Journal of AE, Volume 27, 2009 (ca. 35 MB) - AEWG

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ACOUSTIC EMISSION LEAK DETECTION OF LIQUID FILLED BURIED PIPELINE ATHANASIOS ANASTASOPOULOS, DIMITRIOS KOUROUSIS and KONSTANTINOS BOLLAS Envirocoustics ABEE, El. Venizelou 7 & Delfon, 14452 Metamorphosis, Athens, Greece Abstract Several limitations and difficulties exist in the inspection and maintenance of underground pipelines that cannot use pigs (pipeline inspection gauges). Leaking is unavoidable in such buried pipelines and poses serious problem to the environment as well as the pipeline owners. Pipeline leakages are usually apparent either when the pressure is dropping for no other obvious reason or when valuable product is lost. However, even in the best-case scenario, where the operators can isolate specific pipeline sections suspected to leak, it is often the case that the operators cannot reliably locate the exact position of the leak so as to take corrective measures. Acoustic emission (AE) is an excellent tool for detecting and locating leaks in buried pipelines. Access to the pipeline is required only locally for mounting AE sensors. Pipeline is pressurized and AE tested in 600-to-1000-m-long sections at a time. The AE sensors detect the turbulent flow at the leak orifice, and with the use of digital AE systems and specialized software, the position of the leak is provided. The present paper deals with the technical description and the physics of the AE leak detection technique, presents the advantages, limitations and requirements of the method, describes the necessary functions of AE equipment for performing such a task, and, finally, reports on several case-studies of successful leak detection and location of buried pipelines. The case studies cover both new and in-service buried pipelines of different sizes. Keywords: Pipeline inspection, Leak test, Loss control, Pipeline integrity Introduction The undesirable fluid losses due to leaks constitute one of the bigger problems in industrial installations, refineries, power stations and, in general, anywhere there are moving or stored liquids or gases, with occasionally enormous, environmental and economic repercussions. Nondestructive leak testing deals with the leaking of liquids or gases in pressurized or evacuated components or systems as a result of pressure differential. Acoustic emission (AE) is widely used for locating such leaks [1-4]. The turbulence caused by the flow of a pressurized fluid through an orifice produces energy waves of both sonic and ultrasonic frequencies. Figure 1 presents some physical features related to and affecting the leakage flow. Pollock and Hsu [2] provided a basic understanding of the leak mechanism and AE testing. Miller and others [3] conducted laboratory tests and experiments to evaluate existing leak detection and location methods. Standards such as ASTM or ASME describe the method for detecting and locating the steady-state source of gas and liquid leaking out of a pressurized system [4, 5]. It is a common understanding in all the above works that AE can be produced by the highly unstable turbulent pressure field at the orifice, and the condition of detection is that the Reynolds number Re > 1000 at the orifice, so as to ensure turbulent flow. The corresponding AE signals generated are of a “continuous” nature. Additional sources that may produce AE in the occasion J. Acoustic Emission, 27 (2009) 27 © 2009 Acoustic Emission Group
Turbulent Flow Condition: Re > 1000 Fig. 1. Leaking flow features. of a leak are local crack/orifice growth, cavitation due to local sub-pressure at the orifice, temporary entrapments and impacts of solid particles at the orifice, soil movements, or even external sources such as impacts etc., which are mainly “burst” type sources. The generated AE waves from such sources propagate through the fluid or through the pipeline itself. Acoustic emission sensors operating between 20 and 100 kHz are mounted on the pipeline, monitoring both continuous and burst type emissions through simultaneous monitoring of time-driven data (threshold independent sampling) and hit-driven data (threshold dependant). In addition to that, acquisition of AE waveforms or waveform streaming is often used. Simplistic estimation of the leak location can be made by measuring the amplitude variations of continuous signal at various positions along the pipe. Based on signal attenuation (known or measured independently on the pipe itself) and signal amplitude reduction with the distance from the source (leak), as measured at various positions, an amplitude variation ratio is recorded. Based on this ratio the distance to the source can be roughly calculated. However, a more effective and accurate method to locate a leak on a buried pipeline is linear location. Two (2) AE sensors placed on either side of the leak are required for this method. If an AE event occurs at an “x” distance from the first sensor, then x = (L - VΔt)/2, where “L” is the known distance between the two sensors “V” is the (known or measured) AE wave velocity and Δt the time difference of the wave arrival on the two sensors measured by the acquisition system [6]. Finally, postprocessing of streamed waveforms (continuous long waveforms) might be used to enhance both detectability and location accuracy. Requirements, Advantages and Limitations Pipeline surface access holes are excavated at pre-defined sensors distances (typically every 100 m) along the pipeline, in order to expose a small part of the pipe (a small exposed surface about 15x15 cm 2 on the top part of the pipeline is required). Any protective sleeve, insulation or fiberglass coating has to be removed for sensor mounting. The section of the pipeline that will be tested has to be isolated (in order to apply static pressure) and without any medium flow (to avoid the associated noise). For testing, pressure in the tested section is increased and kept stable. Although a single channel leak detection portable instrument might be used to acquire the average AE signal level of the pipe at the exposed points and identify the area that is suspected for the leak, a multichannel system is needed for reliable source location. Therefore, multiple AE sensors are placed on the exposed points along the suspected pipeline section and a multi-channel AE leak detection system is used to acquire the leak signals. Special software is used to acquire the signals, to U d l v P P atm Re = Mean fluid velocity through orifice = Mean orifice diameter = Orifice length = Kinematic viscosity of fluid = Pressure inside the pipeline = Atmospheric Pressure = Reynolds number 28
Page 2 and 3: Journal of Acoustic Emission, Volum
Page 4 and 5: and HISAKAZU MORI 233-240 27-241 EF
Page 6 and 7: JOURNAL OF ACOUSTIC EMISSION Editor
Page 8 and 9: MEETING CALENDAR: EWGAE29 EWGAE-201
Page 10 and 11: eference in that document to see if
Page 12 and 13: AE LITERATURE Chinese Acoustic Emis
Page 14 and 15: Li Guanhai, Liu Shifeng (Tsinghua U
Page 16 and 17: Monitoring during Landing Gear Cont
Page 18 and 19: MONITORING THE CIVIL INFRASTRUCTURE
Page 20 and 21: including stringers, floor beams (i
Page 22 and 23: Fig. 3. A sensor array for monitori
Page 24 and 25: Fig. 7. Maintenance follow-up recom
Page 26 and 27: Fig. 9 Fatigue-prone location on th
Page 28 and 29: ACOUSTIC EMISSION TESTING OF A DIFF
Page 30 and 31: adius of each other in order to pas
Page 32 and 33: Fig. 3. AE transducer arrays. (a) A
Page 34 and 35: References Holford, K. and Lark, R.
Page 36 and 37: Unfortunately, there are neither a
Page 38 and 39: Table 2: Total damage extension of
Page 40 and 41: Some structures, even when visibly
Page 42 and 43: identification processes leading to
Page 46 and 47: evaluate and to calculate the linea
Page 48 and 49: Further analysis was made on-site u
Page 50 and 51: graph) shows higher activity betwee
Page 52 and 53: Fig. 8. (a) Aerial photo of the com
Page 54 and 55: Fig. 12. ASL measurements at 8.3 ba
Page 56 and 57: Today’s modern AE systems offer i
Page 58 and 59: Fig. 1: Primary AE parameters [6].
Page 60 and 61: Fig. 4: Backpropagation neural netw
Page 62 and 63: does not drop below the set thresho
Page 64 and 65: Fig. 8: Amplitude histogram for a t
Page 66 and 67: Table 2 shows the tests for the 3.8
Page 68 and 69: Fig. 14: Cumulative energy versus c
Page 70 and 71: crack jumps have a higher amplitude
Page 72 and 73: etween these two fatigue life value
Page 74 and 75: Table 5: 25% HCF BPNN testing file.
Page 76 and 77: Fig. 23: Range of data used for 50%
Page 78 and 79: 4. Conclusions and Future Work Acou
Page 80 and 81: [15] Suleman, J., Korcak, A., Barso
Page 82 and 83: on the resin plate hardness or its
Page 84 and 85: Fig. 4 Schematics of cutting load a
Page 86 and 87: (a) Line force during idle cutting
Page 88 and 89: (a) Non-arranged die type, f imax /
Page 90 and 91: Fig. 11 Sensor position and impact
Page 92 and 93: Fig. 14 Relationship between Δf im
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DAMAGE ONSET AND GROWTH IN CARBON-C
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(a) (b) (c) Fig. 1. (a-b) Polarized
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Fig. 4(c). The dependency of the th
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Fig. 6 (c) cumulative AE counts at
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as shown schematically in Fig. 8(a)
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(a) (b) (c) (d) Fig. 11. Micro-fail
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FUNDAMENTAL STUDY ON INTEGRITY EVAL
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Fig. 3 UT C-scan images of internal
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Fig. 7 AE signal peak amplitude and
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Fig. 9 Relationship between AE sign
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monotonically with load. As mention
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A phenomenon of stress wave generat
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where plus sign refers to the first
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kHz. The result of filtration is sh
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Sphere radius, R, mm Sphere mass, m
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Regime ρV 2 /Yd (MPa) Approximate
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a b Fig. 6. AE signal at the remote
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a b Fig. 9. AE waveforms from impac
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9. А.А. Harkevich, Spectrums and
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amplitudes in the AE signals. In th
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Fig. 2. Signals from small aperture
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Fig. 4. Signals from small aperture
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during the duration of the direct w
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Fig. 8. Expanded time scale view of
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Fig. 10. Displacement signal result
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Fig. 12. Top surface out-of-plane d
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Fig. 14. Signals from small apertur
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Fig. 16. Filtered signal showing hi
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Fig. 18. WTs of the first 200 µs o
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Summary There are three summary obs
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FRACTURE BEHAVIOR IN BONE CHARACTER
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Fig. 2. Schematic diagram of static
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and AE events during loading were d
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References [1] Hirai T., Katayama T
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where and stand for the strain-hard
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Experimental The samples for mechan
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Fig. 4. Typical AE waveforms and th
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detected just before the upper yiel
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the AE energy in iron upon Lüders-
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The most intriguing issue, which ha
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ACOUSTIC AND ELECTROMAGNETIC EMISSI
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Fig. 2 Sample assembly: (a) setup f
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We have observed that the EME signa
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EME under Repeated Loading Rectangu
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the fact that the AE due to the fri
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Abstract IDENTIFICATION OF AE MULTI
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sensor/sonde/surface units have fla
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Fig. 3. Coherency between a pair of
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These multiplets may be related to
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Fig. 7. Hypocenter location and sou
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Accordingly, semi-supervised or uns
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Indeed, the AE events are also non-
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Fig. 5. Overlap plot of AE events f
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(a) Individual Events Test 1 Test 2
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Fig. 10. The distribution of event
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loading tests of concrete elements
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Fig. 4: Typical cracking of specime
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(a) Plain concrete. (b) Composite.
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3) T. Shiotani, M. Shigeishi and M.
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data cannot be recorded by most exp
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where A and I are the area and mome
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Fig. 3 Loading condition for the si
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4. Simulation Results 4.1 Stress-st
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dilatancy observed in an actual uni
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Fig. 9. Energy-frequency distributi
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After the AE cluster formation, mic
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Fig. 13 Spatial distribution of all
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7. J.F. Hazzard and R.P. Young: Int
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obvious that the calibrating AE sou
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Fig. 2. Geometry and schematic illu
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The boundary conditions for tempera
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Calculations have shown that in ord
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Fig. 9. Example of an AE sensor cal
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Hence, the distinct features and ad
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Developed Sensor Configuration Rela
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In this study, the sensor width was
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Fig. 8 Typical AE waveforms and the
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Fig. 11 Relationship between shell
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CORROSION DETECTION BY FIBER OPTIC
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sensor by an FOD sensor. Due to the
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Corrosion AE Monitoring AE monitori
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operated at the normal condition. S
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EFFECT OF SHOT PEENING ON THE DELAY
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Figure 1 shows the SSI and three-po
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stresses do not give any positive e
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Fig. 7 Examples of Lamb waveforms p
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Figure 10 shows results of an SSI t
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as-received strip. It is noted that
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Almen strips were charged in less a
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more AE transducers. AE events may
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Given the data with , and the new d
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Fig. 4. Projection of AE data: (a)
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Table 2. Feature-discriminated stat
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FLEXURAL FAILURE BEHAVIOR OF RC BEA
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Table 2. Weight loss of rebar after
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(a) Global (b) Close up (only skele
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AE hits. By comparison among corros
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dots). It seems that the ratio of A
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Fig. 1. The scheme of double-bottom
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Fig. 3. The other two at 2 and 7 o
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Fig. 6. Located AE sources with the
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Fig. 10. The intensity of recorded
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STUDY OF IDENTIFICATION AND REMOVAL
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Fig. 4 2D Location result with ch5-
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Fig. 8 Noise removal result with th
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Identification and removal for drop
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It was confirmed that a minimum thi
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A GENERIC TECHNIQUE FOR ACOUSTIC EM
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where ∆t exp are the experimental
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stone disc. Oolitic limestone is a
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CFC plate test, larger errors occur
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ACOUSTIC EMISSION TESTING - DEFININ
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What is the detection performance o
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A.3. Performance analysis - Calcula
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- Planar testing configuration (2.5
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Whereas in the planar testing confi
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We can see that full use of the pla
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B.3. Influence of the minimum numbe
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technique such as GBP in France, au
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Page 149 Elastic Waves in Solids Ro
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A Note from the Editor Kanji Ono Nu
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Page 173 5th Colloquium on AE D. Sc
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S78 S82 S86 S90 S94 On the Applicat
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S247 In-Process Acoustic Emission M
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Page i Appreciation K. Ono / Europe
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Page 83 Number 2 Page 85 Page 93 Pr
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Page 111 Page 119 Page 129 Page 129
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S53 S57 S62 S66 S70 S71 S75 "AE App
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S231 S236 Vessels", S238 S239 "Tool
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Number 4 Page 65 Page 93 Page 99 A
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Page 197 Page 203 Nondestructive Ev
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M. Momayez, F. P. Hassani and H. R.
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Number 3 Page C1-C24 Page 95-99 Pag
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12th International Acoustic Emissio
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Source Simulation Analysis Hiroaki
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Page S70-S79 Page S80-S88 Page S90-
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Page S343 Page i-iii Page iii Page
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A. Tsimogiannis, B. Georgali, and A
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VOLUME 19, 2001 ACOUSTIC EMISSION E
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VOLUME 20, 2002 Contents 20-001 DAM
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20-285 ACOUSTIC EMISSION CHARACTERI
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21-197 New Concept Of Ae Standard:
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22-243 Rods And Tubes As Ae Wavegui
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23-196 Ae And Electrochemical Noise
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Kazuaki Arai, Katsuyuki Kaiho, Hiro
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25-132 A SIMPLE METHOD TO COMPARE T
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25-373 IMPACT IMAGING METHOD TO MAP
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High-Voltage Electric Devices Jan P
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27-212 ELECTROMAGNETIC METHOD OF EL
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Authors Index, Volumes 1-27 (1982-2
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Thomas Bidlingmaier 14-S47 Jacek M.
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S. Y. Chuang 4-S137 S. S. Christian
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J.-P. Favre 9-97 Carol Featherston,
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Y. Haddad 8-S314 M. E. Hager 8-S42
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Y. Ichimura 19-142 Makoto Ichinose
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Kunihisa Katsuyama 11-S27 Harold E.
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MOSHE KUPIEC, 27-077 D. S. Kupperma
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Eric Madaras 23-037 M. R. Madhava 8
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F. Mostert 18-189 Andre Moura 23-10
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Tatsuma Ohnishi 19-109 Tadashi Onis
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M. Raab 20-274 Amani Raad 20-300 Ja
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Keiichi Sato, 4-S240, 8-S213, 9-209
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Wolfgang Stengel 4-S312 D. Stöver
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R.G. Tobin 8-25 Akira Todoroki 26-1
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M. Wevers, 4-S186, 8-S272, 13-79, 1
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Daihua Zou 5-S1 Ryszard Zuchowski 2
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Journal of AE, Volume 27, 2009 (ca. 35 MB) - AEWG

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