Composite Multi-scale Fuzzy Entropy based Rolling Bearing Fault diagnosis method
Jinde Zheng1 Haiyang Pan1 Junsheng Cheng2 Jun Zhang1
1 School of Mechanical Engineering, Anhui University of Technology, Maanshan, Anhui, 243032
2 State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, Hunan, 410082
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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