Multi-fault classification based on support vector machine trained by chaos particle swarm optimization |
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| Authors: | Xianlun Tang Ling Zhuang Jun Cai Changbing Li |
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| Affiliation: | 1. Indian Institute of Technology, Kharagpur 721302, India;2. Mechanical Engineering Department, Indian Institute of Technology, Kharagpur 721302, India;3. Reliability Engineering Centre, Indian Institute of Technology, Kharagpur 721302, India;1. College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, PR China;2. Huazhong University of Science and Technology, Wuhan 430074, PR China;1. School of Rail Transportation, Soochow University, Suzhou, Jiangsu Province, China;2. Wenzheng college of Soochow University, Suzhou, Jiangsu Province, China;1. School of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065, China;2. School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China;3. The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China;1. Research Laboratory, Matériaux, Mesures et Applications (MMA), INSAT, Tunisia;2. Institut National des Sciences Appliquées et de Technologie (INSAT), Carthage University, Tunisia;3. Research Laboratory, LR-SITI, Ecole National d’Ingénieurs de Tunis (ENIT), Tunisia |
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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. |
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