Robust bearing performance degradation assessment method based on improved wavelet packet-support vector data description

Mechanical Systems and Signal Processing

Volumn 23, Issue 3, 2009, Pages 669-681

  Pan, Yuna ^a   Chen, Jin ^a   Guo, Lei ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Bearing accelerated life test; Improved wavelet packet decomposition; Performance degradation assessment; Support vector data description

Indexed keywords

BEARINGS (STRUCTURAL); COMPUTER NETWORKS; DATA DESCRIPTION; DEGRADATION; HEALTH; IMAGE SEGMENTATION; MAINTAINABILITY; MAINTENANCE; TESTING; WAVELET ANALYSIS; WAVELET DECOMPOSITION;

BEARING ACCELERATED LIFE TEST; BEARING PERFORMANCES; FEATURE VECTORS; HEALTH INDICES; IMPROVED WAVELET PACKET DECOMPOSITION; MAXIMUM PRODUCTIVITIES; MODEL FITTINGS; NODE ENERGIES; PERFORMANCE DEGRADATION ASSESSMENT; PROACTIVE MAINTENANCES; RESEARCH RESULTS; ROBUST METHODS; SUPPORT VECTOR DATA DESCRIPTION; TEST DATUMS; WAVELET PACKETS;

VECTORS;

EID: 58049182562     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2008.05.011     Document Type: Article

References (30)

1. Gebraeel N., Lawley M., Liu R., and Parmeshwaran V. Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Transactions on Industrial Electronics 51 3 (2004) 694-700
2. McFadden P.D., and Smith J.D. Vibration monitoring of rolling element bearings by the high-frequency resonance technique-a review. Tribology International 17 1 (1984) 3-10
3. Jardine A.K.S., Lin D.M., and Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20 7 (2006) 1483-1510
4. Huang N.E., Shen Z., and Long S.R. A new view of nonlinear water waves: the Hilbert spectrum. Annual Review of Fluid Mechanics 31 (1999) 417-457
5. Peng Z.K., Tse P.W., and Chu F.L. A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing. Mechanical Systems and Signal Processing 19 5 (2005) 974-988
6. Crupi V., Guglielmino E., and Milazzo G. Neural-network-based system for novel fault detection in rotating machinery. Journal of Vibration and Control 10 8 (2004) 1137-1150
7. Samanta B., Al-Balushi K.R., and Al-Araimi S.A. Artificial neural networks and genetic algorithm for bearing fault detection. Soft Computing 10 3 (2006) 264-271
8. Ocak H., and Loparo K.A. HMM-based fault detection and diagnosis scheme for rolling element bearings. Journal of Vibration and Acoustics-Transactions of the ASME 127 4 (2005) 299-306
9. Nelwamondo F.V., Marwala T., and Mahola U. Early classifications of bearing faults using hidden Markov models, Gaussian mixture models, Mel-frequency Cepstral Coefficients and fractals. International Journal of Innovative Computing Information and Control 2 6 (2006) 1281-1299
10. Abbasion S., Rafsanjani A., Farshidianfar A., and Irani N. Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mechanical Systems and Signal Processing 21 7 (2007) 2933-2945
11. Shin H.J., Eom D.H., and Kim S.S. One-class support vector machines-an application in machine fault detection and classification. Computers & Industrial Engineering 48 2 (2005) 395-408
12. Widodo A., and Yang B.S. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing 21 6 (2007) 2560-2574
13. Qiu H., Lee J., Lin J., and Yu G. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Advanced Engineering Informatics 17 3-4 (2003) 127-140
14. N. Lybeck, S. Marble, B. Morton, Validating prognostic algorithms: a case study using comprehensive bearing fault data, in: Aerospace Conference, IEEE, 2007, pp. 1-9.
15. Lei Z., Qixin C., Lee J., and Lewis F.L. PCA-CMAC based machine performance degradation assessment. Journal of Southeast University (English Edition) 21 3 (2005) 299-303
16. Lee J. Measurement of machine performance degradation using a neural network model. Computers in Industry 30 3 (1996) 193-209
17. Pengju K., and Birtwhistle D. Condition assessment of power transformer onload tap changers using wavelet analysis and self-organizing map: field evaluation. IEEE Transactions on Power Delivery 18 1 (2003) 78-84
18. Huang R.Q., Xi L.F., Li X.L., Liu C.R., Qiu H., and Lee J. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing 21 1 (2007) 193-207
19. Yan J.H., and Lee J. Degradation assessment and fault modes classification using logistic regression. Journal of Manufacturing Science and Engineering-Transactions of the ASME 127 4 (2005) 912-914
20. Yan J., Koç M., and Lee J. A prognostic algorithm for machine performance assessment and its application. Production Planning and Control 15 (2004) 796-801
21. R.X. Gao, W. Changting, Y. Ruqiang, A.A.M.A. Malhi, A neural network approach to bearing health assessment, in: International Joint Conference on Neural Networks, 2006, pp. 899-906.
22. Ocak H., Loparo K.A., and Discenzo F.M. Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics. Journal of Sound and Vibration 302 4-5 (2007) 951-961
23. Lingjun L., Jie H., Wei H., Xinmin D., and Zhengjia H. Condition evaluation for mechanical equipment by means of support vector data description. Chinese Journal of Mechanical Science and Technology 24 12 (2005) 1426-1429
24. Sweldens W. The lifting scheme: a construction of second generation wavelets. SIAM Journal on Mathematical Analysis 29 2 (1998) 511-546
25. Chendong D., Lingjun L., and Zhengjia H. Application of second generation wavelet transform to fault diagnosis of diagnosis of rotating machinery. Chinese Journal of Mechanical Science and Technology 23 2 (2004) 224-226+229
26. Tax D.M.J., and Duin R.P.W. Support vector domain description. Pattern Recognition Letters 20 11-13 (1999) 1191-1199
27. Williams T., Ribadeneira X., Billington S., and Kurfess T. Rolling element bearing diagnostics in run-to-failure lifetime testing. Mechanical Systems and Signal Processing 15 5 (2001) 979-993
28. Sunnersjo C.S. Rolling bearing vibrations-the effects of geometrical imperfections and wear. Journal of Sound and Vibration 98 4 (1985) 455-474
29. http://www-ict.ewi.tudelft.nl/~davidt/dd_tools.html
30. Rubini R., and Meneghetti U. Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing 15 2 (2001) 287-302