期刊
MEASUREMENT
卷 47, 期 -, 页码 576-590出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2013.08.021
关键词
Multi-kernel support vector machine; Chaotic search; Particle swarm optimization; Roller bearing; Fault diagnosis
资金
- National Natural Science Foundation of China [51275546]
- Chongqing Natural Science Foundation for Distinguished Young Scholars [cstc2011jjjq70001]
- Fundamental Research Funds for the State Key Laboratory of Mechanical Transmission in Chongqing University [SKLMT-ZZKT-2012 MS 09]
A novel intelligent fault diagnosis model based on multi-kernel support vector machine (MSVM) with chaotic particle swarm optimization (CPSO) for roller bearing fault diagnosis is proposed. Multi-kernel support vector machine is a powerful new tool for roller bearing fault diagnosis with small sampling, nonlinearity and high dimension. Chaotic particle swarm optimization is developed in this study to determine the optimal parameters for MSVM with high accuracy and great generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by time-domain, frequency-domain and empirical mode decomposition (EMD) and the typical manifold learning method LTSA is used to select salient features. The experimental results indicate that this proposed approach is an effective method for roller bearing fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the single kernel SVM or the MSVM which parameters are randomly extracted. (C) 2013 Published by Elsevier Ltd.
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