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Entropy 2012, 14(8), 1343-1356; doi:10.3390/e14081343
Article

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

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Received: 31 May 2012; in revised form: 26 June 2012 / Accepted: 24 July 2012 / Published: 27 July 2012
Download PDF [292 KB, uploaded 27 July 2012]
Abstract: 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).
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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

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.

AMA Style

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(8):1343-1356.

Chicago/Turabian Style

Wu, Shuen-De; Wu, Po-Hung; Wu, Chiu-Wen; Ding, Jian-Jiun; Wang, Chun-Chieh. 2012. "Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine." Entropy 14, no. 8: 1343-1356.


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