Advanced system for automatically detecting faults occurring in bearings

Advanced System for Automatically Detecting Faults Occurring in Bearings

Volumn , Issue , 2010, Pages 1-80

  Tse, Peter W ^a   Leung, Jacko C ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINERY;

ADVANCED SYSTEMS; BEARING FAULT; BEARING FAULT DIAGNOSIS; CATASTROPHIC FAILURES; DAMPED OSCILLATIONS; VIBRATION SIGNAL;

FAULT DETECTION;

EID: 84892056314     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (29)

1. Kiral, Z. & Karagulle, H. (2006). Vibration analysis of rolling element bearing with defect under the action of unbalanced force. Mechanical System and Signal Processing, 20(8), 1967-1991.
2. Peng, Z., Chu, F. & He, Y. (2002). Vibration signal analysis and feature extraction based on reassignment wavelet scalogram. Journal of Sound and Vibration, 253(5), 1087-1100.
3. Tse, P. & Yang, W. (2002, September). The practical use of wavelet transforms and their limitations in machine fault diagnosis. International Symposium on Machine Condition Monitoring and Diagnosis, 9-16.
4. Tse, P., Yang, W. & Tam H. (2004). Machine fault diagnosis through an effective exact wavelet analysis. Journal of Sound and Vibration, 277(4-5), 1005-1024.
5. Neale, N. (1980). Condition monitoring methods and their interpretation. A guide to the condition monitoring of machines (pp.50-89). London: Department of Trade and Industry Press.
6. Wowk, V. (1991). Machinery monitoring. Machinery Vibration- Measurement and Analysis, (17-18). New York: McGraw-Hill Inc.
7. International Standard Organisation. (2002). Mechanical vibration-evaluation of machine vibration by measurements on non-rotating parts, 10816(1).
8. Lin, J. & Qu, L. (2000). Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. Journal of Sound and Vibration, 234(1), 135-148.
9. Lindsay, J. & Asoke, K. (2000). Genetic algorithms for feature selection in machine condition monitoring with vibration signals. Journal of Visual Image Signal Process, 147(3), 205-212.
10. Sadettin, O., Nizami, A. & Veli, C. (2007). Vibration monitoring for defect diagnosis or rolling element bearings as a predictive maintenance tool: comprehensive case studies. NDT & E International, 39(4), 293-298.
11. Tse, P. & Lai, W. (2003, August). Minimizing interference in bearing condition monitoring and diagnosis by using adaptive noise cancellation. International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management, 739-748.
12. Stein, M. & Shakarchi, R. (2003). Convergence of fourier series. Fourier analysis-an introduction, (69-99). New Jersey: Princeton University Press.
13. Li, C. & Ma, J. (1997). Wavelet decomposition of vibrations for detection of bearing-localized defects. NDT and E International, 30(5), 143-149.
14. Newland, D. (1994). Wavelet analysis of vibration-part 1: theory and part 2: wavelet Maps. Journal of Vibration and Acoustic, 116(5), 409-425.
15. Chui, K. (1992). Continuous wavelet transforms. An introduction to wavelet (7-9). Boston: Academic Press.
16. Tse, P. & Yang, W. (2002, July). 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, 465-473.
17. Tse, P. & Yang, W. (2003). Development of an advanced noise reduction method for vibration analysis based on singular value decomposition. NDT & E International, 36(6), 419-432.
18. Auger, F. & Flandrin, P. (1995). Improving the readability of time-frequency and time-representation by the reassignment method. IEEE Transactions on Signal Processing, 43(5), 1068-1089.
19. Lorenzo, F., & Calabro, M. (2007, June). Kurtosis: a statistical approach to identify defects in rolling bearing. International Conference on Marine Research and Transportation, 607-610.
20. Peled, R., Braun, S. & Zacksenhouse, M. (2005). A blind de-convolution separation of multiple sources with application to bearing diagnostics. Mechanical Systems and Signal Processing, 19(6), 1181-1195.
21. Antoni, J. & Randall, B. (2006). The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, 20(2), 308-331.
22. Dwyer, R. (1983). Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals, IEEE Journal of Oceanic Engineering, 9(2), 85-92.
23. Tse, P., Peng, Y. & Yam, R. (2001). Wavelet analysis and envelope detection for rolling element bearing fault diagnosis-their effectiveness and flexibilities. Journal of Vibration and Acoustics, 123(3), 303-310.
24. Antoni, J. (2006). The spectral kurtosis: a useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, 20(2), 282-307.
25. Sawalhi, N., Randall, B. & Endo, H. (2007). The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy de-convolution combined with spectral kurtosis. Mechanical Systems and Signal Processing, 21(6), 2616-2633.
26. Gupta, P. (1979). Dynamics of rolling element bearings part I and II. Journal of Lubrication Technology, 101(5), 293-318.
27. Ho, D. & Randall, B. (2000). Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical Systems and Signal Processing, 14(5), 763-788.
28. McFadden, P. & Smith, J. (1985). The vibration produced by multiple point defects in rolling element bearing. Journal of Sound and Vibration, 98(2), 263-273.
29. Ohta, H. & Sggimoto, N. (1996). Vibration characteristics of tapered roller bearing. Journal of Sound and Vibration, 190(2), 137-147.