Multi-fault classification based on support vector machine trained by chaos particle swarm optimization

被引：96
|
作者：
Tang, Xianlun [1 ]
Zhuang, Ling [1 ]
Cai, Jun [1 ]
Li, Changbing [1 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Minist Educ, Key Lab Network Control & Intelligent Instrument, Chongqing 400065, Peoples R China
来源：
KNOWLEDGE-BASED SYSTEMS | 2010年 / 23卷 / 05期
关键词：
Multi-fault classification; Support vector machine (SVM); Chaos; Particle swarm optimization (PSO); DIAGNOSIS;
D O I：
10.1016/j.knosys.2010.01.004
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel method of training support vector machine (SVM) by using chaos particle swarm optimization (CPSO) is proposed. A multi-fault classification model based on the SVM trained by CPSO is established and applied to the fault diagnosis of rotating machines. The results show that the method of training SVM using CPSO is feasible, the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, the precision and reliability of the fault classification results can meet the requirement of practical application. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码：486 / 490
页数：5
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