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Sensors 2018, 18(6), 1934; https://doi.org/10.3390/s18061934

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier

1
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2
Center for Industrial Production, Aalborg University, 9220 Aalborg, Denmark
*
Author to whom correspondence should be addressed.
Received: 7 May 2018 / Revised: 12 June 2018 / Accepted: 12 June 2018 / Published: 14 June 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
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Abstract

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available. View Full-Text
Keywords: rolling bearing; fault diagnosis; WPE; SVM ensemble classifier; hybrid voting strategy rolling bearing; fault diagnosis; WPE; SVM ensemble classifier; hybrid voting strategy
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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. (CC BY 4.0).
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Zhou, S.; Qian, S.; Chang, W.; Xiao, Y.; Cheng, Y. A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier. Sensors 2018, 18, 1934.

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