Machine fault diagnosis through an effective exact wavelet analysis

Journal of Sound and Vibration

Volumn 277, Issue 4-5, 2004, Pages 1005-1024

  Tse, Peter W ^a   Yang, Wen Xian ^b   Tam, H Y ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; BEARINGS (MACHINE PARTS); ERROR DETECTION; FOURIER TRANSFORMS; GENETIC ALGORITHMS; HARMONIC ANALYSIS; INSTRUMENT ERRORS; MACHINE COMPONENTS; OPTIMIZATION; VIBRATION CONTROL; WAVELET TRANSFORMS;

CONTINUOUS WAVELET TRANSFORMS (CWT); HARMONIC SIGNALS; MACHINE FAULTS; WAVELET ANALYSIS;

MACHINE VIBRATIONS;

EID: 4544275882     PISSN: 0022460X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jsv.2003.09.031     Document Type: Article

References (27)

1. Staszewski W.J. Structural and mechanical damage detection using wavelets. The Shock and Vibration Digest. 30:1998;457-472.
2. Paya B.A., Esat I.I., Badi M.N. Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a pre-processor. Mechanical System and Signal Processing. 11(5):1997;751-759.
3. Okafor A.C., Dutta A. Structural damage detection in beams by wavelet transforms. Smart Materials and Structures. 9(6):2000;906-917.
4. Wang W.J. Wavelet for detecting mechanical faults with high sensitivity. Mechanical System and Signal Processing. 15(4):2001;685-696.
5. Tse P., Peng Y.H., Yam R. Wavelet analysis and envelope detection for rolling element bearing fault diagnosis - their effectiveness and flexibility. Transactions of the American Society of Mechanical Engineers, Journal of Vibration and Acoustics. 123(3):2001;303-310.
6. Cempel C. Vibroacoustics Condition Monitoring, Ellis Horwood Series in Mechanical Engineering. 1991;Ellis Horwood Ltd. Chichester.
7. R.B. Randall, Frequency Analysis, Brüel & Kjaer Ltd., Nærum, pp. 263-269.
8. Liu B., Ling S.F. On the selection of informative wavelets for machinery diagnosis. Mechanical Systems and Signal Processing. 13(1):1999;145-162.
9. Newland D.E. Ridge and phase identification in the frequency analysis of transient signals by harmonic wavelets. Transactions of the American Society of Mechanical Engineers, Journal of Vibration and Acoustics. 121:1999;149-155.
10. Mallat S., Zhang Z. Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing. 41(12):1993;3397-3415.
11. Telfer B.A., Szu H.H., Dobeck G.J., Garcia J.P., Ko H., Dubey A., Witherspoon N. Adaptive wavelet classification of acoustic and backscatter and imagery. Optical Engineering. 33:1994;2192-2203.
12. Szu H.H., Telfer B.A., Kadambe S. Neural network adaptive wavelets for signal representation and classification. Optical Engineering. 31:1992;1907-1916.
13. Kocur C.M., Rogers S.K., Myers L.R., Burns T., Kabrisky M., et al. Using neural networks to select wavelet features for breast cancer diagnosis. IEEE Engineering in Medicine and Biology. 95-102(May/June):1996;108.
14. G. Tagliarini, E. Page, Genetic algorithms for adaptive wavelet design, SPIE Wavelet Applications III 2762, 8-12 April, Orlando, FL, 1996, pp. 82-93.
15. Galvao R.K.H., Yoneyama T., Rabello T.N. Signal representation by adaptive biased wavelet expansions. Digital Signal Processing. 9:1999;225-240.
16. Silva A.R.F.D. Evolutionary based methods for adaptive signal representation. Signal Processing. 81(5):2001;927-944.
17. I. Daubechies, Ten Lectures on Wavelets, Science for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1992, pp. 1-351.
18. P. Tse, W.X. Yang, A new wavelet transform for eliminating problems usually occurring in conventional wavelet transforms used for fault diagnosis, The Ninth International Congress on Sound and Vibration, ICSV'9, Orlando, Florida, Paper # 465, CD-ROM version, 2002.
19. P. Tse, W.X. Yang, The practical use of wavelet transforms and their limitations in machine fault diagnosis, International Symposium on Machine Condition Monitoring and Diagnosis invited paper, Tokyo, Japan, 2002, pp. 9-16.
20. Borde B.L. New wavelet class for fine structure identification. SPIE Wavelet Applications II. 2491-2499:1995;1073-1085.
21. Borde B.L. Generic explicit wavelet tap derivation. SPIE Wavelet Applications III. 2762:1996;94-104.
22. Luenberger D.G. Introduction to Linear and Nonlinear Programming. 1973;Addison-Wesley, Reading, MA.
23. Fletcher R. Practical Methods of Optimisation. 1986;Wiley, New York.
24. Rabow A.A., Scheraga H.A. Lattice neural network minimization application of neural network of optimisation for locating the global-minimization conformations of proteins. Journal of Molecular Biology. 232(4):1993;1157-1168.
25. Goldberg D.E. Genetic Algorithms in Search, Optimisation, and Machine Learning. 1989;Addison Wesley, Reading, MA.
26. D. Bently, Predictive maintenance through the monitoring and diagnostics of rolling element bearings, Applications Note, ANO44, Bently Nevada Co., 1989, pp. 2-8.
27. Ho D., Randall R.B. Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical System and Signal Processing. 14(5):2000;763-788.