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Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

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Department of Mechatronic Technology, National Taiwan Normal University, Taipei 10610, Taiwan
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Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan
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Mechanical and Systems Research Laboratories, Industrial Technology Research Institute, Hsinchu 31040, Taiwan
*
Author to whom correspondence should be addressed.
Entropy 2012, 14(8), 1343-1356; https://doi.org/10.3390/e14081343
Received: 31 May 2012 / Revised: 26 June 2012 / Accepted: 24 July 2012 / Published: 27 July 2012
Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE) and multiscale entropy (MSE). View Full-Text
Keywords: fault diagnosis; machine vibration; multiscale; permutation entropy; multiscale permutation entropy; support vector machine fault diagnosis; machine vibration; multiscale; permutation entropy; multiscale permutation entropy; support vector machine
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Wu, S.-D.; Wu, P.-H.; Wu, C.-W.; Ding, J.-J.; Wang, C.-C. Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine. Entropy 2012, 14, 1343-1356.

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