained data with the data of used
thermogravimetric method (TG) with differential thermal analysis (DTA). An X-ray diffraction
analysis (XRD) identified the initial substance and products of PHC thermal decomposition.
2. EXPERIMENTAL
Reagent grade samples of PHC were placed on an upper surface of quartz waveguard
inside the electrical furnace. Thin layer of vacuum pump oil was used as an acoustical contact.
Acoustic emission (AE) signals were received by transducer R-151-AST (PAC) fixed on a bottom
surface of waveguard outside of furnace and connected to PCI-2 system. Sample temperature
was controlled by the differential Cu-constantan thermocouple fed via the F136
nanovoltampermeter and the R111 amplifier into the parametric input of the PCI-2. Three typesof PHC samples, shown in Fig. 1, were heated from room temperature up to 250°C through theseries of 3X20 tests. For comparison, a thermogravimetric analysis (TG) was also carried out incombination with the differential thermal analysis (DTA) of PHC powder by the F. Paulik, J. Pauliksystem. The analyzed sample was previously kept at a temperature of 100°C for 2 hours toremove crystallization water, and then cooled to room temperature without ambient air access.
To identify the substances, before and after the thermal decomposition tests X-ray diffraction
analysis was carried out on by Dron-3 diffractometer.

Fig.1. Test samples of PHC: a) - single crystal; b) - monolayer of freely laying several crystals;c) - tablet of 1.5 g, compressed from thousands of crystals.
3. RESULTS AND DISCUSSION
By heating the samples with varied amount of substance, i.e. beginning from PHC single
crystals, then by tests monolayers of dozens of loosed crystals and at last, up to thousands ofcrystals pressed into the tablet, the boards of thermal decomposition of PHC were defined moreaccurately. The onset of the process has been found about 130-150°C, which corresponds to an
average 5 times less scatter than the mentioned one in the existing literature.
In tests with PHC crystals pressed into the tablet, the interval of AE activity was detectedin the temperature range of 40-80ºС. Certainly, in this area AE caused by the process of releaseof elastic energy stored in PHC crystals under the pressure, i.e. caused by the crystals’ relaxation,
as the sample gets hot. Fig. 2 shows the AE Amplitude spectrum and the Cumulated AE Signal
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Strength vs. Temperature for the heated PHC tablet weighing 1g and pressed at 200 kg/sm2. It is
important that relaxation ends before the substance thermolysis beginning, and these processes
can be separated by AE. The 2nd interval of AE activity with Тº = 130-200°С is directly related tothe thermal decomposition of PHC. The absence of the 1st interval of AE activity within 40ºС < T°
< 80ºС at the tests with single crystals and monolayers of loosed PHC dozens of crystals canserve as negative evidence to relaxation process as a reason of AE activity in tablets’ tests.

Fig.2. AE amplitude spectrum for the heated PHC tablet (∙);
Cumulated AE Signal Strength vs. Temperature (─).
There are shown in Fig. 3 time dependences of temperature, TG - mass changes, DTG differential thermogravimetric and DTA - differential thermographic for hinge of 1g PHC powder.
The thermogravimetric test began at room temperature after pre-holding the sample at Tº = 100ºСfor 2 hours, followed by cooling without air to avoid water adsorption by the powder. The area of
PHC thermal destruction is highlighted in gray. The beginning of the process can be tracked byaccelerated mass loss, and its completion - when the mass loss stops. An additional feature toindicate the process of thermal destruction is to find out the boundaries of the sample temperaturedependence failure, as far as the reaction of the PHC decomposition is endothermic. According
to the thermogravimetry data, the thermal decomposition process of the PHC starts at ~ 130°C
and finishes at ~ 180°C, which gives a good agreement with the data obtained by the AE method.
Tests with PHC compressed in tablet shape revealed Kaiser Effect. Fig. 4 shows the total
AE Hits account; Amplitude-time spectrum and the temperature curve for the sample subjected
to cyclic heating within the temperature range where PHC pressed crystals’ mechanical relaxation
occurs. In this case the temperature is changing cyclically, to act as a cyclically varying
mechanical load in a classic test that detects the Kaiser Effect. It is clearly seen that at the firstthermal loading of the sample the relaxation process, which generates AE, begins at 30°C andstops at the subsequent cooling. At the sample repeated heating the relaxation, followed by AE,

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starts only when the specified temperature level is exceeded. This is evidence of the presence ofthe Kaiser Effect.

Fig.3. Thermogravimetric characteristics of PHC: T°, TG, DTG and DTA.

Fig.4. Total AE Hits account; Amplitude-time spectrum and Temperature-time dependence of
PHC tablet cyclic heating.

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In [2] a composite sorbent K2CO3/γ-Al2O3 was synthesized and tested for direct carbon
dioxide capture from ambient air. As a result of CO2 sorption, potassium carbonate is converted
to potassium hydrogen carbonate and by heating at T º = 250-300ºС the sorbent is regenerated.
In the same paper, it was shown that effective release of CO2 occurs already at Tº ~ 200 ºС. At
the same temperature, the XRD spectrum shows a complete recovery of K2CO3 from KHCO3 after
heating. Increasing the temperature to 250-300ºС in general slightly increases the output of CO2.
The results of our research show that the upper temperature of regeneration can be limited by200ºС. This will lead to reduction of power inputs for regeneration and utilization of carbon dioxide.
The AE method can reliably control the dynamics of the regeneration process.
CONCLUSIONS
1. Potassium hydrogen carbonate thermal decomposition has been studied by means of AE
technique, supplemented by differential thermal analysis, thermogravimetric analysis and X-ray
diffraction analysis.
2. There are detected two not intersecting temperature ranges of AE activity at the heating ofpotassium hydrogen carbonate crystals and powder pressed in form of tablets. It’s supposed thatAE within the 1st range of 60-90°C caused by mechanical relaxation of crystals releasing theelastic energy accumulated in the process of tablet pressing. AE within the 2nd range of 130200°C has been related directly to the thermal decomposition of potassium hydrogen carbonate.
3. Kaiser effect was detected at the process of crystals’ mechanical relaxation during the cyclicheating/cooling potassium hydrogen carbonate tablets in the temperature range of 60-90°C.
4. AE method can be recommend to control the regeneration process of carbon dioxide absorbers
based on a composite from potassium carbonate, deposited on a porous matrix of γ-aluminumoxide, which is converted into potassium hydrogen carbonate during sorption of carbon dioxide.
The dynamics of the regeneration resulting in the reverse transformation of potassium hydrogencarbonate into potassium carbonate can be monitored by the AE activity accompanying thisprocess.
REFERENCES
1. Potassium Bicarbonate Handbook. Armand Products Company, 469 North Harrison Street
Princeton, NJ 08543-5297, http://www.armandproducts.com.
2. J.V. Veselovskaya, V.S. Derevschikov, T.Yu. Kardash, O.A. Stonkus, T.A. Trubitsina,
A.G. Okunev. “Direct CO2 capture from ambient air using K2CO3/Al2O3 composite sorbent”.
International Journal of Greenhouse Gas Control. Vol. 17, p. 332–340, 2013.
3. I.T. Goronovskii, J.P. Nazarenko, E.F. Nekriach. “Kratkiy spravochnik po khimyi” (Concise
handbook of chemistry), p. 144, 1974.
4. J.A. Dean. “Lange's handbook of chemistry”, p.3.43, 1972.
5. B.E Poling, G.H. Thomson, D.G. Friend, R.L. Rowley, W.V. Wilding. “Perry’s chemicalengineers’ handbook book”, 8th edition, section 2: Physical and Chemical Data, p.2-21, 2009.

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Acoustic Emission based Crack Tracking for Concrete
Structures
Fengqiao Zhang, Yuguang Yang
Delft University of Technology, the Netherlands
ABSTRACT
Acoustic Emission (AE) monitoring is one of the possibilities to detect the crack distribution inexisting concrete structures. However, the conventional method requires further destruction like
opening of new cracks or propagation of the existing cracks. In this paper, a new strategy ofusing local cumulative AE activities during unloading to track the crack trajectory is proposed.
With this strategy, a relatively low load level which does not cause further destruction to thestructure is needed. The possibility of this strategy is experimentally examined using a realscale beam of 10-ton damaged under cyclic loading. For calibration, the crack opening ismeasured by Digital Image Correlation (DIC). The crack patterns detected by the new strategy
and DIC show good agreement.
Keywords: concrete beam, cyclic loading, Acoustic Emission, source localization, crack
tracking.

1. INTRODUCTION
The safety of the large stock of aging existing concrete structures is important to the society. Asa key link in the maintenance of these structures, the present conditions including the crack
distribution have to be evaluated accurately. At the moment, effectively evaluating the crack
distribution inside the structures remains challenging. One of the available possibilities is to use
Acoustic Emission (AE) technique [1,2]. In the conventional method, application of AE requires
opening of new cracks or further propagation of the existing cracks, which is linked to heavier
loading of the structures towards its ultimate limit state. Usually this is recognized as proof
loading test of the structures [3], which requires heavy loading equipment with high cost. In thispaper, a different strategy on crack tracking is proposed. In the proposed approach, a relatively
low magnitude of load is applied to open the existing cracks. The AE activities during the crackclosure is used to track the trajectories of the cracks.
The new strategy is validated by a large scale lab test of a 10-ton reinforced concrete beam. For
calibration, the crack development is analyzed by Digital Image Correlation (DIC) measurement.
The result of this paper provides the possibility of AE based crack tracking in the assessment of
the present conditions of the existing concrete structures.

2. AE BASED CRACK TRACKING
2.1 AE activity during unloading

For concrete structures with cracks, it was shown that during unloading, the closure of theexisting cracks or the friction between rebar and concrete, etc. may lead to the AE activities[4,5]. In order to make use of the AE activities during unloading, the expression Calm Ratio was
proposed by Ohtsu et al. in [5,6], which was defined as the ratio of the cumulative AE activitiesth

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during unloading to that of the whole cycle. In a later study reported by Yang et al. [4] on beams
from an existing concrete bridge, it was found that the calm ratio is proportionally linked to thecrack opening. The observations suggest that the AE activities during unloading can be used as
an indication of the presence of existing cracks.
2.2 AE source localization and error estimation
As a widely applied algorithm, grid search method is employed in the study to determine the location of
the AE signal source [1]. The basic principle of the algorithm is to compare the calculated arrival timedifference from the estimated location and the observed arrival time difference from themeasurement [7].

Factors involved in the algorithm such as the choice of arrival time picking methods and thepresence of cracks influence on the travel times of the signal, thus induce an offset between theestimated source location and the real source location, which is defined as source localization
error in [8]. According to Zhang, considering an arrival time picking error within 5 µs and a crackwith an opening in range of [0.05 3] mm between the source and the receiver, the source
localization error was around 15 cm in a target area of 1m × 1m. This amount of source
localization error should be taken into account in the AE based crack tracking in concrete
structures.
2.3 Local cumulative AE activities

In the study of Ohtsu et al. [5,6] and Yang et al. [4], the relationship between AE activities andthe crack opening were studied at global level across the height of the whole specimen. Similar
relationship is expected at the level of the crack trajectory locally. In this paper, a term local
cumulative AE activities is proposed. The AE activities during unloading located in each cell arecounted as the local cumulative AE activities during unloading. Here, cells are discretizedmeasuring area. The size of the cell determines the accuracy of the crack tracking but is limitedby the AE source localization error. This approach of using local cumulative AE activities during
unloading to track the crack pattern is referred to as AE based crack tracking. It cannot only
locate the existing cracks, but also indicate the local crack opening along its trajectory.

3. EXPERIMENT
To explore the AE source localization on real-size structural members, a test on a 10-ton
reinforced concrete beam with length of 10 m, height of 1.2 m, and width of 0.3 m wasconducted. The concrete had a nominal compressive strength of 65 MPa. The maximum
aggregate size was 16 mm. Reinforcing bars in the beams were 6ɸ25, with concrete cover of
25 mm. The beam was simply supported with a span of 9 m and loaded by a point load at 3 mfrom one support (Figure 1).
The beam was loaded cyclically shown in Figure 2. The first seven load cycles are marked as
L1-L7. After the beam was loaded to 250 kN in L5, two smaller magnitudes of load (L6 and L7)
were applied on the beam to reproduce the practical condition that structures in service can be
deteriorated due to heavier loads before the assessment.
Fourteen AE sensors of R6I-AST with a central frequency of around 60 kHz were installed on
the south side of the beam to record the AE hits (Figure 1). The hits with the peak amplitude
over 60 dB were selected to do the source localization. In the source localization, the arrival
times were based on the threshold crossing method where the threshold was set to be 45 dB in
the data acquisition system.

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On the north side of the beam, a sprinkle pattern was painted to do the DIC displacement
measurement. Since the boundary conditions of the beam is plain stress, the crack distribution
at the two side surfaces can be assumed as constant in the width direction of the specimen.
Therefore, the measured crack development from DIC can be used to calibrate the AE source
localization results.

Figure 1: (left) test setup and (right) AE sensor layout on the back side

Figure 2: loading history combined with the cumulative AE hits per minute; the load cycles ofinterest are marked as L1-L7

4. RESULTS
4.1 Crack development

In the three-point bending test, the gradient of the bending moment resulted in a gradualpropagation of the bending cracks with the increase of the load level. Crack patterns at different
load levels have been obtained by DIC. The crack pattern at 250 kN in L5 is shown in Figure 3(left) as an example in which the five cracks reaching the neutral axis of the specimen werestudied further and marked as C2-C6. The crack C1 was marked because it was the firstinitiated crack (in L1). Consequently, C2, C3, C4, and C5 occurred in L3, and C6 occurred in L5.
During the load cycles L6 and L7, no new cracks were observed.
The crack opening along its trajectory was determined using DIC output. Measuring points wereselected on the profile of the crack (C4 shown in Figure 3, right, as an example). The short lineS161

at each point indicates the local normal direction. The crack opening was calculated by checking
the displacement difference between the two points in the normal direction of the crack locally.
The opening of the cracks C3 and C4 at 200 kN (L6) and 150 kN (L7) from the previous
unloaded condition is shown in Figure 4. It can be found that the re-opening of the existing crack
was proportionally linked to the applied load. For the crack opening along its trajectory, in
general, the crack opening was wider in the bottom part than that at the crack tip. While, due tothe opening of the secondary cracks near the crack C4 in the bottom, the crack opening of C4
below 0.3 m is limited. In this study, the lower bound of the DIC measurement is assumed to be
0.05 mm due to the resolution limitation. Therefore, according to the DIC measurement, C3
hardly opened in L6 and opened towards a height of around 0.3 m in L7; C4 opened towards aheight of around 1 m and 0.9 m in L6 and L7, respectively.
250 kN

C4

C4

C2

C3

C5

C6

C1

Figure 3: DIC measurement: (left) crack pattern at 250 kN, and (right) measuring points for the
opening of C4 (the short line at each point shows the normal opening direction.)

Figure 4: opening of existing cracks C3 (left) and C4 (right) in the loading process of L6 and L7
4.2 AE source localization in the unloading

The AE activity during unloading was observed to be more active than that during loading in L6
and L7 (shown in Figure 2). This meets with the expectation that for the damaged beam with ahigher previous load (in this case, 250 kN in L5), in the cyclic loading of a smaller load level,
more AE activities were generated due to the closure of the cracks during unloading.
Grid search method was applied to do the source localization, with the target area (i.e. 2500 mm
× 1200 mm) divided by a mesh with a size of 5 mm. Sources located outside the sensor
enclosed area had less localization accuracy [8] and were neglected in this work.

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The AE source localization results during unloading in L6 and L7 are shown in Figure 5. Eachpoint represents one localized AE activity. It can be seen that AE activities were mostly localizedaround the cracks C2, C4 and C5, while the cracks C3 and C6 could not be traced back. This
may due to the fact that C3 and C6 were opened limitedly in L6 and L7, observed from DIC
measurements (the maximum opening of C3 is shown to be less than 0.1 mm in Figure 4, C6
not shown). The closure of these narrow cracks may generate weak signals which could not bereceived by the AE sensors after attenuation in the propagation. Other cracks which were notreaching the neutral axis could not be detected due to the same reason. It can also be foundthat the crack C4 was traced back with a higher crack height in L6 than in L7, which met with
the DIC measurements.

Figure 5: AE source localization in the unloading process: (left) L6 and (right) L7

In order to indicate the crack opening along its trajectory, the local AE activities during unloading
was calculated. The target area was discretized by cells of size of 80 mm × 80 mm and thenumber of AE activities located in each cell was counted. The results are illustrated in Figure 6.
The cumulative AE activities ranged from 0 to 40 in each cell. The crack opening measured by
DIC is shown on the same graph with a scale of 250 times for comparison.

Figure 6: local cumulative AE activities during unloading in: (left) L6 and (right) L7, comparedwith the crack opening of C4 from DIC; the center of AE clustering around C4 at 0.12- m heightis marked in L6

By comparing the local cumulative AE activities with the crack opening along the trajectory of
the crack C4, it can be found that cells at heights in range of [0.3 0.65] m had more AE
activities, where larger crack openings were observed. An initial conclusion can be drawn that in
the cyclic loading, the local cumulative AE activities during unloading proportionallycorresponded with the crack opening in the previous loading. It can therefore be used as a local
indicator of the crack opening along its trajectory. A further quantitative relationship will bereported in another paper with test data from more specimens.
Regarding to the accuracy of AE based crack tracking, the crack tip of C4 can be tracked with ahigher accuracy than the bottom part of the crack. If assuming the center of AE clustering as theS163crack center, the bottom part of the crack C4 was located with an error of around 16 cm (twocells) to the left of DIC result, shown in Figure 6, left. This error came from the influence of thesecondary crack between the source and the receiver, combined with the influence of arrival
time picking error from the threshold crossing method. More cracks between the source and thereceiver can result in larger crack tracking errors. Additional considerations need to be taken.

5. CONCLUSIONSThis paper explores the possibility of using local cumulative AE activities during unloading to
track the crack pattern in reinforced concrete beams with no further destruction. By comparing
with the physical damage in terms of crack opening measured by DIC, several conclusions canbe drawn:
 In the performed test in this paper, the local cumulative AE activities during unloading isable to track the trajectory of the cracks with the maximum normal opening larger than
0.1 mm. The closure of narrow cracks less than 0.1 mm may not be detected by thesensors within 0.5 m.
 With an existing crack between the source and the receiver, the accuracy of the AE
based cracking tracking has a reduction of around 16 cm. With multiple cracks,
additional considerations have to be taken when applying this method.
 The local cumulative AE activities during unloading turns out to be proportionally related
to the local normal crack opening from previous loading. The relationship between the
cumulative AE activities and the crack opening in a local scale will be quantified inanother paper with more test data from various beams.
6. REFERENCE LINKING
1.
2.
3.
4.
5.
6.

7.
8.

Grosse, C.; Ohtsu, M. Acoustic emission testing: Basics for research-applications in civilengineering. 2008; p 1-404, 3540698957.
Schechinger, B.; Vogel, T. Acoustic emission for monitoring a reinforced concrete beam
subject to four-point-bending. Construction and Building Materials 2007, 21, 483-490.
Yang, Y.; Hordijk, D.A.; de Boer, A. Acoustic emission measurement in the proof loadingof an existing bridge affected by asr. In 5th International Symposium for Life-Cycle Civil
Engineering, Delft, 2016.
Yang, Y.; Hordijk, D.A.; de Boer, A. Acoustic emission study on 50 years old reinforced
concrete beams under bending and shear tests. In 8th International Conference on
Acoustic Emission, Kyoto, 2016.
Ohtsu, M.; Uchida, M.; Okamoto, T.; Yuyama, S. Damage assessment of reinforced
concrete beams qualified by acoustic emission. ACI Structural Journal 2002, 99, 411-417.
Ohtsu, M. Recommendation of rilem tc 212-acd: Acoustic emission and related nde
techniques for crack detection and damage evaluation in concrete: Test method fordamage qualification of reinforced concrete beams by acoustic emission. Materials andStructures/Materiaux et Constructions 2010, 43, 1183-1186.
Tobias, A. Acoustic-emission source location in two dimensions by an array of three
sensors. Non-Destructive Testing 1976, 9, 9-12.
Zhang, F. Evaluation of acoustic emission monitoring of existing concrete structures.
Master Thesis, Delft University of Technology, the Netherlands, 2017.

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Radar Tower Inspection using Omnidirectional Shear
Acoustic Emission Sensors
Jason Philtrona, Ronnie K. Millerb, Cody Borigoa, Steven Owensa, and Ward FongcaGuidedwave, 450 Rolling Ridge Dr., Bellefonte, PA, USA;b
Independent AE Consultant, Loveland, OH, USA;b
United States Air Force, Hill AFB, UT, USA
ABSTRACTA new, patent-pending, omnidirectional shear wave Acoustic Emission (AE) sensor has beenvalidated in field trials on steel radar tower structures. This work was conducted under an ongoing Phase II SBIR1 to develop an AE inspection method for radar tower structures. Agencies
such as the US Air Force, Federal Aviation Administration (FAA), and National Oceanic andAtmospheric Administration (NOAA) have many such steel tower structures which support
rotating radar arrays. Many of these structures have seen continuous use for decades and havereceived a minimal level of maintenance and inspection. Historically, visual inspection has beenrelied upon for flaw detection, but this method can be unreliable as small or hidden flaws caneasily be missed. AE inspection provides a 100% coverage inspection solution. New
omnidirectional shear AE sensors have been used in tandem with conventional sensors. The
shear sensors have been validated through detection of cracks confirmed with visual andmagnetic particle testing. The shear sensors can also provide an advantage for sensing on
exposed structures during environmental noise such as rain which can cause false calls or
overwhelm data throughput rates with conventional sensors. Additionally, the sensors provide
increased source localization performance with similar sensitivity as conventional sensors,depending on the AE source type. For conventional AE sensors, wave velocity must be
determined in the field for each test setup. However, with shear sensors users do not need tocalibrate and can rely on handbook values (bulk shear wave velocity for a material) to produce
excellent results.
Keywords: shear, crack, localization, characterization, sensors

1. INTRODUCTION
Reliability of radar tower structures is essential for military and civilian aircraft operation [1].
Agencies such as the US Air Force, Federal Aviation Administration (FAA), and National Oceanicand Atmospheric Administration (NOAA) have many such steel tower structures which support
rotating radar arrays. Radar operation is typically continuous, and some tower structures havebeen in use for 50 years, or longer. Damage (e.g., cracks, corrosion) to steel tower structures
leads to decreased structural strength which can eventually result in catastrophic failure.
Structural failure will result in radar downtime, as well as loss of expensive equipment. In addition
to the need for 100% coverage of the structure, areas with limited access need inspection, which
can be challenging for other conventional NDT techniques. AE inspection can provide a fast,
reliable, and robust method for tower inspection. Radar rotation
1

The work described in this presentation is part of a Phase II, Small Business Innovating Research program with the
U.S. Air Force and Guidedwave. The topic number is AF161-11 and the title of the project is, “Hybrid Acoustic Emission
and Guided Wave Inspection System for Radar Towers”. Reference CASE NUMBER 75ABW-2019-0030.
9th International Conference on Acoustic Emission

S165with an offset center of mass provides a natural loading function that can be used during
inspection, and it has been found that electromagnetic interference (EMI) from radar operation is
not of concern for shielded cables and sensors.
Initial field tests of a novel, patent-pending [2,3] omnidirectional shear AE sensor on radar tower
structures show promise for increased source location accuracy and insensitivity to environmental
noise (rain) when compared to conventional (Lamb-type wave sensitive) sensors, while still
maintaining sensitivity to emissions from cracks. Improvements to source location accuracy wereachieved using threshold triggering on the main wave packet arrival, as opposed to conventional
methods that typically perform complicated waveform analysis in post-processing to achieve
improved source location accuracy [4]. Additionally, there is no need to spend time performing avelocity calibration for the shear sensors, since the shear wave velocity is always used. These
shear sensors were used in parallel with conventional 150 kHz resonant sensors in several fieldtests of steel radar tower structures. This paper presents data from multiple tower structurescontaining real flaws.

2. RADAR TOWER GEOMETRY
Radar tower structures are typically constructed of structural steel and have 3, 4, or more support
legs connected by a truss structure. The distance between adjacent legs may be as few as 6 feetor as much as 50 feet, depending on the design. Additionally, tower height can range from 25 feetto 50 feet or more. The rotating radar array is housed inside a radome mounted on top of thetower structure. Images of several radar tower designs are shown in Figure 1.

Figure 1. Example radar tower structures, from left to right: Air Force tower, FAA tower [5], and NOAA
tower [6].

3. RESULTS – CRACK DETECTION
3.1 Example location error with conventional sensors
Location calculations using conventional acoustic emission sensors can be prone to error for avariety of reasons. In some cases, the amplitude of emissions from a source may vary during thetest period. This can cause significant variation in where along a waveform the software sets thestarting point used for delta-t calculations. Due to multi-modal and dispersive mode content, a
fixed threshold can easily trigger off of a variety of locations in the signal, from the first wave arrival
to near the maximum signal amplitude.
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An example of such an error is shown in Figure 2. Data was recorded for 40 minutes during normal
radar tower operation by two conventional 150 kHz resonant sensors placed on legs of a steel
radar tower structure connected by a horizontal I-beam. This beam was welded to each leg.
Software with a floating detection threshold of 40 dB with an FTband of 20 dB was used and avelocity of 90,000 in/sec. Emissions were detected from a visually-confirmed 2-inch long crack in
one of the welds. Although the sensor spacing is only 5.5-feet, calculated locations from AE
signals were spread over a range of 15 inches, or almost 25% of the sensor spacing. This levelof location error can be avoided using shear wave acoustic emission sensors.

Figure 2. AE data from a 2” crack collected using conventional 150 kHz resonant sensors. Significant
variation in calculated location is shown.

3.2 Example of crack detection with omnidirectional shear sensors
In order to characterize the crack identified in Figure 2, to determine its precise location, and toassess the capability of the shear sensor to detect emissions from a crack, a set of two shear(200 kHz resonant) and two conventional (150 kHz resonant) sensors were placed on either sideof the crack, with a sensor spacing of 28 and 30 inches, respectively. A floating threshold startingat 30 and 40 dB with an FTband of 20 dB was used for the shear and conventional sensors,
respectively. Data was collected for 30 minutes during normal radar operation. Results are shownin Figure 3.
The shear and conventional sensors show very similar sensitivity in terms of the energy andamplitude of the recorded waveforms. When comparing the 26 events recorded by the shear
sensors to the time-linked events recorded by the conventional sensors, the shear sensors haveon average 3 dB greater amplitude. This is a promising result, as it was not known prior to fieldtesting whether the shear sensors would have similar sensitivity to true cracks. It is expected thatthe relative shear horizontal and Lamb-type guided wave mode energy generated by a crack will
be dependent on the type of crack and its relative orientation to the sensors.
The true crack location is close to 171 inches, indicating that the shear sensors provide superior
location accuracy. The input velocity for the shear sensors is 123,000 in/sec, which is simply thebulk shear wave velocity of the steel. This velocity can be used because the shear sensors are
sensitive to the fundamental shear horizontal guided wave mode, which is non-dispersive andhas a phase and group velocity equal to the bulk shear wave velocity that is independent ofstructure thickness and frequency. Use of the shear wave velocity also saves time and limits
confusion, as no calibration procedure is required during sensor array setup to determine thevelocity to use for location calculations (unlike for conventional sensors). One of the reasons for
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the location error experienced by conventional sensors in this case could be that the wave musttravel through multiple thicknesses of the steel structure, for which propagating guided waves will
generally have different group velocities. In contrast, the fundamental shear horizontal wave
velocity is insensitive to structure thickness.
Additionally, the location data from the shear sensors curiously shows two location clusters, which
is believed to be due to the two different paths around the circumference of the tower leg (pipe)
between the crack and one of the sensors. The time of arrival data indicates that the groups ofhits locating to the two different positions occurred at different relative times during the radararray’s rotation cycle. This suggests that different source mechanisms during the rotation cycle
generated ultrasound in different relative directions which resulted in the emissions located fromthe two wave paths.

Figure 3. AE data from a 2” crack collected using shear 200 kHz resonant (above) and conventional 150
kHz resonant (below) sensors. The crack location is near 171”, indicated by the vertical dashed line. The
amplitude and energy of AE signals recorded by the two sensor types is similar, but the shear sensors
provide superior location performance.

3.3 Example of small flaw detection with omnidirectional shear sensors
Additional data was collected on a separate section of a steel radar tower. In this case,conventional and shear sensors were placed vertically along a section of pipe which served as a
tower leg. This section of pipe had multiple welded cross-beams, and an indication was detectedusing AE. This indication was not confirmed visually as a crack, but an undercut in a weld area.
The sensor spacing was 30 inches for both the shear (200 kHz resonant) and conventional (150
kHz resonant) sensors, and the two sensor types were offset 4 inches vertically. A floating
threshold starting at 30 and 40 dB with an FTband of 20 dB and velocities of 123,000 and 90,000
in/sec were used for the shear and conventional sensors, respectively, and data was collected for
30 minutes during normal radar operation. AE data is shown in Figure 4.
Conventional sensors recorded 123 events during the recording period. These events cluster toan area of ± 2.25 inches, representing ±7.5% of the sensor separation distance. The shear
sensors recorded 134 events during this recording period. About 120 of these events were timelinked to the events detected by the conventional sensors, indicating that it is highly likely that thesame source was producing all of the detected emission events. The event locations calculatedby the shear sensors cluster to an area of ±0.25 inches, representing ± 0.8% of the

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sensor separation distance. Additionally, the location of the undercut nearly matches the location
calculated by the shear sensors. The shear sensors show improved location accuracy andprecision.
In this case the shear sensors show less sensitivity (in terms of recorded signal energy andamplitude) to the detected indication/flaw. However, since the flaw could not be confirmed visually
or by another NDT method, no additional characterization of the flaw type is given here, and nofurther generalizations can be made about the relative sensitivity to flaws by the two sensor types.
It is hypothesized that much of the error in the location calculation when using conventional
sensors is due to triggering from different parts of the wave signal. The conventional sensors
recorded a more gradual ramp up of signal amplitude, due to the multi-modal and dispersive
nature of the wave propagation, where the arrival of multiple wave packets occurs at different
times. In contrast, the shear sensors detect the arrival of a single, distinct wave packet. Example
waveforms are shown in Figure 5 and Figure 6 for shear and conventional sensors, respectively.
The signals shown were generated by an acoustic emission event that was received by both setsof sensors at nearly identical times.

Figure 4. AE data for conventional (left) and shear (right) sensors on a steel tower leg. AE signals indicate
damage at a welded cross-beam with a weld undercut, but no visible cracking was identified. The undercut
location is near 208-210”, indicated by the horizontal dashed box.

Figure 5. Waveforms recorded by the two shear sensors from a time-linked event also recorded byconventional sensors. The shear sensors show a distinct wave packet arrival. The left and right plots show
the arrival at the first and second sensor, respectively.

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Figure 6. Waveforms recorded by the two conventional sensors from a time-linked event also recorded byshear sensors. The conventional sensors show a more gradual increase in wave amplitude as multiple
wave packets arrive at different times. The left and right plots show the arrival at the first and second sensor,
respectively.

4. RESULTS – ENVIRONMENTAL NOISE (RAIN)
Data assessing the effect of rain was collected on the Air Force tower shown in Figure 1. The
external tower structure was instrumented with 14 conventional 150 kHz resonant sensors whichspanned the entire north side of the external tower structure. One 200 kHz resonant shear sensor
was collocated on the structure next to a conventional sensor. A fixed threshold of 40 dB wasused for all of the conventional sensors and a fixed threshold of 25 dB was used for the shear
sensor.
During a 10-minute period of light rain at 39°F, the conventional sensors collectively recorded 51
hits. The shear sensor recorded no hits. The conventional sensor collocated with the shear sensor
recorded 11 hits, with a maximum amplitude of 50 dB. The lack of hits recorded by the shear
sensor during rain demonstrates a key advantage of using a shear sensor in outdoor
environments. Many radar tower structures are external, and data collection during storms, whenwind loading is highest, is of particular interest since crack growth is expected during the highest
loading events. Beyond tower inspection, other applications such as bridge health monitoring andpower transformers could benefit from sensors less sensitive to rain, as typically monitoring is
stopped or the data is discarded during periods of rain. When using shear sensors, it may bepossible to allow continuous monitoring through storm events.

5. CONCLUSIONS AND RECOMMENDATIONS
Initial field tests of new omnidirectional shear AE sensors show promise for increased localization
accuracy and decreased sensitivity to environmental noise. Additional study is needed to further
characterize the sensors’ benefits and extend field testing onto new structures and into new
applications. The shear AE sensors show promise as replacement or companion sensors,depending on the application. In future work, the potential to improve source characterization will
be assessed when jointly using conventional and shear AE sensors. Source characterization may
be improved through analysis of wave information from all three fundamental guided wave mode
types.

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6. REFERENCES
1. SBIR Solicitation topic number F161-011, “Acoustic Emission of Frangible, Composite,
Concrete and Metallic Radar Towers,” SBIR / STTR Website, Accessed Jan 15, 2019,https://www.sbir.gov/sbirsearch/detail/870157.
2. C. Borigo, S. Owens, and J.L. Rose, “Piezoelectric Shear Rings for Omnidirectional Shear
Horizontal Guided Wave Excitation and Sensing,” U.S. Patent 20170030848 A1 issued
February 2, 2017.
3. J. Philtron, C. Borigo, S. Owens, and R. Love, “Shear Wave Sensors for Acoustic Emissionand Hybrid Guided Wave Testing,” U.S. Patent Appln. 16/033,911 filed July 12, 2018.
(Provisional 62/531,663 filed July 12, 2017.)
4. Acoustic Emission Testing, Volume 6, 3rd Edition, NDT Handbook Series published by
ASNT, Ronnie K. Miller and Eric v.K. Hill Technical Editors (2005).
5. FAA radar (Bennett, Colorado).JPG, Jeffrey Beall, Creative Commons Attribution 4.0
International
license,https://commons.wikimedia.org/wiki/File:FAA_radar_(Bennett,_Colorado).JPG. Accessed January 15, 2019.
6. Wea01195 - Flickr - NOAA Photo Library.jpg, Eric Kurth, Creative Commons Attribution
2.0 Generic license, https://commons.wikimedia.org/wiki/File:Wea01195_-_Flickr__NOAA_Photo_Library.jpg. Accessed January 15, 2019.

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A new Time-domain Feature called the Really Keen Metric (RKM) for
Acoustic Emission Transient Signal Processing1
Ronnie K. Millera, Jason Philtronb, Cody Borigob, and Steven Owensba
AE Consultant; bGuidedwave, Inc., Bellefonte, PA
Abstract:
A series of dynamic tests were performed where certain equipment was monitored with Acoustic
Emission (AE). Resonant AE sensors were used and coupled to MISTRAS AE instrumentation.
The recorded data comprises data files with 8,000 to 10,000 Hits recorded over a very short periodof time <15 seconds). Of interest during these tests were AE signals that are truly indicative oftransient signals that have a clear beginning and end and an exponential decay after the peak
amplitude is observed. Unfortunately, the data sets were contaminated with considerable
amounts of extraneous emission that could only be filtered by manually examining the waveform
collected for each Hit. The use of correlation plots to separate these AE signals was helpful to acertain extent but through the process of manual data filtering, it became clear that a well-definedtime-domain feature could be very efficient in separating the transient signals from extraneousemissions. This paper presents a concept for a new time-domain feature that is capable of better
defining transient-like AE signals as defined above. This feature is called the Really Keen Metric(RKM) and will be presented with actual examples recorded during the above testing.
Keywords: Waveforms, Transient-like, Non-transient-like, Data Filtering, Exponential Decay

1. Introduction:
In most AE applications, signal
processing is based on the assumptionthat you will be working with a transient
shaped signal (volts vs time) that has aclear beginning and end when compared
to a detection threshold. In addition,
early in the signal, the amplitude rises toa peak and then decays exponentially
until the signal reaches the backgroundnoise level. A typical signal is shown in
Figure 1.
A typical transient signal can be
described with a set of time-domainfeatures that can be extracted from asignal like the one shown in Figure 1.
These features include: Rise Time, peak
Amplitude, Duration, Counts, andFigure 1: Example of a transient AE signal.

1

NAVAIR SBIR, Phase II Topic N171-011, Contract # N68335-18-C-0520,“Acoustic Emission andUltrasonic Inspection System for Advanced Arresting Gear Water Twister”.

9th International Conference on Acoustic Emission

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Energy (or Absolute Energy). In theory, different AE sources create different AE signals when
detected by a sensors. The shape of the signal is dictated by the source function, wave
propagation, and the characteristics of the sensor that detects the elastic wave that was
generated by the AE source. On occasion, correlation plots of these features can be used todifferentiate different AE sources. However this is not true in all cases as one sometimes finds
signals from extraneous emissions mixed in with the signals of interest.
This paper looks at several cases of signals that were recorded during actual testing of realequipment. Attempts to separate the different signals using the correlation plot approach failed
pointing out the need for some other means for separating these signals. The solution was todefine a new time-domain feature that could be extracted from the AE signal that provides anindication that the signal is transient in shape by means of a decay measurement. The newfeature is called the Really Keen Metric (RKM).
2. Test Results:
A series of tests were performed on real equipment as it was being used in the field. A descriptionof the equipment and how it is used is not important in this case. What is important is thatconventional resonant transducers were being used and waveforms were being recorded forevery Hit detected. The stimulus involved changed dramatically over the course of the test
resulting in signals that saturated (around 100 dB) to signals that were barely detectable using a
30 dB detection threshold.
An example of a desirable transient signal is shown in Figure 2. A “good healthy one” is shownon the left with a “low-amplitude questionable one” on the right.

Figure 2: Examples of a “healthy” transient signal (left) and a “low-amplitude questionable”
one.
In contrast to the transient-like signals shown in Figure 2, many signals were recorded thatappeared quite different as shown in Figure 3. This signal is mostly saturated leading you tobelieve that the time-domain features extracted from this waveform could easily be used toS173

Figure 3: Example of saturated signal recorded during the same test.
differentiate it from the transient signals shown in Figure 2. That’s not the case. More examples
are shown in Figure 4 where once again, the time-domain features could not be used to easily
differentiate these signals from the desirable transient-like features.

Figure 4: Examples of other extraneous signals recorded during the same test.

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In truth, there are no time-domain features defined that focus on the transient shape of the
traditional AE signal. Especially the exponential decay that is seen after the peak amplitude isreached. This led to an effort to establish such a feature as described in the following.
The first approach focused on rectifying the signal first. Next, the peak amplitude was identified
and then 20 kHz low pass filtering was applied from that point on. The results are shown in Figure
5 for the two transient signals shown in Figure 2. The decay is shown in the graph title in units ofµV/µs. It can be seen that the decay values range from about 1.15 to 164 µV/µs. The sameprocessing was applied to the AE signals shown in Figure 3 and 4 with the results shown in Figure
6. Decay values range from 0.126 to 6,517 µV/µs.

Figure 5: The transient-like signals from Figure 2 after rectification and 20 kHz low passfiltering from the peak amplitude to the end of the waveform.

Figure 6: The non-transient-like signals from Figure 3 and 4 after rectification and 20 kHz
low pass filtering from the peak amplitude to the end of the waveform.

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Based on the results shown above, this method appeared to have too much spread in the values
for decay measured in units of µV/µs. As a result, another approach was investigated were therectified signal was converted into a logarithmic scale with the same low-pass filtering applied.
The results are shown in Figure 7 for the transient-like signals and Figure 8 for the non-transientlike signals. Decay values range from 0.00417 to 0.00872 dB/µs for the transient-like signals andfrom -0.00134 to 0.00139 dB/µs for the non-transient-like signals.

Figure 7: Results after logarithmic conversion, filtering and averaging from the peak
amplitude to the end of the waveform for transient-like AE signals.

Figure 8: Results after logarithmic conversion, filtering and averaging from the peak
amplitude to the end of the waveform for non-transient-like AE signals.

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This type of processing was applied to AE signals produced by Electro Magnetic Interference(EMI) as it is expected to produce a much higher decay rate. The results for the signal shown in
Figure 9 (left) are shown in Figure 9 (right) where the decay rate is estimated at 1.4896 dB/µs.

Figure 9: EMI waveforms (left) and results of the proposed signal processing (right).

3. Conclusions and Recommendations:
Based on the results of this study, it was determined that transient-like AE signals can beseparated from non-transient-like AE signals by virtue of the decay observed in the signal. Thiswas done by processing the portion of the waveform where the peak amplitude is observed to theend of the waveform. The processing first requires rectification of the AE signal and then 20 kHz
low pass filtering. Then the rectified and filtered signal in converted into a logarithmic scale sothat the decay from the peak amplitude to the end of the waveform can be calculated. This decay
feature is called the Really Keen Metric (RKM) and it is recommended that the commercial
suppliers of AE instrumentation add this time-domain feature to the list of currently available
features that can be extracted from the AE signal.
Ideally, this will lead to faster real time and posttest filtering for separating transient-like and nontransient-like AE signals. In addition, it remains to be seen how useful the RKM feature may bein terms of the mechanical properties of the material being monitored or the performance of the
AE sensor used.

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Abstracts of Papers Submitted
Burst pressure prediction of composite vessels
Markus Sause1, Stefan Schmitt1, Birte Hoeck2, Andreas Monden1
1

University of Augsburg, Germany
2
MT Aerospace AG, Germany

We propose an acoustic emission based method to predict the local stress exposure of a fiberreinforced material. This approach uses a stepwise increased load profile as common in testingprocedures of fiber-reinforced pressure vessels. We apply an artificial neural network to establish
a relationship between acoustic emission criteria evaluated for each load cycle and the globally
applied load ratio. The acoustic emission training database for this neural network is obtained fromlab-scale experiments, such as tensile tests, bending tests, double-cantilever-beam tests, endnotched-flexure tests and lap-shear tests. We present the application of this neural network to burst
tests of three small-scale pressure vessels (560 mm length) and two large-scale pressure vessels(2600 mm length) for burst pressure prediction. In all cases, the deviation between predicted andmeasured burst pressure is less than 3.0 %, with a maximum prediction uncertainty of 9.8 %. In
addition, we present a segmentation technique, which allows evaluating the acoustic emission
criteria for sub-volumes of the full structure. This is based on precise source localization of theacoustic emission signals using artificial neural networks. The local prediction of load ratios allows
evaluating the stress exposure of the structure as function of the applied load cycles. The resulting
concentrations of stress exposure are compared to in situ camera analysis during the tests, strain
gage measurements and post mortem analysis of the vessels. In all cases, good agreement withthe prediction of the local load ratio is found.
Keywords: Burst pressure, Failure prediction, Felicity Ratio
Using machine learning to predict welding quality from airborne AE sensor data
Kaiser Asif1, Lu Zhang1, Sybil Derrible1, Ernesto Indacochea1, Didem Ozevin1, Brian Ziebart1
1

University of Illinois at Chicago, United States

Current weld monitoring and evaluation processes are usually conducted in the post-weld stage.
In this way, defects are found after the weld is completed, often resulting in disposal of theexpensive material or lengthy repair processes. Simultaneously, weld quality inspections tend tobe performed manually by a human, even for an automated weld. Therefore, a proper real-time
weld quality monitoring method associated with decision-making strategy is needed to increase theproductivity and automaticity in weld. In this study, acoustic emission (AE) as a real-time monitoring
method is introduced for gas metal arc weld (GMAW). Several types of AE sensors (R6, WD, R15
and R1.5) are used to cover all possible frequency ranges from 5 kHz to 400 kHz. Additionally, thewelding parameters (weld current, voltage, gas flow rate and heat input) are also recorded during
the welding process. Different types of weld are artificially created to generate different signals. For
the automated decision-making system, machine learning algorithms are used. Several features
extracted from the AE and welding parameter monitoring system feed into a machine learningalgorithm. For decision-making, we train supervised learning models and evaluate theirperformances on unseen data. General machine learning models for classification e.g. Logistic
Regression predict each data-point separately. In this work, we also show that considering theprediction task as a sequence tagging problem provides better performance. We use our own
developed method for sequence tagging in this work. Finally, we compare different feature
combinations and models and present the analyses of the automated welding quality prediction
system.
Keywords: Welding, airborne AE, machine learningaPhased Acoustic Emission Sensor Array for Determining Radial and Axial
Position of Defects in Pipe-like Structures
Lu Zhang1, Chen Enpei1, Gorkem Okudan1, Didem Ozevin1
1

University of Illinois at Chicago, United States

The Acoustic Emission (AE) method has been successfully applied to detect and pinpoint horizontal
position of defects in pipelines. The AE method relies on propagating elastic waves triggered by
the turbulence flow due to the internal pressure release at the leak location or sudden burst due tocrack growth or impact. Considering the ratio of length to cross-section in pipe-like structures, thesource localization is considered as one-dimensional (1D) problem. In this paper, AE sensor arrayis positioned around the circumference of pipe. The sensing orientation of each neighbor sensor isdesigned as longitudinal and transverse directions in order to determine the axial location of defectfrom the arrival time differences as longitudinal and transverse modes have two distinct wave
velocities. Numerical simulations are performed to determine the influence of sensor-source
distance in radial coordinate system such that actual coordinate of defect location can be
determined. The influence of source-sensor distance is studied by varying the axial and radial
position of the simulated source. The numerical results are tested on a laboratory-scale steel pipe.
Crack growth and leak are simulated using pencil lead break and orifice, respectively. In
conventional AE, each channel detects AE signals independent from each other. The synchronized
AE sensing is studied to determine the influence of trigger approach to determine the time of flight.
Keywords: Pipe, phase-array AE, localization
Development of Optical Fiber AE Sensor by FBG with WDM
Hisafumi Asaue1, Tomoki Shiotani1, Katsufumi Hashimoto1 ,Iori Yamamoto2, Hidehiro
Inaba2
1
Kyoto University, Japan
Fuji Ceramics Corporation, Japan

2

Electric sensor of AE method have some problem. For example, all weight become heavy if thenumber of cable connected sensor to equipment increase. It is difficult to embed the sensor basedon long-term use due to deterioration or breakdown. The sensor is affected by electrical noise. To
solve these problem, AE sensor consisted of FBG was developed by irradiating ultraviolet light tooptical fiber. Since the optical fiber has characteristics such as light weight, non-inductivity ofelectromagnetic interference noise, insulation, explosion resistance, wide band and low loss of
signal transmission, the above sensor is resistant to electrical noise, low failure rate, usable evenunder high or low temperature conditions, and acquisition of high reliability data in long time use.
The optical AE sensor can be measured data 800 kHz sampling time using Wavelength Division
Multiplexing. As a result of elastic wave waveform with good signal-to-noise ratio could bemeasured by interrogator capable of high-speed measurement and performing filtering processing.
Keywords: Optical fiber, FBG, WDM, Elastic wave, Filtering processing
The material condition evaluation of pressure vessels after long-term
operation in the oil refining processing using acoustic emission method andstructure stress field analysis
Igor Lyasota1, Marek Nowak2, Lukasz Sarniak3, Pitr Kustra4
1

Cracow University of Technology, Poland
2
Office of Technical Inspection, Poland
3
Warsaw University of Technology, Poland
4
AGH University of Science and Technology, PolandbDifferent types of pressure equipment are widely used in oil refining and petrochemical processing.
A lot of these were made from carbon steels and have been used for a few decades. Long-term
operation under extreme conditions causes defect initiation and growth in the microstructure of theequipment materials. The defects, which grow due to work loads, may damage the entire structure
and may lead to the failure of the entire industrial unit. Consequently, the development of new nondestructive evaluation methodologies which allow the assessment of the condition of materials
comprising the pressure equipment after long-term operation in the refinery industry is very
important. The methodologies development is possible as a result of complex destructive laboratory
tests combining different experimental techniques. The paper presents results obtained from thecomplex destructive tests with acoustic emission (AE) recording on real pressure vessel (capacityof 2.6 m3) after long-term operation in the refinery industry. The vessel has been operated in crude
oil distillation unit for over 40 years. A lot of corrosion damages were present on the inner surfaceof the vessel shell as a result of long-term operation. The material’s microstructure of some shellplates was coarse-grained, which arose from improper heat treatment during plates manufacturing.
The hydraulic pressure test with AE recording of the pressure vessel was shown that AE method
allowed to detect of local plastic deformation processes and early stage of crack initiation in
overheated and corrosion degraded shell’s material. The results of the conducted investigations
form the basis for the developing of a complex methodology for assessment of the technicalcondition of pressure equipment in the refinery industry. The investigations, presented in this paper,
were realised within the project of LIDER VII Programme financed by the National Centre for
Research and Development of Poland.
Keywords: Acoustic emission; pressure equipment, material degradation, carbon steel
A comparison study of Vibro-Acoustic Modulation technique and Conventional AE
Behnoush Golchinfar1, Majid Ramezani Goldyani2, Hossain Saboonchi1, Dimitri Donskoy2
1

MISTRAS Group, Inc., United States
Stevens Institute of Technology, United States

2

The necessity of safety diagnosis of structures has recently been emphasized due to the aging of
public PSC bridges over 20 years. Tendons consist of external tendon and internal tendon type.
External tendon types are already undergoing inspection and repair through visual inspection andgeneral non-destructive methods in case of problems. However, internal tendon-type PSC bridges
are buried in the concrete filled with grout to prevent corrosion of stranded strand, and after thesevere damage caused by corrosion, there is no inspection method that can be confirmed. Acoustic
emission technology has attracted attention recently as a technology capable of diagnosing defectsof large structures and locating defects. The acoustic emission technique was used to detect the
failure state of the internal tendon of the PSC bridge and to analyze the defect signal transmitted
through the tendon to find the degree of damage and the location of the defect. The purpose of thisstudy is to evaluate the integrity of the tendon by distinguishing the general signal from the concreteand the defect signal from the internal tendon. Average frequency and RA value were used asparameters for fault diagnosis and analysis. In addition, RTRI and Ib Value were applied for theevaluation of defectiveness and Integrity. Finally, in this study, it was confirmed that diagnosis ofinternal tendon of PSC structure is possible by analyzing the acoustic emission characteristic signal
generated in the PSC structure at the time of load application.
Keywords: Nonlinear Ultrasonic, Acoustic Emission, Vibro-Acoustic Modulation TechniqueFar-field wave propagation signals from pencil lead breaks on the open end of a
thick-walled layered cylinder: Part 1 Amplitudes and frequency content*
Marvin Hamstad1
1

University of Denver, United StatescRelative to potential use of acoustic emission (AE) to monitor pressure testing of large steelpressure vessels, in-plane pencil lead breaks (PLBs) were carried out on the open end of a remnantof such a vessel. This remnant had an inner diameter of 58 in. (1473 mm), a wall thickness of 3.3
in. (84 mm) and a total cylindrical length of 125 in. (3175 mm) ending in a head welded to thecylindrical section. The cylindrical section did not have a monotonic wall, but instead it was madeof 11 layers (10 at about 0.277 in. [7 mm] thick, and the inside one of about 0.47 in. [11.9 mm]
thick) to create a layered pressure vessel (LPV). Due to the presence of the multiple interfaces
between layers, a question arose as to whether a sensor mounted on the outer layer was relatively
less sensitive and had different frequency content for sources on the inner layers compared tosources on the layers? The PLB study used four pairs of different types of AE sensors. The sensor
types included: i) strong resonance at a single frequency region; ii) multiple resonance frequencies;iii) and iv) broadband response with different frequency responses. Each sensor pair was
acoustically coupled, one on the outside surface and one directly below on the inside surface. The
sensor pairs were located at 66 in. (1676 mm) (20 times the wall thickness) from the PLB points.
This distance was considerably less than the helical path of about 214 in. (5436 mm). Considering
PLBs on the ends of the different layers, part 1 focused on changes in the character of the AE
signals from the four sensor types. Comparisons for each sensor type were made of the amplitudesof the outside sensor relative to those of the inside one. In addition, as the PLB was moved todifferent layers, the changes in the signals and frequency content of the different sensor signals
were studied. Except for one sensor type, the outer sensor peak signal amplitudes relative to theinner sensor peak signal amplitudes changed significantly as the PLB layer moved from the outerlayers to the inner layers. In addition, the signals and frequency content also varied with changes
in the PLB layer. The results represent a lower bound due to the potential for some increasedtransmission between layers when a LPV is pressurized to significant levels.
* The author is grateful for the funding, purchase of equipment, facilities and excellent experimental
support provided by NASA Marshall Space Flight Center, Huntsville, AL, USA.
Keywords: Thick-walled layered cylinder, pencil lead breaks, four sensor types, inside and outside
sensors, wave propagation, amplitudes, frequency content.
Far-field wave propagation signals from pencil lead breaks on the open end of
a thick-walled layered cylinder: Part 2 Modes of wave propagation*
Marvin Hamstad1
1

University of Denver, United States

Relative to potential use of acoustic emission (AE) to monitor pressure testing of large steelpressure vessels, in-plane pencil lead breaks (PLBs) were carried out on the open end of a remnantof such a vessel. This remnant had an inner diameter of 58 in. (1473 mm), a wall thickness of 3.3
in. (84 mm) and a total cylindrical length of 125 in. (3175 mm) ending in a head welded to thecylindrical section. The cylindrical section did not have a monotonic wall, but instead it was madeof 11 layers (10 at about 0.277 in. [7 mm] thick, and the inside one of about 0.47 in. [11.9 mm]
thick) to create a layered pressure vessel (LPV). Due to the presence of the multiple interfaces
between layers, a question arose as to whether a sensor mounted on the outer layer was relatively
less sensitive and had different frequency content for sources on the inner layers in comparison tosources on the outer layers? The PLB study used four pairs of different types of AE sensors. The
sensor types included: i) strong resonance at a single frequency region; ii) multiple resonancefrequencies; iii) and iv) broadband response with different frequency responses. Each sensor pair
was acoustically coupled, one on the outside surface and one directly below on the inside surface.
The sensors were located at 66 in. (1676 mm) (20 times the wall thickness) from the PLB points.
This distance was considerably less than the helical path of about 214 in. (5436 mm). Considering
PLBs on the ends of the different layers, part 2 focused on identification of Lamb modes of wave
propagation in the AE signals from the four sensor types. In addition, by use of superimposed groupvelocity curves on frequency versus time results, the peak intensity in the frequency versus time
domain for the outside sensor types was studied relative to determination of the presence ofdominant full thickness propagation or dominant single layer propagation. For PLBs on the outerdmost layers, both single layer propagation and full thickness propagation were present in some ofthe outer sensor signals.
* The author is grateful for the funding, purchase of equipment, facilities and excellent experimental
support provided by NASA Marshall Space Flight Center, Huntsville, AL, USA
Keywords: Thick-walled layered cylinder, pencil lead breaks, four sensor types, inside and outside
sensors, wave propagation, Lamb modes
Self-Healing Degradation of Asphalt Concrete Materials due to Cooling Cycles
Behzad Behnia1, Henrique Reis2
1

Department of Civil and Environmental Engineering,106 William Rowley
Laboratories, Clarkson University, United States
2
University of Illinois at Urbana-Champaign, United States

Asphalt concrete is one of the most widely used materials in transportation infrastructure, covering
the surface of approximately 94% of more than 4 million miles of highways in United States. Asphalt
pavements located in cold regions or in milder climate areas with large daily temperaturefluctuations encounter thermal cracking manifesting itself in the form of several transversely
oriented surface-initiated top-down cracks of various lengths and widths. Self-healing as an intrinsic
property of asphalt materials mitigates the thermal cracking process in asphalt pavements andextends pavements’ service life. However, self-healing properties of asphalt pavement graduallydiminishes with aging. The present study utilizes the Acoustic Emission (AE) technique and thefracture-based Disk-shaped Compact Tension (DCT) test to provide quantitative assessment ofself-healing of thermally-induced damage in asphalt concrete materials at the micro (AE test) as
well as macro (DCT test) levels. Asphalt concrete specimens were subjected to eight controlled
cooling cycles and effects of resting, i.e., dwell time, between cooling cycles on self-healing were
investigated. The Felicity effect phenomenon was observed in this study indicating occurrence ofself-healing in the material. The AE test results showed gradual degradation in self-healing
capability of asphalt mixtures as the material was exposed to cooling cycles. However, it wasobserved that the rate of self-healing degradation was not constant as it was higher at the beginningand then gradually reduced until it reached almost zero after the fourth cooling cycle. Moreover,
AE results also indicated that the 12 hours dwell time between cooling cycles significantly increased
the self-healing by more than 30% and allowed the material to regain most of its self-healing
capabilities. DC(T) fracture energy results were consistent with the findings from AE test. Results
also demonstrated that, on the average, 12 hours resting time increased fracture energies ofasphalt mixtures by about 13%.
Keywords: Asphalt concrete; thermal cracking; self-healing; Acoustic Emission; disk-shaped
compact tension; cooling cycles; Felicity effect; Kaiser effectApplication of Surface-Wave Tomography to Sealed Conditions of Repaired Cracks in
Concrete
Nobuhiro Okude1, Tomoki Shiotani2
1

Tokai Technology Center, Kyoto University, Japan
2
Kyoto University, JapanNDT method to confirm sealed conditions of repaired cracks in concrete has not been yet
established. As a promising technique, the surface-wave tomography is applied to evaluate theconditions. In a concrete abutment for water pipelines located at a cold region, surface cracks arevisually identified. Since these cracks are repaired with sealing, the tomography measurement isapplied before and after sealing in concrete. As a result, it is confirmed that the repaired conditions
are successfully evaluated by the surface-wave tomography. In this regard, it is found that theeffects of wave-lengths of the surface-waves and material properties of sealing agent are to befurther clarified for practical applications.

eKeywords: Surface wave, injected depth, crack injection method
Modeling the acoustic emissions produced by the dynamic fracture of a glass beam
Valentin van Gemmeren1, Jurg Dual1
1

Institute for Mechanical Systems, ETH Zurich, Switzerland

In structural health monitoring, a wide variety of parameters are employed to characterize acoustic
emissions (AE) belonging to specific failure modes. To better understand acoustic emissions andthe corresponding parameters, researchers aim to model the fracture process and the resulting
elastic waves. In previous work, we have developed a method to induce precisely controlled
dynamic fracture in a glass beam by focusing flexural waves at an arbitrarily chosen location in
space and time. During the fracture event, a single AE occurs, propagates through the glass beam
and is measured with a laser Doppler vibrometer. The AE from this experiment is reproducible as
the loading conditions during fracture are highly controlled. We model the fracture process as theinstantaneous splitting of the beam into two parts and the subsequent unloading of the fracturesurfaces. Then, we simulate the propagation of the resulting elastic waves through the glass beamwith a spectral element model. The AE obtained in the simulation show good quantitative
agreement with the measured AE from the experiment.
Keywords: AE modeling, dynamic fracture
Infrastructure monitoring: solutions and examples
Horst Trattnig1, Michael Hauserer1
1Vallen Systeme GmbH, Germany

The tragic collapse of the Morandi Bridge in Genoa in August 2018 is another indication that themassive worldwide investment backlog for long living infrastructures is not yet solved. On the one
hand huge investments are required to replace the dilapidated structures and on the other handlots of time is required for planning the reconstructions. During the years of required planning time,
the critical structures are still in operation and the risk has to be minimized. The owner of theinfrastructures, like cities and municipalities, use monitoring solutions as backup. Among othermethods such as temperature measurement, strain gauges ect., the demand for Acoustic Emission
is strongly increasing and moves more and more in focus to solve the task. The method is suitablefor permanent monitoring and its flexibility makes it adaptable to a variety of test objects. Vallen
System GmbH is involved in many monitoring projects as equipment provider together with service
providers, engineering firms and the infrastructure owners. The measuring systems have been
adapted to the new requirements throughout the entire measuring chain, from the sensors, theirmounting devices and cables, via the data recording to the software. A plurality of external sensors
e.g. for temperature or strain gauges etc. can be recorded in a standardized way via data loggers
and processed together with the Acoustic Emission data in the Vallen software. In the future,decision relevant information will be accessible via cloud based dashboard.
Keywords: Infrastructure monitoring bridge
Multichannel fiber laser acoustic emission sensor system
Caitlin Williams1, Meredith Hutchinson2, Marriner Merrill2, William Pogue2, Peter
Finkel2, Geoffrey Cranch2
1

2

KeyW Corporation, United States
US Naval Research Laboratory, United States

We demonstrate a multichannel fiber laser acoustic emission (AE) sensor system comprising four
fiber laser sensors on a single optical fiber in an accelerated fatigue experiment. Specifically,fexperiments are reported using fiber laser sensors to detect cracking in aluminum plates (multiple
sensors) and riveted lap joints (single sensor). The multichannel data acquisition system is event
driven and capable of sampling sensor signals at a rate of up to 25 MS/s per channel. The laser
sensors are shown to provide comparable signal-to-noise ratio to industry standard piezo-electric
sensors (MISTRAS S9225). We also present AE event and location-finding data from theseexperiments, along with data from piezo-electric sensors co-located with the fiber laser sensors.
Keywords: Fiber laser sensors, crack detection, accelerated fatigue
Acoustic Emission Information Discovery at the University of Illinois
Mary C Schlembach1, William H Mischo1
1

University of Illinois at Urbana-Champaign, United States

In the late 1990s, Allen T. Green, president of Acoustic Technology Group, donated his personalcollection of acoustic emission publications to the Grainger Engineering Library at the University
of Illinois at Urbana-Champaign. This collection is comprised of around 4,500 articles, technicalreports and conference papers. The collection is particularly strong in government and corporate
technical reports and has been utilized by researchers for many years. While many of the papers
are available electronically, we are reviewing the more unique items in preparation for digitization.
Some of these digitized documents will be placed in the Library’s document repository, IDEALS.
The Library has also developed an expanded information discovery and delivery service, called
Easy Search, which provides access and full-text linking capabilities to journal, book, and report
literature of acoustic emission. The full-text links in Easy Search resolve to publisher sites,
government agency document sites, and to open access versions of the papers. Open accessversions are available to anyone using the system. Our presentation includes information about
digitization, copyright issues, and a demonstration of the Easy Search discovery software.
Keywords: Information discovery, publications, full text, open accessOn the Use of Acoustic Emission in Vibrations Testing or Progressive Failure
Monitoring and Modeling
Brian Wisner1, Mira Shehu1, Johnathon Kordell2, Abhijit Dasgupta2, Harsh Baid3,
Antonios Kontsos1
1

Drexel University, United States
University of Maryland, United States
3
AlphaStar, United States

2

Damage in material systems is a stochastic, multiscale process which bridges incubation,
nucleation, and growth. As the level of damage in a system progressively increases, changes inmaterial response to external loads occur. In the case of rotor blades, shifts in the natural
frequency during vibrational testing have been proposed and used as a means to identify damage
as well as to replace components. Significant shifts, though, that can reliably identify damage
during component use typically do not occur until near the end of component life and therefore
provide minimal warning of impending failure. In this context, Acoustic Emission (AE) monitoringis proposed as a scale bridging method used in conjunction with frequency shifts for detection ofearly damage starting at the microscale, as well as for significant damage as end of life
approaches. This work demonstrates the feasibility of this concept for two aerospace material
systems, a precipitate-hardened aluminum alloy (Al 2024-T3) and a carbon fiber reinforced
composite (IM7/8552). The specimens were damaged by application of a resonant frequency in asingle degree of freedom shaker while the vibration response was measured by an accelerometerattached to the tip of the specimen. Additionally, strain and AE activity was monitored via sensors
placed near the root of the specimen where damage is most significant. The monitored AE wasfirst compared to results obtained for these material systems in monotonic and cyclic loading
experiments where particular damage mechanisms were identified. The ensemble of recorded AEgdata was then used in a post processing-scheme based on outlier analysis to form damage
progression curves. The use of such progressive failure estimates produced by AE monitoring intoa virtual testing framework is also presented.
Keywords: Vibrations, Resonant Frequency, Acoustic Emission, Progressive Failure Analysis,
Virtual Testing
Acoustic Emission Monitoring for Additively Manufactured Materials
Brian Wisner1, Emine Tekerek1, Mir Shehu1, Antonios Kontsos1
1

Drexel University, United States

Acoustic Emission (AE), among other Nondestructive Evaluation (NDE) techniques, has been
demonstrated to be particularly useful in the characterization of material behavior and themonitoring of their failure processes. Such NDE information, has been used to infer on material
design choices mostly by connecting AE with microstructure and failure information to make bothdesign and manufacturing changes. For example, in metallic alloys the role of defects such as
voids, twins and inclusions has been investigated using AE during mechanical testing and resulted
in insights related to recommendations of the appropriate use of metallurgical procedures tomanufacture them. In this context, the increasing use of advanced manufacturing methods has
resulted in an unprecedented number of new materials which although they are produced using
the same base material (e.g. aluminum) they are now made using novel methods (e.g. selective
laser melting, SLM) and consequently their quality and performance needs to be evaluated toallow their adoption in engineering applications. In this talk, the potential of AE monitoring toassess the manufacturing quality of a particle-reinforced Aluminum system is investigated.
Specifically, AE monitoring coupled with microscopy is used to create a link between the level ofthe laser power used in SLM, the produced microstructure, and the resulting mechanicalproperties of this additively manufactured material. Extensions related to the use of AE inadvanced manufacturing are also provided.
Keywords: Additive Manufacturing, Metals, Acoustic Emission, Mechanical Behavior,
Characterization
Detection of Contaminants in Unidirectional Carbon Fiber Lay-ups
Fady Barsoum1, Isabel McBrayer1
1

Embry-Riddle Aeronautical University, United States

Composite manufacturing facilities work hard to ensure that the presence of contaminants in their
lay-ups are minimized. Eradicating all potential contaminants is, however, not possible in astandard manufacturing environment as it is a costly and time-consuming exercise. Composite
repairs performed “on the fly” are also at an increased risk for potential contaminants frommicroparticles in the air and from various oils and solvents.
The purpose of this paper is to investigate the presence of contaminants in composite lay- ups,
using interrogation of an acoustic emission signal. The test specimens for the investigation are
four ply unidirectional carbon fiber flat panels with a contaminant introduced between the second
and third ply. A Hsu Nielsen source is utilized to produce the acoustic signal, and the resulting
data for virgin and contaminated test specimens is then collected and compared using hardware
from MISTRAS, and software program AEWin to interrogate the signals. Tensile testing to failureis then conducted to assess the effects of the contaminants on the failure progression of the test
specimens. This is part of a larger study into the use of Acoustic Emission testing to study theeffects of different variables on composite repairs.
Keywords: Composites, Repairs, ContaminantshInvestigating the Structural Response of Composite Repairs under Cyclic
Loading
Fady Barsoum1, Isabel McBrayer1
1

Embry-Riddle Aeronautical University, United States

Composite repairs are a useful tool in industries such as Aerospace, Automotive and Naval.
Particularly when extended downtime is undesirable. Different repair techniques exist in industry
and each repair technique is subject to multiple variables, to include ply orientation, overlap length,
grade of epoxy, double sided vs. single sided and scarf angle. As such it can be difficult toascertain the integrity of a repair, specifically with respect to life under cyclic loading.
The purpose of this study is to investigate the effects of different repair variables under cyclicloading, using interrogation of an acoustic emission signal. This report focusses on external patch
type repairs on thin laminates, and the effects of overlap length and ply orientation on the failure
progression under cyclic bending loads. The test specimens for the investigation are four ply
unidirectional carbon fiber flat panels that are damaged with a small perforation prior to repair.
Both virgin and repaired test specimens are then subjected to cyclic three-point bending at 5Hz
for a fixed number of cycles before being subjected to a tensile to failure test. The tensile testingis accompanied by Acoustic Emission monitoring in order to assess the failure modes of the test
specimens, initiated by cyclic loading. Resulting data for virgin and contaminated test specimens
is then collected and compared using hardware from MISTRAS, and software program AEWin tointerrogate the signals. This is part of a wider study to assess structural composite repairs to thinlaminates under various cyclic type loading conditions.
Keywords: Composites, Repairs, Cyclic Loading
An Acoustic Emission IoT Framework for Monitoring, Data Management,
Diagnostics and Prognostics
Krzysztof Mazur1, Brian Wisner1, Antonios Kontsos1
1

Drexel University, United States

Monitoring of complex structures subject to loading, data management and mining, as well as
efficient signal processing of Acoustic Emission (AE) data for diagnostics and prognostics are
crucial parameters for the successful implementation of AE in asset management applications.
Recorded AE signals contain a variety of intrinsic (hardware) and extrinsic (noise) factors thatmake the damage assessment process both complex and computationally expensive. In addition,
the use of a constantly growing amount of statistical/machine learning tools to analyze AE data,
would benefit from efficient data workflows and processing procedures. In this context, this talkpresents a novel Internet of Things (IoT) framework for AE that can be implemented to perform
online predictions of remaining useful life (RUL) during material and structural behavior
assessment. The framework consists of two parts: the hardware/software integration that allows
the establishment of an IoT framework and the computational approach to perform RUL
predictions. The IoT framework comprises three components: the Edge, the Fog and the Cloud.
The Edge is the bottom level hardware that interfaces with the sensors used and is capable offiltering and transmitting data. The Fog is the hardware that provides an environment where
computational algorithms for both diagnostics and prognostics can be implemented leveraging
cloud-like properties. The Cloud is used to store data as well as to perform demanding
computations such that required large databases and training sets. The computational approachis based on the use of an outlier analysis combined with custom information quality metrics used
to identify a given set of degradation states. Predictions of RUL are made by extracting andprocessing AE features and then using Gaussian mixture modeling to identify different signal types
by clustering. Such information is then used to train a linear support vector machine and adaptive
neuro-fuzzy network that can be implemented to make RUL predictions. An application of the
proposed approach in fatigue experiments of an aerospace-grade composite material isipresented. Extensions that can make this approach applicable to industrial applications are
discussed.
Keywords: Acoustic emission, Prognostics, Diagnostics, Remaining Useful Life, Internet of Things,
Data Mining, Machine Learning
Acoustic Modes in Optical Fibers for Lamb Wave and Acoustic Emission
Detection
Kara Peters1, Junghyun Wee1
1

North Carolina State University, United States

Fiber Bragg grating sensors are traditionally used as both quasi-static strain sensors andultrasound acoustic structures when bonded to structures. In the latter case, the optical fiber isconstrained at the bond location and therefore displaces locally with the propagating ultrasonicwave in the structure. In addition, traveling ultrasound waves (fundamental longitudinal mode) arealso generated in the optical fiber through force transmission. The amplitude and direction of theresulting traveling ultrasound waves strongly depends on the form of the bond between the opticalfiber and the substrate. In this presentation we investigate the directionality and amplitude of theresulting traveling wave as a function of the different bond interfaces. For example, we start witha simple adhesive bonds and then add the presence of a protective coating or tubing between the
structure and the optical fiber, creating additional adhesive layers. The experimental results
demonstrate the presence of multiple traveling modes and their reflections off the multiple
interfaces. We also compare the results to a theoretical representation using coupled mode theory.
The implications of these results on the detection of Lamb waves and acoustic emission in
structures will be discussed.
Keywords: fiber Bragg grating, Lamb waves, structural health monitoring
Monitoring of fatigue cracking and damage of metallic materials throughAcoustic Emission and Digital Image Correlation
Stephanie Deschanel1, Julien Rethore2, Jerome Weiss3
1

Mateis - INSA Lyon, France
Gem - Centrales Nantes, France
3
Institut des Sciences de la Terre, Université Grenoble Alpes, France
2

The failure of mechanical structures under cycling loading, remains a considerable technologicalchallenge as it occurs unexpectedly when the structure is operating apparently in a safe andsteady state regime, without external signs of mechanical deterioration. It is therefore important tofind new ways to identify early precursors of fatigue. The acoustic emission (AE) technique hasreceived growing interests to detect and monitor fatigue cracks.
Here we report for the first time, in different metallic materials, the detection of acoustic emissions
specific of fatigue crack growth. These so-called acoustic multiplets are characterized by nearly
identical waveforms, signature of a unique source, are repeatedly triggered over many successive
loading cycles at the same stress level, and originate from a single location. They mark the slow,
incremental propagation of a fatigue crack at each cycle, or the rubbing along its faces.
In addition, experiments have been performed to specifically monitor the fatigue crack growth withAE and digital image correlation (DIC), simultaneously. DIC allows displacement fieldmeasurements at the surface of the specimen from the images. This measurement can beanalyzed to detect the presence of cracks and follow their propagation. The goal is thus to answer
the fundamental question about the exact origin of these multiplets through the interpretation of
AE signals coupled with DIC and to ensure the physical processes involving the appearance of
these signals. To our knowledge, such signals have never been reported in the case of metal
fatigue, and no study combined the two methods.

jIn fine, being specific to fatigue cracking, these groups could be used as early warnings of crack
propagation, which will ultimately lead to structural failure. Their detection and characterization
thus open the way towards a new, reliable monitoring of the onset of fatigue cracking during
mechanical tests or within structures in service.
Keywords: AE, fatigue cracking, multiplets
Study of dislocation dynamics of copper single crystals under cycling loading
through acoustic emission and microstructural characterizations
Gabriel L’Hote1, Stephanie Deschanel1, Sophie Cazottes1, Maurine Montagnat2
1

Mateis, INSA Lyon, France
IGE, Université Grenoble Alpes, France

2

Acoustic Emission (AE) is in an appropriate tool to study crystalline plasticity. Two kinds ofplasticity may be identified: one linked to numerous, small and uncorrelated dislocation motions(mild plasticity) and another linked to dislocation avalanches (wild plasticity).
Here, we study pure Cu single crystal, with different orientations favoring multiple glide, the AE
sources being reduced to dislocations motion and micro cracking only. Uniaxial stress-controlled
low-cycle fatigue tests are performed through different stress steps are imposed. Two types of AE
are considered: continuous (background noise associated with the mild plasticity) and discrete AE(above a given threshold, associated with avalanches). Dislocation structure is characterized
before, during and after cyclic test using ECC-imaging, in a Supra-55VP SEM.
The firs results show that the amount of continuous AE energy increases with the imposed stresslevel. Almost no discrete AE is recorded before the macroscopic yield strength. Above the yield,
discrete AE is observed, especially during the first cycles. Meanwhile, the continuous AE isobserved at each cycle, but decreasing in intensity with the number of cycles. The plastic
intermittency and dislocation avalanches rapidly decay over very few cycles, meaning that thedislocation structure evolves rapidly during the first cycles, through avalanches and single
dislocation motion. After few cycles, as a structure is formed, the movement of single dislocation
is made harder by the formation of dislocation junction. Consequently, the dislocation mean freepath decreases and the elastic energy generated by their motions falls off simultaneously. Thus,
continuous AE decreases until it becomes stationary, sign that the dislocation structure evolves
less rapidly than at the beginning of the cycles. Also, the evolution of the wildness degree ofplasticity (fraction of plastic deformation accommodated through dislocation avalanches) is
studied in correlation with AE energy distributions along the cycles and according to the force
applied and the dislocation structure formed.
Keywords: AE, dislocation, microstructure, cyclic loading, pure Cu single crystals
Off-the-Shelf Tools for Generalized, Automated Large-Scale Location Accuracy
Refinement
Horst Trattnig1, David Kosnik2, Richard Nordstrom3
1

Vallen Systeme, Germany
CTLGroup, United States3
Nordstrom Consulting, United States
2

Location analysis of AE data has been a powerful tool, but location accuracy can be limited by avariety of physical and signal-processing factors. Working groups such as AEWG, EWAGAE, and
JCAE have been a good forum to discuss these limits, and understanding of these issues has
increased steadily; however, practical means to apply the resulting refinements to large amountsof data have been limited.
Several developments in recent years have made approaching such scalability issues with AE
location refinements more manageable. Implementation of open database (SQL) formats as ak

replacement to proprietary binary data formats is perhaps the most significant. Along with SQL
queries, external scripting tools can be built to facilitate large-scale analyses.
This talk focuses on a scalable, automated approach to location refinements through SQL queries
and scripting. A generalized arrival-time-picking algorithm approach is described which can (1)be employed with at least one off-the-shelf commercial system and (2) compare the data withthousands or millions of events without reprogramming. Examples of how the two routines - thearrival time picking routine and the automated data refinement - are applied are given fromexamples.
Keywords: Location analysis, automation
Applying Location Accuracy Refinement to Simulated Location Testing
Results
David Kosnik1, Richard Nordstrom2
1

CTLGroup, United States
Nordstrom Consulting, United States

2

In the last two AEWG meetings, we have presented simulated location results using automatedarbitrary waveform pulses over relatively large-scale data results (typically 10,000 distinct location
results) and generated some errors that arise in AE location results. Simulating errors was just a
step toward scaled testing refinement, in this year’s talk, we start to address not just simulating
the errors, but how the errors can be limited and mitigated. Part 1 of this talk was the off-the-shelfroutines which give us a means to apply corrections across large amounts of data. This wasnecessary to facilitate a scalable approach to the results. The next step was means to record datain such a manner that we could compare original results versus proposed correction mechanisms
side by side. The final step was to apply correction mechanisms. Over the years, many, manyerror corrections that could be applied to waveforms one at a time have been published. Here weshow how some of these can be adapted through the scalable approach and how they performen masse against simulated errors.
Keywords: Location analysis, automation
Fiber-optic acoustic emission sensor systems using fiber Bragg gratings
Ming Han1, Guigen Liu2, Yupeng Zhu2
1

University of Nebraska-Lincoln, United States
2
Michigan State University, United States

Compared to traditional piezoelectric acoustic emission (AE) sensors, fiber-optic AE sensors have
many advantages such as small size, light weight, immunity to electromagnetic interference,
multiplexing capability, and remote sensing capability. However, the development of practicalfiber-optic AE sensor systems has many challenges in terms of detection sensitivity, system cost,and robust operation when subject to environmental perturbations. We present our recent workon fiber-optic AE sensor systems based on fiber-Bragg gratings (FBGs) aimed for overcoming
these challenges. Specifically, 1) we perform noise analysis of the sensor system and the results
show that the AE detection sensitivity is mostly limited by the frequency noise of the laser source.
2) We will discuss the use of low-cost distributed feedback (DFB) semiconductor laser with selfinjection locking for high-sensitivity AE detection. 3) We will introduce a new multiplexing/demultiplexing method of FBG AE sensors based on laser intensity modulation. 4) We will present
several sensor designs and demodulation methods that are adaptable to large-backgroundstrains.
Keywords: AE sensors; Optical fiber sensors; Fiber Bragg gratings; LaserslAE and X-Ray study of Microstructure Damage to Cr-Mo-V Rotor Steel as aConsequence of Extremely Long Operation
Albert Leksovskii1, Boris Baskin1, Shavkat Azimov2
1

Ioffe Physical Technical Institute, Russian Academy of Sciences, St. Petersburg,
Russia2
Physical-technical Institute, Academy of Sciences of Tajikistan, Dushanbe,
Tajikistan

Long-term forecast of stability of complex-alloyed materials of electric equipment requires Longterm forecast of stability of complex-alloyed materials of electric equipment requires knowledge of
micro-destruction mechanics, deformation kinetics and permissible value of accumulated
deformation. Acoustic emission (AE) studied to characterize Cr-Mo-V rotor steel damage in theinitial structural state and as a result of operation for more than 20 years at 300°C and 510°C.
AE data acquisition system used developed in Kurchatov’s IAE. Samples of 100×10×3 mm
deformed at 0.22 mm/min at 20ºC by 1958U-10-1 test machine. X-ray study covered: residual
stresses (type I) σresidual, MPa; micro-deformation <ε>; internal structural stresses (type II) σ<ε>,MPa; average size of mosaic blocks D, nm and dislocation density ρtotal, cm-2.
AE was caused by local explosive plastic deformation, having rate of change about 2-3 orders ofmagnitude higher than "background" creep rate. This leads to a nonlinear redistribution of local
stresses and causes “mesostructural” changes. Degradation of Cr−Mo−V heat-resistant steel
plasticity as consequence of more than 20 years operation at 510ºC and 300ºC observed. This iscaused by an increase in microdistortions of crystal lattice, dislocation density, sizes of mosaic
blocks and accompanied by AE intensity Ṅ and average amplitude Ā increase. According to AE
linear location data, formation of clusters - areas of local explosive plastic deformation - isaccelerated by increasing stresses of type II. Clustering leads to equilibrium mesocrack generationof millimeter range probability increase.
Keywords: Acoustic emission; alloyed steel; explosive plastic deformation; cluster; equilibrium
mesocrack; internal structure stresses
Acoustic Emission Characterization of a Composite Overwrapped Pressure
Vessel as a Structural Health Monitoring Method
Behnoush Golchinfar1, Miguel Gonzalez Nunez1, Patsi Francis1
1

MISTRAS Group, Inc., United States

Composite Overwrapped Pressure Vessels (COPVs) are substituting the conventional metalpressure vessels that were previously used for storage and containment of compressed gases atabove atmospheric pressure due to its higher storage capacity and properties, such as being
lightweight. Monitoring of these vessels plays an important role in the Compressed Gases
industry. It is vital to detect structural damage that could later lead to compromised burst pressure
strength in a composite cylinder. Acoustic Emission monitoring technique has been proven to be
an acceptable testing method applied during periodic inspection.
In this study, characterization of one of the most recent manufactured COPVs was conducted byAcoustic Emission sensors. Attenuation tests at 0, 45, and 90 degrees angles were performed on
the cylinder of a pressure vessel by implementing four various sensors including both broadband
and resonant sensors. The wave propagation velocity in the material was also measured on thecylinder in various directions for comparison purposes. These tests resulted in different velocities
being obtained in different directions. The characterization of the head of the pressure vessel was
also conducted via the same sensors.
The achieved results would help us through better understanding the structure in terms of acoustic
waves and figuring out the superior method for monitoring the brand-new composite overwrappedpressure vessels. In addition to that the acquired data is vital for the next steps toward generatinga procedure for the test setup, data collection and data analysis using Acoustic Emission.
Keywords: Acoustic emission, composite overwrapped pressure vesselmExperimental Characterization of PKBBI Sensor: the Low Power Flat
Frequency Response, Broadband Acoustic Emission Sensor
Hossain Saboonchi1, Gonzalez Nunez Miguel1
1

MISTRAS Group, Inc., United States

Application of Modal Acoustic Emission (MAE) method to evaluate the health of the pressure
vessels has been established in standards and procedures. Most of these standards ask forbroadband transducers with certain level of flatness in the frequency response of the sensor in a
specific frequency range with certain level of sensitivity so that the captured traveling waves’energy gets a fair representation from all the different modes present in that frequency range. In
this paper the PKBBI sensor, developed by MISTRAS Group Inc, as required by these standards,
is characterized. The results of some example real applications have been presented andanalyzed to further evaluate the capabilities of this sensor. The PKBBI sensor shows superior
performance in the MAE analysis versus the conventional sensors. Spectrograms of the waves
have been overlaid to the group velocity curves to evaluate and analyze the acoustic emission
modes and how they can be utilized to extract valuable information on the traveling path, medium,and source classification of acoustic event.
Keywords: Acoustic Emission, Experimental Characterization, sensor, Modal AE
AE Measurements and Analysis on a Layered Pressure Vessel
Adrian Pollock1, Miguel Gonzalez1, Hossain Saboonchi1
1

MISTRAS Group, Inc., United States

Acoustic emission (AE) testing on a layered steel vessel was performed during a pressure test. A
comparison of both conventional feature-based AE and Modal Acoustic Emission (MAE) was
carried out. Most of this paper deals with the results obtained with this latest-generationtransducer, especially some applications of MAE technology.
On site, high priority was assigned to source location to provide maximum support to theRadiographic Testing measurements that were scheduled to begin immediately after the AEpressurization activities were completed. Acoustic Emission technique was capable of determining
the location of the source cluster. In addition to that by using the Modal AE, the waveforms were
further analyzed for additional characterization and location refinement.
MAE results described in this paper include source location refinements, source-sensor distancemeasurements used to validate the linear nature of the discovered source cluster, and thedemonstration that wave propagation characteristics (at least for the discovered cluster) were likethose of an unrestrained plate having the same thickness as a single layer of the layered vessel.
Keywords: Modal Acoustic Emission, Layered Vessels. Wave Propagation
Power Transformers Monitoring
Caricia Martinez1, Arturo Nunez1
1

MISTRAS Group, Inc., United States

Power Transformers represent the largest portion of capital investment in transmission anddistribution substations. Owners face several challenges:- Their assets are reaching or have already reached the end of their operating life
- Delivery times for a new transformer are very long (18 – 24 months)- Ever increasing demand prohibits the transformers to be taken out of service for routine
electrical testing. There only a few techniques that can be applied with the transformer energized,
these are Dissolved Gas Analysis, Oil quality, Infrared Inspection, Vibration and AcousticnEmission. Acoustic Emission (AE) is a powerful tool used to "listen" and register acoustic signalsof different frequencies generated within a power transformer. An acoustic emission monitoringmight be implemented to detect, locate and assess faults on power transformers.
The main advantages of this technique are:• No transformer outage is required to perform the inspection• Non invasive• Global monitoring of the asset• Use of multiple Acoustic Emission sensors provide location of the fault (s)• Offers real time information of when the fault is active• Indicates under what operating conditions the fault is active• Severity of the fault can be determined• Continuous monitoring to ensure safe operation and prevent a catastrophic failureThis paper presents the latest developments on this technology and cases studies are presented.
Keywords: Power Transformers, Online monitoring
3D-printed anisotropic structure for switching elastic wave propagation
Yoshihiro Mizutani1, Ryuichiro Otonari1, Takeshi Ashizawa1, Akira Todoroki1
1

Tokyo Institute of Technology, Japan

Several anisotropic structures made of CFRP and PLA are prepared by using 3D-printer. The
structures consisting “switch” which can control propagation direction of elastic wave. One of thestructures consists of hundreds of narrow columns that act as wave guides. When an external
force is applied to the structure, the columns buckle, and its buckling direction instantaneously
reverts according to the direction of external force. We use this phenomenon to make the switch.
The other structure consists of CFRP with curved fibers. As elastic wave propagates along thefibers, the structure which can control direction of curved fibers is prepared to realize the switch.
In order to discuss feasibility of the structures for switching wave propagation direction, wave
propagation in the structures are visualized by scanning ultrasonic receiver on the structure’s
surfaces.
Keywords: Elastic wave, anisotropic material, 3D-printed material
The Qualification / Certification Dilemma - An Innovated Approach for theDevelopment of Advanced Nondestructive Testing Technicians
Donald Blanchette1
1

MISTRAS Group, Inc., United States

Organizations and companies will gain the competitive edge only through the integration ofstrategies that bring together the power of technology and an engaged and well-trained workforce.
The pace of change of the marketplace and customer expectations demand a continuous
improvement to how individuals are being trained and certified for advance nondestructive testing
applications such as Acoustic Emission. Dr. Adrian Pollock believed that a solution to thischallenge is not to “force-fit” individuals into positions that are mismatched with their
responsibilities and duties that don’t fit their technological and certification profiles.
This paper will provide Dr. Pollock’s conceptual and practical approach to the training andcertification of individuals that will be conducting Acoustic Emission as Level 1 and 2s.
Keywords: AE Training Certification, Advanced NDE
AE as a Vehicle for High School STEM OutreachDavid Kosnik1
1

CTLGroup, United StatesoThe multidisciplinary nature of the practice of acoustic emission (AE) monitoring makes AE auseful vehicle for engaging high school (i.e. US secondary school, typically ages 14-18) students
in courses under the science-technology-engineering-mathematics (STEM) umbrella. Specifically,
aspects of theory and analysis techniques familiar to AE practitioners, such as attenuation,
frequency considerations, and location analysis, may be adapted to demonstrate real-world
applications of concepts presented in high school courses such as geometry, advanced algebra,
trigonometry, calculus, and physics, as well as the introductory engineering, electronics, andcomputer science courses now offered in more and more school districts. In addition, hypothesisdriven investigative and problem-solving approaches presented within AE case studies give
students a look into broad applications of the scientific method in general. This paper willsummarize my experience in making presentations to various high school mathematics andintroductory engineering classes over the past few years. All class presentations were based on
the same AE case study involving diagnosis of anomalous noises on a movable bridge, but thepresentation to each class was specifically tailored to the concepts studied in that class incoordination with the classroom teacher.
Keywords: K12 outreach, STEM, case study
Increase uptime and plant reliability deploying asset monitoring solutions
Arturo Nunez1
1

MISTRAS Group Inc., United States

Traditionally, periodical testing of assets has been the method of choice to evaluate its integrity.
This approach can have some disadvantages such as erroneous measurements, unavailability of
the asset for testing or testing using different instrumentation settings/techniques. The
development of instrumentation for continuous monitoring allows the inspection intervals to be
reduced significantly as well as to be able to monitor the asset during the very different operating
conditions it experiences, hence allowing to identify correlation between fault mechanisms(cracks, leaks, corrosion, arcing, etcetera) and the operation of the asset.
This paper presents different applications where asset monitoring is used for detecting active
faults in power transformers, tube leaks in boilers, cracks on combustion turbines, thickness
measurements. Case studies where the benefits for monitoring are presented.
Keywords: Monitoring, acoustic emission, remote
Identifying The Presence and Position of Wheelflat via Acoustic Emission
Okan Topcu1
1

Turkish State Railways, Republic of Turkey

Intensive usage of railways brings structural problems together for both track and rolling stocks.
Wheelflat is one of the most important problems faced in trains and detecting the wheel flat in realtime without interrupting train operation is important for all infrastructure managers and railway
operators. While there are studies to detect the wheelflat up to 40 km/h, there is no literature to
identify the position of wheel with wheelflat real time. In one of the recent research projects of theState Railways of the Republic of Turkey, acoustic emission measurements were carried out to
detect the presence and position of wheel flat. The aim of this study is to identify the presence of
wheel flat and the position of the wheel flat at the wheels of trains passing through at different
speeds up to 65 km/h. A linear array of acoustic emission sensors array was mounted on the railwith the spacing of 3 m or 5 m. Three different types of acoustic emission sensors (F15I, R6I, WD)
were mounted on the web of the rail to understand the influence of AE frequency to the wheelflat
measurement while trains at different speeds were passing through. The presence of wheel flat
was detected by repetitive impact signal with the periodicity proportional to wheel diameter andtrain velocity. The periodic impact was observed in the time driven absolute energy per averagepsignal level or streamed waveform signals. The expected impact frequency was determined by
the inverse of impact period. The position of wheel with wheel flat was measured by relative timeof flight of the peak signal by the sensor array. According to the results of the study acoustic
emission amplitude energy increases exponentially with increasing train speed,and the presence
and position of wheelflat can be determined real time using AE.
Keywords: Wheel flat, rail wheels, acoustic emission, train wheels inspection
History and Development of Acoustic Emission Personnel Qualification andCertification
Miguel A. Gonzalez Nunez1, Adrian A. Pollock1
1

MISTRAS Group, Inc., United States

The need for widespread AE technician certification came to be recognized at Physical Acoustics
Corporation in 1986. Prior to that time, education in AE was readily available to engineers andscientists at short courses offered by the equipment manufacturers, at the meetings of dedicatedgroups such as AEWG and EWGAE, and at conference series such as the International AE
Symposia held biennially in Japan. Physical Acoustics Corporation made a first public offering oftechnician-oriented AE training based on the recommended training course outline alreadyavailable in the American Society for Nondestructive Testing’s publication SNT-TC-1A. Since
then, Physical Acoustics Corporation (now MISTRAS Group Inc.) has offered AE Level I, Level II
and Level III courses over 150 times. Trainings have been delivered to over 2000 technicians andother attendees, in the curriculum of SNT-TC-1A, ISO 9712 and in numerous specialized AE
certification schemes. This presentation will focus upon the qualification and certifications
processes, and the challenges for new and emerging AE applications.
Keywords: Personal Qualification and Certification
APPLICATION OF ACOUSTIC EMISSION DURING PRESSURE TEST OFAUSTENITIC STEEL VESSEL
Václav Svoboda1, František Žemlička1
1
Preditest Ltd., Pod Višňovkou 1662/23, 14000 Praha 4, Czech Republic
The paper describes a method of pressure testing of a vessel made from austenitic steel type AISI
304. The method of acoustic emission was used as a non-destructive inspection tool. On the outersurface of the vessel was placed a network of AE sensors comprising the entire volume of thevessel. In normal operation, this pressure vessel operates under cryogenic conditions(-160 ° C). During the pressure test with nitrogen, increased emission activity was registered,especially in the cylindrical part of the vessel. The individual emission events were not located in
sub-sources (clusters), but were distributed throughout the shell volume. This caused acomplication in evaluation of the pressure vessel according to the current valid standards for theevaluation of the AE signals. By further analysis has been found that the registered AE signals
were induced by phase transformation of austenite to deformation-induced martensite, including
the reverse process at local sites.
.Keywords: Pressure test, austenitic steel vesselq

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