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

被引：33
作者：
Ding, Shichuan [1 ]
Hao, Menglu [1 ]
Cui, Zhiwei [1 ]
Wang, Yinjiang [1 ]
Hang, Jun [1 ]
Li, Xueyi [1 ]
机构：
[1] Anhui Univ, Sch Elect Engn & Automat, Anhui, Peoples R China
来源：
ISA TRANSACTIONS | 2023年 / 133卷
基金：
中国国家自然科学基金;
关键词：
Electrical machine; Fault diagnosis; Support vector machine; Gravity search algorithm; Particle swarm optimization; ARTIFICIAL NEURAL-NETWORK;
D O I：
10.1016/j.isatra.2022.06.029
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：529 / 538
页数：10
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