Rolling Element Bearing Fault Diagnosis by Combining Adaptive Local Iterative Filtering, Modified Fuzzy Entropy and Support Vector Machine

Keheng Zhu; Liang Chen; Xiong Hu

doi:10.3390/e20120926

Rolling Element Bearing Fault Diagnosis by Combining Adaptive Local Iterative Filtering, Modified Fuzzy Entropy and Support Vector Machine

Entropy (Basel). 2018 Dec 4;20(12):926. doi: 10.3390/e20120926.

Authors

Keheng Zhu¹, Liang Chen², Xiong Hu¹

Affiliations

¹ School of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China.
² College of Energy and Power Engineering, Dalian University of Technology, Dalian 116023, China.

Abstract

A new fault feature extraction method for rolling element bearing is put forward in this paper based on the adaptive local iterative filtering (ALIF) algorithm and the modified fuzzy entropy. Due to the bearing vibration signals' non-stationary and nonlinear characteristics, the ALIF method, which is a new approach for the analysis of the non-stationary signals, is used to decompose the original vibration signals into a series of mode components. Fuzzy entropy (FuzzyEn) is a nonlinear dynamic parameter for measuring the signals' complexity. However, it only emphasizes the signals' local characteristics while neglecting its global fluctuation. Considering the global fluctuation of bearing vibration signals will change with the bearing working condition varying, we modified the FuzzyEn. The modified FuzzyEn (MFuzzyEn) of the first few modes obtained by the ALIF is utilized to form the fault feature vectors. Subsequently, the corresponding feature vectors are input into the multi-class SVM classifier to accomplish the bearing fault identification automatically. The experimental analysis demonstrates that the presented ALIF-MFuzzyEn-SVM approach can effectively recognize the different fault categories and different levels of bearing fault severity.

Keywords: SVM; adaptive local iterative filtering; fault diagnosis; modified fuzzy entropy; rolling element bearing.