Composite Multi-scale Fuzzy Entropy based Rolling Bearing Fault diagnosis method

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (8) : 116-123.

Jinde Zheng1 Haiyang Pan1 Junsheng Cheng2 Jun Zhang1

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Abstract

To precisely extract the linear fault features from rolling bearing vibration signal, a novel method for measuring the self-similarity and complexity of time series termed composite multi-scale fuzzy entropy (CMFE) is proposed, aiming at the coarse-grained way of multi-scale entropy (MSE). Compared with MSE, CMFE combines the information of multiple coarse-grained sequences and obtains more stable values with a better consistency. Based on the CMFE, Fisher score for feature selection and support vector machines, a newly intelligent rolling bearing fault diagnosis method is proposed. The proposed method is applied to analyze the rolling bearing experimental data by comparisons and the results have verified its effectiveness and superiority.

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multi-scale entropy / Composite multi-scale fuzzy entropy / feature selection / rolling bearing / fault diagnosis

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Jinde Zheng1 Haiyang Pan1 Junsheng Cheng2 Jun Zhang1 . Composite Multi-scale Fuzzy Entropy based Rolling Bearing Fault diagnosis method[J]. Journal of Vibration and Shock, 2016, 35(8): 116-123

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