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CLC number: TH17; TP18

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Received: 2003-10-03

Revision Accepted: 2004-02-27

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Cited: 12

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.5 P.433~439

http://doi.org/10.1631/jzus.2005.A0433


Support Vector Machine for mechanical faults classification


Author(s):  JIANG Zhi-qiang, FU Han-guang, LI Ling-jun

Affiliation(s):  Zhengzhou Aeronautical Institute of Industry Management, Zhengzhou 450015, China; more

Corresponding email(s):   fhg64@263.net

Key Words:  Support Vector Machine (SVM), Fault diagnosis, Multi-fault classification, Intelligent diagnosis


JIANG Zhi-qiang, FU Han-guang, LI Ling-jun. Support Vector Machine for mechanical faults classification[J]. Journal of Zhejiang University Science A, 2005, 6(5): 433~439.

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author="JIANG Zhi-qiang, FU Han-guang, LI Ling-jun",
journal="Journal of Zhejiang University Science A",
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%I Zhejiang University Press & Springer
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T1 - Support Vector Machine for mechanical faults classification
A1 - JIANG Zhi-qiang
A1 - FU Han-guang
A1 - LI Ling-jun
J0 - Journal of Zhejiang University Science A
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SP - 433
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A0433


Abstract: 
support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based on binary classifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for on line diagnosis for mechanical system.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[2] Gao, J.F., Shi, W.G., Tan, J.X., Zhong, F.J., 2002. Support Vector Machines Based Approach for Fault Diagnosis of Valves in Reciprocating Pumps. IEEE Canadian Conference on Electrical & Computer Engineering, p.1622-1628.

[3] Guo, G.D., Li, S.Z., Chan, K.L., 2001. Support Vector Machine for face recognition. Image and Vision Computing, 19:631-638.

[4] Drucker, H., Wu, D., Joksons, D.W., 1999. Support Vector Machine for spam categorization. IEEE Trans on Neural Networks, 10:1048-1054.

[5] Hsu, C.W., Lin, C.J., 2002. A comparison of methods for multiclass Support Vector Machines. IEEE Trans on Neural Networks, 13:415-425.

[6] Rychetsky, M., Ortmann, S., Glesner, M., 1999. Support Vector Approaches for Engine Knock Detection. International Joint Conference on Neural Networks. IJCNN 99. Washington, USA, p.969-974.

[7] Sebald, D.J., Bucklew, J.A., 2000. Support Vector Machine techniques for nonlinear equalization. IEEE Trans on Signal Processing, 48:3217-3226.

[8] Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York, p.157-173.

[9] Yuan, Z.G., 2000. Artificial Neural Network and Its Application. Tsinghua University Press, Beijing, p.177-205 (in Chinese).

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