Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

Volumn 224, Issue 5, 2010, Pages 557-573

  Zhang, L ^a   Xiong, G ^b   Liu, H ^c   Zou, H ^a   Guo, W ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b EAST CHINA JIAOTONG UNIVERSITY   (China)

^c SHANGRAO NORMAL UNIVERSITY   (China)

Author keywords

bearing fault diagnosis; lifting scheme; neural networks; support vector machines; wavelet

Indexed keywords

BEARING CONDITIONS; BEARING FAULT; BEARING FAULT DIAGNOSIS; CLASSIFICATION RATES; COMPUTATIONAL BURDEN; CURSE OF DIMENSIONALITY; DATA PARTITION; DECOMPOSITION TREES; DIFFERENT FREQUENCY; ENERGY METHOD; FAULT CLASSIFICATION; FAULT DIAGNOSIS; FEATURE DIMENSIONS; LIFTING SCHEMES; LIFTING WAVELET; NEURAL NETWORK CLASSIFIER; SALIENT FEATURES; SAMPLE ENTROPY; SIGNAL COMPONENTS; SUB-BANDS; SUPPORT VECTOR; VIBRATION SIGNAL; WAVELET;

BEARINGS (STRUCTURAL); ELECTRIC FAULT CURRENTS; ENTROPY; FEATURE EXTRACTION; FREQUENCY BANDS; GENETIC ALGORITHMS; INDUCTION MOTORS; LAKES; NEURAL NETWORKS; OPTIMIZATION; SIGNAL PROCESSING; VECTORS; WAVELET TRANSFORMS;

SUPPORT VECTOR MACHINES;

EID: 77956568473     PISSN: 09596518     EISSN: None     Source Type: Journal    
DOI: 10.1243/09596518JSCE991     Document Type: Article

References (49)

1. Gao, Y. and Zhang, Q. A wavelet packet and residual analysis based method for hydraulic pump health diagnosis. Proc. IMechE, Part D: J. Automobile Engineering, 2006, 220(D6), 735-745. DOI: 10.1243/09544070JAUTO253. (Pubitemid 46478115)
2. Jiang, W., Spurgeon, S. K., Twiddle, J. A., Schlindwein, F. S., Feng, Y., and Thanagasundram, S. A wavelet cluster-based band-pass filtering and envelope demodulation approach with application to fault diagnosis in a dry vacuum pump. Proc. IMechE, Part C: J. Mechanical Engineering Science, 2007, 221(C11), 1279-1286. DOI: 10.1243/09544062 JMES544.
3. Shao, Y. and Nezu, K. Extracting symptoms of bearing faults in the wavelet domain. Proc. IMechE, Part I: J. Systems and Control Engineering, 2004, 218(I1), 39-51.
4. Pan, Y., Chen, J., and Guo, L. Robust bearing performance degradation assessment method based on improved wavelet packet-support vector data description. Mech. Syst. Signal Process., 2009, 23(3), 669-681.
5. Huang, Y., Liu, C., Zha, X. F., and Li, Y. An enhanced feature extraction model using liftingbased wavelet packet transform scheme and sampling-importance-resampling analysis. Mech. Syst. Signal Process., 2009, 23(8), 2470-2487.
6. Rafiee, J., Arvani, F., Harifi, A., and Sadeghi, M. H. Intelligent condition monitoring of a gearbox using artificial neural network. Mech. Syst. Signal Process., 2007, 21(4), 1746-1754.
7. Rafiee, J., Tse, P. W., Harifi, A., and Sadeghi, M. H. A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system. Expert Syst. Applic., 2009, 36(3), 4862-4875.
8. Hu, Q., He, Z. J., Zhang, Z. S., and Zi, Y. Y. Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mech. Syst. Signal Process., 2007, 21(2), 688-705.
9. He, Z. and Zi, Y. New approach of wavelet fractal analysis to mechanical fault diagnosis. In Proceedings of the ASME 2001 Technical Conference on Design engineering and Conference on Computers and information in engineering, Pittsburgh, Pennsylvania, 9-12 September 2001, pp. 9-12 (American Society of Mechanical Engineers, New York).
10. Chen, C. H., Shyu, R. J., and Ma, C. K. Rotating machinery diagnosis using wavelet packets-fractal technology and neural networks. J. Mech. Sci. Technol., 2007, 21(7), 1058-1065.
11. Chen, C. H., Shyu, R. J., and Ma, C. K. A new fault diagnosis method of rotating machinery. Shock Vib., 2008, 15(6), 585-598.
12. Yan, R. and Gao, R. X. Approximate entropy as a diagnostic tool for machine health monitoring. Mech. Syst. Signal Process., 2007, 21(2), 824-839.
13. Jiang, J. D., Chen, J., and Qu, L. S. The application of correlation dimension in gearbox condition monitoring. J. Sound Vib., 1999, 223(4), 529-541.
14. Wang, W., Chen, J., and Wu, Z. The application of a correlation dimension in large rotating machinery fault diagnosis. Proc. IMechE, Part C: J. Mechanical Engineering Science, 2000, 214(C7), 921-930. DOI: 10.1243/0954406001523155.
15. Zhang, L., Xiong, G. L., Liu, H. S., and Zou, H. J. Bearing condition identification using improved multiscale entropy and SVMs. Key Engng Mater., 2009, 419-420, 817-820.
16. Zhang, L., Xiong, G., Liu, H., Zou, H., and Guo, W. An intelligent fault diagnosis method based on multiscale entropy and SVMs. Advances in Neural Networks-ISNN 2009, 2009, 724-732.
17. Richman, J. S. and Moorman, J. R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol., 2000, 278(6), H2039-H2049.
18. Al-Angari, H. M. and Sahakian, A. V. Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome. IEEE Trans. Bio-Med. Engng, 2007, 54(10), 1900-1904.
19. Alcaraz, R. and Rieta, J. J. Sample entropy of the main atrial wave predicts spontaneous termination of paroxysmal atrial fibrillation. Med. Engng Phys., 2009, 31(8), 917-922.
20. Sweldens, W. The lifting scheme: a custom-design construction of biorthogonal wavelets. Appl. Comput. Harmon. A, 1996, 3(2), 186-200.
21. Zhou, R., Bao, W., Li, N., Huang, X., and Yu, D. Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform. Digit. Signal Process., 2010, 20(1), 276-288.
22. Jiang, H. K., He, Z. J., Duan, C. D., and Chen, P. Gearbox fault diagnosis using adaptive redundant lifting scheme. Mech. Syst. Signal Process., 2006, 20(8), 1992-2006.
23. Li, Z., He, Z. J., Zi, Y. Y., and Chen, X. F. Bearing condition monitoring based on shock pulse method and improved redundant lifting scheme. Math. Comput. Simulat., 2008, 79(3), 318-338.
24. Bao, W., Wang, W., Zhou, R., Li, N., Yang, J., and Yu, D. Application of a two-dimensional lifting wavelet transform to rotating mechanical vibration data compression. Proc. IMechE, Part C: J. Mechanical Engineering Science, 2009, 223(C10), 2443-2449. DOI: 10.1243/09544062JMES1514.
25. Çelik, M. and Bayir, R. Fault detection in internal combustion engines using fuzzy logic. Proc. IMechE, Part D: J. Automobile Engineering, 2007, 221(D5), 579-587. DOI: 10.1243/09544070JAUTO366. (Pubitemid 46988358)
26. Twiddle, J. A. and Jones, N. B. Fuzzy model-based condition monitoring and fault diagnosis of a diesel engine cooling system. Proc. IMechE, Part I: J. Systems and Control Engineering, 2002, 216(I3), 215-224.
27. Althoefer, K., Lara, B., Zweiri, Y. H., and Seneviratne, L. D. Automated failure classification for assembly with self-tapping threaded fastenings using artificial neural networks. Proc. IMechE, Part C: J. Mechanical Engineering Science, 2008, 222(C6), 1081-1095. DOI: 10.1243/09544062JMES546.
28. McCormick, A. C. and Nandi, A. K. Classification of the rotating machine condition using artificial neural networks. Proc. IMechE, Part C: J. Mechanical Engineering Science, 1997, 211(C6), 439-450. DOI: 10.1243/0954406971521845.
29. Ilott, P. W. and Griffiths, A. J. Fault diagnosis of pumping machinery using artificial neural networks. Proc. IMechE, Part E: J. Process Mechanical Engineering, 1997, 211(E3), 185-194. DOI: 10.1243/ 0954408971529665.
30. Nahvi, H. and Esfahanian, M. Fault identification in rotating machinery using artificial neural networks. Proc. IMechE, Part C: J. Mechanical Engineering Science, 2005, 219(C2), 141-158. DOI: 10.1243/095440605X8469. (Pubitemid 40473151)
31. Crowther, W. J., Edge, K. A., Burrows, C. R., Atkinson, R. M., and Woollons, D. J. Fault diagnosis of a hydraulic actuator circuit using neural networks-an output vector space classification approach. Proc. IMechE, Part I: J. Systems and Control Engineering, 1998, 212(I1), 57-68. DOI: 10.1243/0959651981539299.
32. Samanta, B. and Al-Balushi, K. R. Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system. Proc. IMechE, Part I: J. Systems and Control Engineering, 2001, 215(I3), 199-207. DOI: 10.1243/0959651011540987. (Pubitemid 34459590)
33. Le, T. T., Watton, J., and Pham, D. T. An artificial neural network based approach to fault diagnosis and classification of fluid power systems. Proc. IMechE, Part I: J. Systems and Control Engineering, 1997, 211(I4), 307-317. DOI: 10.1243/0959651971539830.
34. Le, T. T., Watton, J., and Pham, D. T. Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks. Proc. IMechE, Part I: J. Systems and Control Engineering, 1998, 212(I2), 87-97. DOI: 10.1243/0959651981539325. (Pubitemid 28349243)
35. Martin, K. F. and Marzi, M. H. Diagnostics of a coolant system via neural networks. Proc. IMechE, Part I: J. Systems and Control Engineering, 1999, 213(I3), 229-242. DOI: 10.1243/0959651991540106. (Pubitemid 43057469)
36. Jack, L. B. and Nandi, A. K. Support vector machines for detection and characterization of rolling element bearing faults. Proc. IMechE, Part C: J. Mechanical Engineering Science, 2001, 215(C9), 1065-1074.
37. Gani, E. and Manzie, C. Misfire-misfuel classification using support vector machines. Proc. IMechE, Part D: J. Automobile Engineering, 2007, 221(D9), 1183-1195. DOI: 10.1243/09544070JAUTO301.
38. Zhao, F., Chen, J., and Xu, W. Condition prediction based on wavelet packet transform and least squares support vector machine methods. Proc. IMechE, Part E: J. Process Mechanical Engineering, 2009, 223(E2), 71-79. DOI: 10.1243/09544089JPME220.
39. Wang, X. H., Mao, H. L., Zhu, C. M., and Huang, Z. F. Damage localization in hydraulic turbine blades using kernel-independent component analysis and support vector machines. Proc. IMechE, Part C: J. Mechanical Engineering Science, 2009, 223(C2), 525-529. DOI: 10.1243/09544062JMES1296.
40. Zhang, X. L., Chen, X. F., and He, Z. J. Fault diagnosis based on support vector machines with parameters optimization by ant colony algorithm. Proc. IMechE, Part C: J. Mechanical Engineering Science, 2010, 224(C1), 217-229. DOI: 10.1243/ 09544062JMES1731.
41. Pincus, S. M. Approximate entropy as a measure of system complexity. Proc. Natl Acad. Sci. USA, 1991, 88(6), 2297-2301.
42. Vapnik, V. The nature of statistical learning theory[M], 1995 (Springer-Verlag, New York).
43. Comak, E., Arslan, A., and Turkoglu, I. A decision support system based on support vector machines for diagnosis of the heart valve diseases. Comput. Biol. Med., 2007, 37(1), 21-27. (Pubitemid 44584252)
44. Shih, P. C. and Liu, C. J. Face detection using discriminating feature analysis and support vector machine. Pattern Recogn., 2006, 39(2), 260-276.
45. Hsu, C., Chang, C., and Lin, C. A practical guide to support vector classification, 19 May 2009, available from http://www.csie.ntu.edu.tw/,cjlin/ papers/guide/guide.pdf.
46. Huang, C. and Wang, C. A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Applic., 2006, 31(2), 231-240.
47. Loparo, K. A. Bearing vibration data, available from http://www.eecs. case.edu/laboratory/bearing/ welcome-overview.htm accessed June 2008.
48. Yang, D., Stronach, A., and MacConnell, P. Thirdorder spectral techniques for the diagnosis of motor bearing condition using artificial neural networks. Mech. Syst. Signal Process., 2002, 31(2-3), 391-411.
49. Kowalski, C. T. and Orlowska-Kowalska, T. Neural networks application for induction motor faults diagnosis. Math. Comput. Simulat., 2003, 63(3-5), 435-448.