基于内积延拓LMD及SVM的轴承故障诊断方法研究

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Abstract Aimed at Local Mean Decomposition(LMD) end effect in feature extraction and Artificial Neural Network(ANN) having disadvantages of convergence slow and over learning in pattern recognition, the paper proposes a bearing fault diagnosis method based on integral waveform extension LMD and Support Vector Machine(SVM). Firstly, it extends the analyzed signal and decomposes it by the method based on integral waveform extension LMD to inhibit end effect; And use the main component , describing the signal characteristics, to establish the initial eigenvector matrix; Then decompose the initial eigenvector matrix by Singular Value Decomposition (SVD) method to achieve characteristic parameters and train the SVM. Finally, use the trained SVM to test and pattern classify. Through the experiments of analyzing actual bearing fault signals and fault types classification, the method not only inhibits the LMD end effects better, but also it avoids the ANN disadvantages, convergence slow and over learning in pattern recognition, and realizes the fault type classify accurately. The method can be used to bearing fault diagnose.

Key words： integral waveform extension LMD；SVD；SVM； roll bearing；fault diagnose

Received: 26 December 2014 Published: 15 March 2016

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	Articles by authors
	JIANG Jiu-liang
	LIU Wen-yi
	HOU Yu-jie
	ZHONG Zhao-ming
	CHEN Si-yao

Cite this article:

JIANG Jiu-liang,LIU Wen-yi,HOU Yu-jie, et al. Research on bearing fault diagnosis method based on integral waveform extension LMD and SVM[J]. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(6): 104-108.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2016/V35/I6/104

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