Fault diagnosis of rolling bearing based on relevance vector machine and kernel principal component analysis

Journal of Vibroengineering

Volumn 16, Issue 1, 2014, Pages 57-69

  Wang, Bo ^a,b   Liu, Shulin ^a   Zhang, Hongli ^a   Jiang, Chao ^a  

^a SHANGHAI UNIVERSITY   (China)

^b CHUZHOU UNIVERSITY   (China)

Author keywords

Fault diagnosis; Kernel principle component analysis; Relevance vector machine; Rolling bearing

Indexed keywords

BEARINGS (MACHINE PARTS); FAILURE ANALYSIS; ROLLER BEARINGS; SUPPORT VECTOR MACHINES; VECTORS;

BETTER PERFORMANCE; KERNEL PRINCIPAL COMPONENT ANALYSES (KPCA); KERNEL PRINCIPLE COMPONENT ANALYSIS; PRINCIPAL COMPONENTS; RELEVANCE VECTOR MACHINE; ROLLING BEARINGS; WAVELET PACKET ENERGIES; WAVELET PACKET TRANSFORMS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84904195325     PISSN: 13928716     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. Orhan S., Akturk N., Celik V. Vibration monitoring for defect diagnosis of rolling element bearings as a predictive maintenance tool: Comprehensive case studies. NDT &E Internatonal, Vol. 39, Issue 4, 2006, p. 293-298.
2. Patil M. S., Mathew J., Rajendra Kumar P. K. Bearing signature analysis as a medium for fault detection: A review. Journal of Tribology-Transactions the Asme, Vol. 130, 2008, p. 0140011.
3. Ma H., Huang J., Zhang S. Y., et al. Nonlinear vibration characteristics of a rotor system with pedestal looseness fault under different loading conditions. Journal of Vibroengineering, Vol. 15, Issue 1, 2013, p. 406-418.
4. Kankar P. K., Sharma S. C., Harsha S. P. Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform. Journal of Vibration and Control, Vol. 17, Issue 14, 2011, p. 2081-2094.
5. Zhou Y., Chen J., Dong G. M., et al. Wigner-Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering science, Vol. 225, Issue 2, 2011, p. 2831-2847.
6. Zhu K. H., Song X. G., Xue D. X. Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient. Journal of vibroengineering, Vol. 15, Issue 2, 2013, p. 597-603.
7. Moosavian A., Ahmadi H., Tabatabaeefar A. Fault diagnosis of main engine journal bearing based on vibration analysis using Fisher linear discriminant, K-nearest neighbor and support vector machine. Journal of Vibroengineering, Vol. 14, Issue 2, 2012, p. 894-906.
8. Jayaswal P., Verma S. N., Wadhwani A. K. Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis. Journal of Vibration and Control, Vol. 17, Issue 8, 2011, p. 1131-1148.
9. Randall R. B., Antoni J. Rolling element bearing diagnostics-A tutorial. Mechanical Systems And Signal Processing, Vol. 25, Issue 2, 2011, p. 485-520.
10. R. R. Coifman, M. V. Wickerhauser Entropy-based algorithms for best basis selection. IEEE Trans. Inform. Theory, Vol. 38, 1992, p. 713-718.
11. He Q. B. Vibration signal classification by wavelet packet energy flow manifold learning. Jouranal of Sound and Vibration, Vol. 332, Issue 7, 2013, p. 1881-1894.
12. Ramirez-Villegas J. F., Ramirez-Moreno D. F. Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. Neurocomputing, Vol. 77, Issue 1, 2012, p. 82-100.
13. Scholkopf B., Smola A., Muller K. R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, Vol. 10, Issue 5, 1998, p. 1299-1319.
14. Chen C. H., Shyu R. J., Ma C. K. Rotating machinery diagnosis using wavelet packets-fractal technology and neural networks. Journal of Mechanical Science and Technology, Vol. 21, Issue 7, 2007, p. 1058-1065.
15. Niu Y. M., Wong Y. S., Hong G. S., et al. Multi-category classification of tool conditions using wavelet packets and ART2 network. Journal of Manufacturing Science and Engineering-transactions of the Asme, Vol. 120, Issue 4, 1998, p. 807-816.
16. Wu J. D., Chan J. J. Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network. Expert Systems with Applications, Vol. 36, Issue 5, 2009, p. 8862-8875.
17. Kumar H., Kumar T., Amarnath M., et al. Fault diagnosis of antifriction bearings through sound signals using support vector machine. Journal of vibroengineering, Vol. 14, Issue 4, 2012, p. 1601-1606.
18. Widodo A., Yang B. S. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems And Signal Processing, Vol. 21, Issue 6, 2007, p. 2560-2574.
19. Li L. M., Wen G. R., Ren J. Y., et al. Genetic algorithm for Lagrangian support vector machine optimization and its application in diagnostic practice. Journal of vibroengineering, Vol. 15, Issue 1, 2013, p. 1-8.
20. Tipping M. E. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, Vol. 1, Issue 3, 2001, p. 211-244.
21. Tolambiya A., Venkatraman S., Kalra P. K. Content-based image classification with wavelet relevance vector machines. Soft Computing, Vol. 14, Issue 2SI, 2010, p. 129-136.
22. Widodo A., Kim E. Y., Son J. D., et al. Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Systems with Applications, Vol. 36, Issue 3, 2009, p. 7252-7261.
23. Pearson K. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, Vol. 2, 1901, p. 559-872.
24. Tipping M. E., Faul A. C. Fast marginal likelihood maximization for sparse Bayesian models. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, NJ, USA, 2003.
25. P. Vlachos StatLib. http://lib.stat.cmu.edu.
26. Zhou J., Dasgupta D. Augmented Negative Selection Algorithm with Variable-Coverage Detectors. CEC2004. Institute of Electrical and Electronics Engineers Inc, 2004, p. 1081-1088.
27. Luo, W., Wang, X., Tan, Y., et al. A novel negative selection algorithm with an array of partial matching lengths for each detector. Lecture Notes in Computer Science. Springer Verlag, 2006, p. 112-121.
28. Hsu C. W., Lin C. J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, Vol. 13, Issue 2, 2002, p. 415-425.
29. Hu Q., He Z. J., Zhang Z. S., et al. Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems And Signal Processing, Vol. 21, Issue 2, 2007, p. 688-705.
30. Wang G., Wang Z. L., Qin X. D., et al. Accurate Diagnosis of Rolling Bearing Based on Wavelet Packet and RBF Neural Networks. Journal of University of Science and Technology Beijing, 2007, p. 184-187.