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

Authors:
Xianlun Tang;Ling Zhuang;Jun Cai;Changbing Li
Affiliations:
Key Laboratory of Network Control and Intelligent Instrument (Chongqing University of Posts and Telecommunications), Ministry of Education, Chongqing University of Posts and Telecommunications, Ch ...;Key Laboratory of Network Control and Intelligent Instrument (Chongqing University of Posts and Telecommunications), Ministry of Education, Chongqing University of Posts and Telecommunications, Ch ...;Key Laboratory of Network Control and Intelligent Instrument (Chongqing University of Posts and Telecommunications), Ministry of Education, Chongqing University of Posts and Telecommunications, Ch ...;Key Laboratory of Network Control and Intelligent Instrument (Chongqing University of Posts and Telecommunications), Ministry of Education, Chongqing University of Posts and Telecommunications, Ch ...
Venue:
Knowledge-Based Systems
Year:
2010

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Abstract

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.