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| A Signal Feature Extraction Method and Its Application Based on EEMD Fuzzy Entropy and GK Clustering |
| JIN Mei1,LI Pan1,ZHANG Li-guo1,JIN Ju2,ZHANG Shu-qing1 |
1.Measurement Technology and Instrumentation Key lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China;
2.School of Civil Engineering, Hebei University of Technology, Tianjin 300401, China |
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Abstract A method of feature extraction combining ensemble empirical mode decomposition with fuzzy entropy, and Gustafaon-Kessel clustering to the rolling bearing fault diagnosis . is introduced.Firstly, rolling bearing vibration signal is decomposed into a series of IMFs.Secondly, IMFs are chosen by the criteria of correlation, and the fuzzy entropies of the chosen IMF component compose eigenvectors. Finally, the constructed eigenvectors are put into GK classifier to recognize different fault types. Experiments show that fuzzy entropy can characterize the feature information of the fault signal better than sample entropy and approximate entropy do, and the result of GK clustering is superior to FCM’s. So, experimental results show that the rolling bearing fault diagnosis method based on EEMD fuzzy entropy and GK clustering is effective and superiority.
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2015, Vol. 36 
