Hybrid Fault Diagnosis Algorithm Based on Fusion Decision of Multiple LS-SVM Classifiers
Li Xinbin, Chen Yunqiang, ZHANG Shuqing
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Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004
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Received
Revised
Published
2012-09-19
2012-11-08
2013-10-15
Issue Date
2013-10-15
Abstract
The fault diagnosis key were the feature extraction and the classifier selection. This paper presented a hybrid diagnosis algorithm, which used features extraction and the fusion decision of multiple classifiers. The feature extraction method included the wavelet packet transform, EEMD, and the improved wavelet energy entropy, and each method may extract the feature information respectively. The features information was input to the classifiers group, which was consist of three LS-SVM classifiers, to make the initial diagnosis. SWDT was chosen to make the fusion decision of the diagnosis results. The experiments indicate that the method realize the reliable identification of different bearing fault, even the compound fault. It confirm the completeness of the different features information, and more reliability of the classifiers group fusion decision.
Li Xinbin;Chen Yunqiang;ZHANG Shuqing.
Hybrid Fault Diagnosis Algorithm Based on Fusion Decision of Multiple LS-SVM Classifiers[J]. Journal of Vibration and Shock, 2013, 32(19): 159-164
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