Method of Bearing Fault Identification Based on SVM Decision Tree

Hang Cheng; Xi Li; Zheng Bo Qin; Chao Yong Huang

doi:10.4028/www.scientific.net/AMM.190-191.1010

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 190-191Method of Bearing Fault Identification Based on...

Method of Bearing Fault Identification Based on SVM Decision Tree

Abstract:

This paper has put forward one method, combining with theory of decision tree and method of voting, and established one kind of multi-failure classifier of SVM, which could finish cross training, repeated classification and accumulative voting. This classifier could accomplish classification of failure bearings and do classification experiments among failures of kinds of bearings. The results have shown that this method could identify states of breakdown equipment for the purpose of diagnosis of failures of mechanical system accurately.

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Periodical:

Applied Mechanics and Materials (Volumes 190-191)

Pages:

1010-1015

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.190-191.1010

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Online since:

July 2012

Authors:

Hang Cheng, Xi Li, Zheng Bo Qin, Chao Yong Huang

Keywords:

Bearing, Decision Tree, Multi-Failure Classifier, Support Vector Machine (SVM)

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References

[1] Vapnik V. Essence of theory of statistics learning [M].Zhang Xue-gong, Translation.Peking: Tsinghua University Press, (2000)

Google Scholar

[2] Nello Cristianini. Introduction of SVM [M].Li Guo-zheng, Translation.Peking: Electronics industry Press, (2004)

Google Scholar

[3] Yuan Sheng-fa, Zhu Fu-lei. SVM and its application of diagnosis of mechanical failure [J]. Vibration and striking, 2007, 26 (11) : 29-34·

Google Scholar

[4] Sun Chang-er, Liu Bing-han. A new kind of decision tree of SVM [J] Fu zhou University Academic Journal. 2007,35(3):361-364.

Google Scholar

[5] He Xue-wen, Po Zhan-yong Method of mechanical diagnosis based on decomposition of wavelet packet and SVM [J]. Strengh of machinery, 2004,26(1):020-02

Google Scholar

[6] Wang Ji-sheng, Yu Jun-qing, Huang Wei-gong. Application of analysis of wavelet packet SVM in diagnosis of failure of cutting tools [J]. Viberation. Test and diagnosis, 2008,28(3):273-276

Google Scholar