A NEW ROLLER BEARING FAULT DIAGNOSIS METHOD BASED ON VMD ENERGY ENTROPY AND BSOA-LSSVM

Abstract

This paper presents a new method for roller bearing fault diagnosis based on least square support vector machine (LSSVM) with parameters optimized by Backtracking Search Optimization Algorithm (BSOA), namely BSOA-LSSVM.  First, roller bearing acceleration vibration signals are decomposed into functions by using Variational Mode Decomposition (VMD) method. Second, initial feature matrices are extracted from those functions by energy entropy to obtain feature matrix. Third, these values serve as input vector for BSOA - LSSVM classifier. Experimental results showed that the proposed method gave the higher classification accuracy (100%) and shorter computational time than other method.