• Smart Structures and Systems
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• v.4 no.5
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• pp.565-582
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• 2008

This paper presents a feasibility study of flexible piezoelectric paint for use in wide-band low-profile surface-mount or embeddable ultrasonic sensor for in situ structural health monitoring. Piezoelectric paint is a piezoelectric composite with 0-3 connectivity. Because of its ease of application, piezoelectric paint can be readily fabricated into sensing element with complex pattern. This study examines the characteristics of piezoelectric paint in acoustic emission signal and ultrasonic guided wave sensing. A series of ultrasonic tests including pitch catch and pencil break tests were performed to validate the ultrasonic wave sensing capability of piezoelectric paint. The results of finite element simulation of ultrasonic wave propagation, and acoustic emission generated by a pencil lead break on an aluminum plate are also presented in this paper along with corresponding experimental data. Based on the preliminary experimental results, the piezoelectric paint appears to offer a promising sensing material for use in real-time monitoring of crack initiation and propagation in both metallic and composite structures.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.57-64
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• 2004

This study deals with the prediction of defect location which can be occurred in structure. The existing methods was very difficult to be applied to predict it, because of complex numerical formula. The triangulation method proposed in this study can predict the source location easily with small amount of data. The arrival time of wave can be directly converted into the distance between sensors. For this purpose, the propagation velocity was measured by Rayleigh wave, and the propagation behavior was analyzed. The welded workpiece is adapted to investigate for the consideration of jointed part in structure, The propagation velocity of signal was measured in welded workpiece and the revised algorithm of source location was proposed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.293-298
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• 2003

This study deals with the source location method of defect which can be occurred in structure. The existing methods was very difficult to be applied to predict it because of using very complex numerical formula. The triangulation method which was proposed in his study can predict the source location predicted easily with small amount of data. Wave arrival time data can be directly converted into source-sensor distance is known. For this purpose, the propagation velocity was measured by Rayleigh wave, and the propagation behavior was analyzed. For the consideration of jointed part in structure, the source location method was applied to the welded workpiece. The signal propagation velocity was measured in welding part for the purpose of application to the part and the revised algorithm of source location was proposed.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.10-17
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• 2008

The purpose of this thesis is to reduce of error for source location through acoustic emission(AE) signal, generated elastic wave from crack growth to leak for facility diagnosis. Especially, in order to overcome noise from original signal, this paper proposed enhancement of source location by using noise reduction based on wavelet transform. To evaluate actual performance in experiments, Pencil Lead Break is used crack signal source on the aluminum plate and drain valve of air compressor is used as substitute pressure vessel to generate leak signal. In signal processing, wavelet shrinkage and soft threshold are used to discriminate signal source and then source location techniques have been effectively used with group velocity using material property and time difference between sensor using cross correlation. Source location for crack and leak test have some difference, but the result show that improved 30% with a average length within 10.46mm in crack test and improved 2% compare with average filter in leak test when we applied wavelet transform.

• Smart Structures and Systems
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• v.18 no.5
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• pp.1063-1085
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• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• Tunnel and Underground Space
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• v.21 no.5
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• pp.349-358
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• 2011

This paper introduces a new P wave arrival time determination algorithm of acoustic emission (AE) suitable to identify P waves with low signal-to-noise ratio generated in rock masses around the high-level radioactive waste disposal repositories. The algorithms adopted for this paper were amplitude threshold picker, Akaike Information Criterion (AIC), two step AIC, and Hinkley criterion. The elastic waves were generated by Pencil Lead Break test on a granite sample, then mixed with white noise to make it difficult to distinguish P wave artificially. The results obtained from amplitude threshold picker, AIC, and Hinkley criterion produced relatively large error due to the low signal-to-noise ratio. On the other hand, two step AIC algorithm provided the correct results regardless of white noise so that the accuracy of source localization was more improved and could be satisfied with the error range.