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Sensors 2017, 17(3), 625; doi:10.3390/s17030625

Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings

1
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China
3
School of Mechanical & Electrical Engineering, Jiangsu Normal University, Xuzhou 221116, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 17 February 2017 / Revised: 13 March 2017 / Accepted: 15 March 2017 / Published: 18 March 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3314 KB, uploaded 18 March 2017]   |  

Abstract

This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results. The features based on wavelet packet decomposition are derived from the original signals. Then the intrinsic geometric features extracted by WKECA are fed into the support vector machine (SVM) classifier to recognize different operating conditions of bearings, and we obtain the overall accuracy (97%) for the experimental samples. The experimental results demonstrated the feasibility and effectiveness of the proposed method. View Full-Text
Keywords: fault diagnosis; weighted kernel entropy component analysis; dimensional reduction; Renyi entropy; feature extraction fault diagnosis; weighted kernel entropy component analysis; dimensional reduction; Renyi entropy; feature extraction
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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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MDPI and ACS Style

Zhou, H.; Shi, T.; Liao, G.; Xuan, J.; Duan, J.; Su, L.; He, Z.; Lai, W. Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings. Sensors 2017, 17, 625.

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