Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum
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State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
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School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
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National Engineering Laboratory for System Safety and Operation Assurance of Urban Rail Transit, Guangzhou 510000, China
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Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China
5
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(1), 50; https://doi.org/10.3390/e21010050
Received: 7 December 2018
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Revised: 2 January 2019
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Accepted: 4 January 2019
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Published: 10 January 2019
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle.
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Keywords:
fault diagnosis; cyclostationary; kernel method; correntropy; impulsive noise
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MDPI and ACS Style
Zhao, X.; Qin, Y.; He, C.; Jia, L.; Kou, L. Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum. Entropy 2019, 21, 50. https://doi.org/10.3390/e21010050
AMA Style
Zhao X, Qin Y, He C, Jia L, Kou L. Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum. Entropy. 2019; 21(1):50. https://doi.org/10.3390/e21010050
Chicago/Turabian StyleZhao, Xuejun, Yong Qin, Changbo He, Limin Jia, and Linlin Kou. 2019. "Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum" Entropy 21, no. 1: 50. https://doi.org/10.3390/e21010050
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