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http://dx.doi.org/10.12989/sss.2016.18.5.1063

Acoustic emission source location and noise cancellation for crack detection in rail head  

Kuanga, K.S.C. (Department of Civil and Environmental Engineering, National University of Singapore)
Li, D. (Department of Civil and Environmental Engineering, National University of Singapore)
Koh, C.G. (Department of Civil and Environmental Engineering, National University of Singapore)
Publication Information
Smart Structures and Systems / v.18, no.5, 2016 , pp. 1063-1085 More about this Journal
Abstract
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.
Keywords
rail head; crack detection; acoustic emission; source location; noise cancellation; wavelet transform; Hilbert transform;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 ASTM E976-10 (2010), Standard Guide for Determining the Reproducibility of Acoustic Emission Sensor Response, ASTM International, West Conshohocken, PA.
2 Audoin, B., Pan, Y., Rossignol, C. and Chigarev, N. (2006), "On the use of laser-ultrasonics technique to excite selectively cylinder acoustic resonances", Ultrasonics, 44, 1195-1198.   DOI
3 Barke, D. and Chiu, W.K. (2005), "Structural health monitoring in the railway industry: a review", Struct. Health Monit., 4(1), 81-93.   DOI
4 Bruzelius, K. and Mba, D. (2004), "An initial investigation on the potential applicability of Acoustic Emission to rail track fault detection", NDT & E Int., 37(7), 507-516.   DOI
5 Chen, G.D. and Wang, Z.C. (2012), "A signal decomposition theorem with Hilbert transform and its application to narrowband time series with closely spaced frequency components", Mech. Syst. Signal Pr., 28, 258-279.   DOI
6 Ciampa, F. and Meo, M. (2010), "Acoustic emission source localization and velocity determination of the fundamental mode A0 using wavelet analysis and a Newton-based optimization technique", Smart Mater. Struct., 19(4), 045027.   DOI
7 Coccia, S., Bartoli, I., Marzani, A., Lanza di Scalea, F., Salamone, S. and Fateh, M. (2011), "Numerical and experimental study of guided waves for detection of defects in the rail head", NDT & E Int., 44(1), 93-100.   DOI
8 Feldman, M. (2011), "Hilbert transform in vibration analysis", Mech. Syst. Signal Pr., 25(3), 735-802.   DOI
9 Ernst, R. and Dual, J. (2014), "Acoustic emission localization in beams based on time reversed dispersion", Ultrasonics, 54(6), 1522-1533.   DOI
10 Esveld, C. (2001), Modern Railway Track, (2nd 2nd), MRT-Productions, Zaltbommel.
11 Feldman, M. (2011), "A signal decomposition or lowpass filtering with Hilbert transform?", Mech. Syst. Signal Pr., 25(8), 3205-3208.   DOI
12 Hamstad, M.A. and O'Gallagher, A. (2005), "Effects of noise on Lamb-mode acoustic-emission arrival times determined by wavelet transform", J. Acoust. Emiss., 23(1-24.
13 Hamstad, M.A., O'Gallagher, A. and Gary, J. (2002), "A wavelet transform applied to acoustic emission signals: Part 2: Source location", J. Acoust. Emiss., 20, 62-82.
14 He, Y., Yin, X. and Chu, F. (2008), "Modal analysis of rubbing acoustic emission for rotor-bearing system based on reassigned wavelet scalogram", J. Vib. Acoust., 130(6), 061009.   DOI
15 Li, S., Wang, X. and Zhao, M. (2015), "An improved cross-correlation method based on wavelet transform and energy feature extraction for pipeline leak detection", Smart Struct. Syst., 16(1), 213-222.   DOI
16 Holford, K.M., Davies, A.W., Pullin, R. and Carter, D.C. (2001), "Damage location in steel bridges by acoustic emission", J. Intel. Mat. Syst. Str., 12(8), 567-576.   DOI
17 Jiao, J., Wu, B. and He, C. (2008), "Acoustic emission source location methods using mode and frequency analysis", Struct. Control Health., 15(4), 642-651.   DOI
18 Li, D., Kuang, K.S.C. and Koh, C.G. (2015), "Detection and quantification of fatigue cracks in rail steel using acoustic emission technique", Structural Health Monitoring 2015, Proceedings of the 10th International Workshop on Structural Health Monitoring, Stanford,CA, September.
19 Ono, K. (2007), "Structural integrity evaluation using acoustic emission", J. Acoust. Emiss., 25, 1-20.
20 Nair, A. and Cai, C.S. (2010), "Acoustic emission monitoring of bridges: Review and case studies", Eng. Struct., 32(6), 1704-1714.   DOI
21 Pandya, D.H., Upadhyay, S.H. and Harsha, S.P. (2013), "Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN", Expert Syst. Appl., 40(10), 4137-4145.   DOI
22 Papaelias, M.P., Roberts, C. and Davis, C.L. (2008), "A review on non-destructive evaluation of rails: state-of-the-art and future development", P. I. Mech. Eng. F - J. Rai., 222(4), 367-384.   DOI
23 Thakkar, N.A., Steel, J.A. and Reuben, R.L. (2010), "Rail-wheel interaction monitoring using Acoustic Emission: A laboratory study of normal rolling signals with natural rail defects", Mech. Syst. Signal Pr., 24(1), 256-266.   DOI
24 Suzuki, H., Kinjo, T., Hayashi, Y., Takemoto, M., Ono, K. and Hayashi, Y. (1996), "Wavelet transform of acoustic emission signals", J. Acoust. Emiss., 14(2), 69-84.
25 Takemoto, M., Nishino, H. and Ono, K. (2000), Wavelet transform applications to AE signal analysis, in: T. Kishi, M. Ohtsu, S. Yuyama (Eds.) Acoustic Emission-Beyond the Millennium, Elsevier, Oxford, UK, pp. 35-56.
26 Teolis, A. (1998), Computational Signal Processing with Wavelets, Birkhauser, Boston.
27 Wang, Z.C., Geng, D., Ren, W.X., Chen, G.D. and Zhang, G.F. (2015), "Damage detection of nonlinear structures with analytical mode decomposition and Hilbert transform", Smart Struct. Syst., 15(1), 1-13.   DOI
28 Zhang, X., Feng, N., Wang, Y. and Shen, Y. (2014), "An analysis of the simulated acoustic emission sources with different propagation distances, types and depths for rail defect detection", Appl. Acoust., 86(0), 80-88.   DOI
29 Zhang, X., Feng, N., Wang, Y. and Shen, Y. (2015), "Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy", J. Sound Vib., 339(419-432.   DOI