All Title Author
Keywords Abstract

Sensors  2010 

Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

DOI: 10.3390/s100504602

Keywords: gearbox, support vector machines (SVM), wavelet lifting, rule-based reasoning (RBR), intelligent diagnosis

Full-Text   Cite this paper   Add to My Lib

Abstract:

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

References

[1]  Jack, L.B.; Nandi, A.K.; McCormick, A.C. Diagnosis of rolling element bearing faults using radial basis function net works. Appl. Signal Process?1999, 6, 25–32.
[2]  Li, L.J.; Zhang, Z.S.; He, Z.J. Research of mechanical system fault diagnosis based on support vector data description. J. Xi’an Jiiaotong Univ?2003, 37, 910–913.
[3]  Li, G.; Xing, S.B.; Xue, H.F. Comparison on pattern analysis performance of SVM and RVM based on RBF kernel. Appl. Res. Comput?2009, 26, 1782–1784.
[4]  Ezenwoye, O.; Sadjadi, S.M. Robust BP EL2: Transparent autonomization in business processes through dynamic proxies. Proceedings of the 8th International Symposium on Autonomous Decentralized Systems, Sedona, AZ, USA, March 21–23, 2007; pp. 17–24.
[5]  Jack, L.B.; Nandi, A.K. Support vector machines for detection and characterization of rolling element bearing faults. J. Mech. Eng. Sci?2001, 215, 1065–1074.
[6]  Jack, L.B.; Nandi, A.K. Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mech. Syst. Signal Process?2002, 16, 373–390.
[7]  Zhang, X.D.; Bao, Z. Non-Stationary Signal Analysis and Processing; National Defense Industry Press: Beijing, China, 1998; pp. 221–230.
[8]  Duan, C.D. Research on Fault Diagnosis Techniques Using Second Generation Wavelet Transform; College of Mechanical Engineering, Xi’an Jiaotong University: Xi’an, Shanxi, China, 2004; pp. 510–513.
[9]  Sweldens, W. The lifting scheme: a custom-design construction of biorthogonal wavelets. Appl. Comput. Harm. Anal?1996, 3, 186–200.
[10]  Vanraes, E.; Jansen, M. Stabilized lifting steps in noise reduction for non-equispaced samples. Pro. SPIE?2001, 4478, 105–116.
[11]  Samuel, P.; Pines, D. Helicopter transmission diagnostics using constrained adaptive lifting. Proceedings of American Helicopter Society 59th Annual Forum, Phoenix, AZ, USA, May 2003; pp. 6–8.
[12]  Duan, C.D.; He, Z.J. Second generation wavelet denoising and its application in machinery monitoring and diagnosis. Mini-micro Syst?2004, 25, 1341–1343.
[13]  Zhang, P.L.; Li, B.; Ren, G.Q. Wear pattern recognition of engine based on SFCE method. Chinese Int. Combustion Engine Eng?2008, 4, 81–84.
[14]  Song, X.; Guo, W.; Wang, Z.Y. Research on case adaptation mechanism based on regression analysis and rule-based reasoning. J. Tianjin Univ. (Science and Technology)?2009, 2, 95–100.
[15]  Xian, G.M.; Zeng, B.Q.; Tang, H. Fault diagnosis method Based on WPA-SVM. Comput. Eng?2006, 35, 212–214.
[16]  Zhu, Y.H.; Li, Y.L.; Liu, A.Z. SVM apply in small sample gear fault diagnosis. Coal Mine Mach?2006, 5, 48–51.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

微信:OALib Journal