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Entropy 2016, 18(7), 242; doi:10.3390/e18070242

Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

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1,3,* , 1,3
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1,4
and
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1
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2
School of Electric Engineering, Beijing Jiaotong University, Beijing 100044, China
3
Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China
4
School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China
5
School of Business, University of Wollongong Australia, Wollongong 2519, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: J. A. Tenreiro Machado
Received: 2 May 2016 / Revised: 18 June 2016 / Accepted: 21 June 2016 / Published: 28 June 2016
(This article belongs to the Section Complexity)
View Full-Text   |   Download PDF [3908 KB, uploaded 29 June 2016]   |  

Abstract

Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM) based correntropy extracted from the Intrinsic Mode Functions (IMFs), which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD) and employ Least Square Support Vector Machine (LSSVM) to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM) as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses. View Full-Text
Keywords: intrinsic mode function correntropy matrix; fast ensemble empirical mode decomposition; AM-correntropy; least squares support vector machine; roller bearing; fault diagnosis intrinsic mode function correntropy matrix; fast ensemble empirical mode decomposition; AM-correntropy; least squares support vector machine; roller bearing; fault diagnosis
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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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Fu, Y.; Jia, L.; Qin, Y.; Yang, J.; Fu, D. Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis. Entropy 2016, 18, 242.

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