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| Rolling Bearing Fault Diagnosis Based on Multiscale Entropy and Distance Evaluation |
| XIE Ping,JIANG Guo-qian,WU Xin,LI Xiao-li |
| Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract In order to solve the difficulty in extracting nonlinear fault features, a new feature extraction method based on multiscale entropy and distance evaluation technique is proposed.First, multiscale entropy analysis is performed to extract and characterize detail features over different scales from bearing vibration signals.Second, distance evaluation approach is employed to evaluate the extracted features and select some sensitive features as subsets.The subsets are input into support vector machines for further condition classification and fault identification.Bearing fault diagnosis experiments validate the effectiveness of the proposed method.The experimental results demonstrate that the optimal sensitive feature subsets outperformed the single scale feature, the original feature sets and other random feature subsets in diagnosis accuracy.
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2013, Vol. 34 
