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Rolling bearing fault diagnosis method based on wavelet packet entropy and clustering analysis


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Rolling bearing fault diagnosis method based on wavelet packet entropy and clustering analysis
Yang QingSun BaicongZhu MeichenYang QingchuanLiu Nian
School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,China
wavelet packet entropy subtractive clustering rolling bearing fault diagnosis K-means clustering
In order to improve the fault diagnosis accuracy of rolling bearing vibration signals,an ensemble approach based on wavelet packet entropy and clustering analysis is presented here.The method of wavelet packet is used to decompose rolling bearing vibration signals into three-layer,and extract the energy characteristics.The vibration signal energy distribution is used as the probability distribution to do the information entropy calculations and extract the vibration signal characteristics.To detect faults,combined with subtractive clustering,the K-means clustering method of optimizing initial cluster centers by the principle of highest density index is proposed.To test the effectiveness of the proposed method,the actual bearing data of rolling bearing with different fault diameters are provided in the experiment.The results show that the proposed approach avoids the sensibility of traditional K-means clustering to initial cluster centers and its result can be used as a basis for rolling bearing fault diagnosis.


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Last Update: 2013-08-31