Application of multi-SVM classifier and hybrid GSAPSO algorithm for fault diagnosis of electrical machine drive system

Abstract

A method, being based on multi-class support vector machine (SVM) classifier and hybrid particle swarm optimization (PSO) and gravity search algorithm (GSA), is presented to diagnose the faults in electrical motor drive system. In this method, the global search ability of PSO and the local search ability of GSA are integrated to combine the advantages of PSO and GSA, and the multi-class SVM classifier is optimized by the hybrid GSAPSO algorithm to improve classification performance. To test the presented method, a series of simulation and experiment are studied. The diagnostic results display that the presented method can gain more precise classification accuracy than multi-class SVM with PSO and multi-class SVM with GSA.