Wavelet Transform-based Higher-order Statistics for Fault Diagnosis in Rolling Element Bearings

Abstract

Signal processing plays a pivotal role in fault diagnostics of mechanical systems. An approach, viz. wavelet transform-based higher-order statistics, was developed in this paper for fault diagnosis in rolling element bearings. In the approach, wavelet transform (discrete wavelet and wavelet packet transform) was introduced into a fourth-order statistic, kurtosis. Thereinto, discrete wavelet transform-based kurtosis (DWTK) was applied to signals to get a higher resolution in low-frequency bands1 on the other hand, wavelet packet transform-based kurtosis (WPTK) was applied to obtain a relatively high resolution in high-frequency bands in comparison with the DWTK. DWTK, WPTK and wavelet transform-based kurtosis (WTK) curves were introduced to calibrate the in-field signals in comparison with the benchmark signals, whereby the non-stationary transients and singularity in the vibration signals attributed to damage were detected. WTK curves of vibration signals collected from bearing with damage of different severities and locations were evaluated for damage detection and classification. The results demonstrated the excellent capability of the WTK in vibration signal processing and fault diagnosis.

Keywords

Wavelet transform higher-order statistics vibration bearing fault diagnosis.

Get full access to this article

View all access options for this article.

References

Angrisani, L. , 2005, "A wavelet packet transform-based approach for interference measurement in spread spectrum wireless communication systems ," IEEE Transactions on Instrumentation and Measurement 54, 2272-2280.

Baillie, D.C. and Mathew, J. , 1996, "A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings," Mechanical Systems and Signal Processing 10, 1-17.

Baydar, N. and Ball, A. , 2001, "A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution," Mechanical Systems and Signal Processing 15, 1091-1107.

Baydar, N. , Chen, Q. , Ball, A. , and Kruger, U. , 2001, "Detection of incipient tooth defect in helical gears using multivariate statistics," Mechanical Systems and Signal Processing 15, 303-321.

Chow, T.W.S. and Fei, G. , 1995, "Three phase induction machines asymmetrical faults identification using bispectrum," IEEE Transactions on Energy Conversion 10, 688-693.

Daubechies, I. , 1992, Ten Lectures on Wavelets, Capital City Press, Philadelphia, Pennsylvania , Montpelier, Vermont.

Hadjileontiadis, L.J. , Douka, E. , and Trochidis, A. , 2005, "Crack detection in beams using kurtosis," Computers & Structures 83, 909-919.

Ho, D. and Randall, R.B. , 2000, "Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals," Mechanical Systems and Signal Processing 14, 763-788.

Li, F. , Su, Z. , Ye, L. , and Meng, G. , 2006, "A correlation filtering-based matching pursuit (CF-MP) for damage identification using Lamb waves," Smart Materials and Structures 15, 1585-1594.

10.

Liu, Z.X. , Yin, X.G. , Zhang, Z. , Chen, D.S. , and Chen, W. , 2004, "Online rotor mixed fault diagnosis way based on spectrum analysis of instantaneous power in squirrel cage induction motors ," IEEE Transactions on Energy Conversion 19, 485-490.

11.

Loughlin, P. , Cakrak, F. , and Cohen, L. , 2000, "Conditional moments analysis of transients with application to helicopter fault data," Mechanical Systems and Signal Processing 14, 511-522.

12.

Luo, G.Y. , Osypiw, D. , and Irle, M. , 2003, "On-line vibration analysis with fast continuous wavelet algorithm for condition monitoring of bearing," Journal of Vibration and Control 9, 931-947.

13.

Mendel, J.M. , 1991, "Tutorial on higher-order statistics (Spectra) in signal-processing and system-theory -theoretical results and some applications ," Proceedings of the IEEE 79, 278-305.

14.

Pan, M.C. , Van Brussel, H. , Sas, P. , and Verbeure, B. , 1998, "Fault diagnosis of joint backlash," ASME Journal of Vibration and Acoustics 120, 13-24.

15.

Qiu, H. , Lee, J. , Lin, J. , and Yu, G. , 2005, "Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics," Journal of Sound and Vibration 289: 1066-1090.

16.

Sun, Q. and Tang, Y. , 2002, "Singularity analysis using continuous wavelet transform for bearing fault diagnosis," Mechanical Systems and Signal Processing 16, 1025-1041.

17.

Tonshoff, H.K. , Li, X.L. , and Lapp, C. , 2003, "Application of fast haar transform and concurrent learning to tool-breakage detection in milling," IEEE-ASME Transactions on Mechatronics 8, 414-418.

18.

Vandiver, J.K. , 2000, "Predicting lock-in on drilling risers in sheared flows ," in Proceedings of the Flow-Induced Vibration 2000 Conference, Lucerne, Switzerland, June 18-22.

19.

Wang, W.J. and McFadden, P.D. , 1993, "Early detection of gear failure by vibration analysis. 1. Calculation of the time-frequency distribution," Mechanical Systems and Signal Processing 7, 193-203.

20.

Wang, W.J. and McFadden, P.D. , 1996, "Application of wavelets to gearbox vibration signals for fault detection," Journal of Sound and Vibration 192, 927-939.

21.

Wang, W.J. and Lin, R.M. , 2003, "The application of pseudo-phase portrait in machine condition monitoring," Journal of Sound and Vibration 259: 1-16.

22.

Yang, D.M. , Stronach, A.F. , MacConnell, P. , and Penman, J. , 2002, "Third-order spectral techniques for the diagnosis of motor bearing condition using artificial neural networks," Mechanical Systems and Signal Processing 16, 391-411.

23.

Yen, G.G. and Lin, K.C. , 2000, "Wavelet packet feature extraction for vibration monitoring ," IEEE Transactions on Industrial Electronics 47: 650-667.