Rolling Bearing Fault Diagnosis Considering Fault Location and Damage Degree Based on Smoothness Priors Approach

Abstract

In this paper, a rolling bearing fault diagnosis method based on smoothness priors approach considering bearing fault location and damage degree is proposed. Firstly, smoothness priors approach is used to adaptively decompose the bearing vibration signals to obtain the trend and detrended terms; then the combined permutation entropy and energy entropy are used to extract the fault features from the trend and detrended terms to obtain the information entropy feature vectors; finally, the information entropy feature vectors are input to the support vector classifier of sine cosine algorithm. This method is applied to the experimental data of rolling bearing. The analysis results show that the diagnosis effect of using the combination of permutation entropy and energy entropy to extract fault features is better than using only permutation entropy to extract fault features when the bearing fault location and damage degree are considered at the same time.