Fault diagnosis of roller bearing based on ELMD sample entropy and Boosting-SVM

HE Zhi-jian1,2 ZHOU Zhi-xiong1

Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (18) : 190-195.

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PDF(1907 KB)
Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (18) : 190-195.

Fault diagnosis of roller bearing based on ELMD sample entropy and Boosting-SVM

  • HE Zhi-jian1,2  ZHOU Zhi-xiong1 
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Abstract

Aiming at the no stationary characteristic of a gear fault vibration signal, it proposes a recognition method based on sample entropy of ELMD (Ensemble local mean decomposition) and Boosting-SVM. First, the vibration signal was decomposed by ELMD, then a series of product function were obtained; Secondly, according to the decomposition characteristics of ELMD, an adaptive method based on K-L divergence was proposed to select principal PFs, then, calculate the sample entropy of principal PF component and combined into a feature vector; Finally, the feature vector were input Boosting-SVM classifier to train and test to identify the type of roller bearing faults. Experimental results show that this method can effectively diagnosis three kinds of working condition, and the effect is better than local mean decomposition method.
 

Key words

roller bearing / fault diagnosis / ensemble local mean decomposition / sample entropy

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HE Zhi-jian1,2 ZHOU Zhi-xiong1 . Fault diagnosis of roller bearing based on ELMD sample entropy and Boosting-SVM[J]. Journal of Vibration and Shock, 2016, 35(18): 190-195

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