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Materials 2017, 10(6), 571; doi:10.3390/ma10060571

Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission

1
Shanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics, Xi’an Jiaotong University, Xi’an 710049, China
2
State Key Laboratory of Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Victor Giurgiutiu
Received: 15 April 2017 / Revised: 12 May 2017 / Accepted: 17 May 2017 / Published: 24 May 2017
(This article belongs to the Special Issue Structural Health Monitoring for Aerospace Applications 2017)
View Full-Text   |   Download PDF [2340 KB, uploaded 24 May 2017]   |  

Abstract

Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise. View Full-Text
Keywords: acoustic emission; correlated kurtosis; Empirical Wavelet Transform; bearing fault detection acoustic emission; correlated kurtosis; Empirical Wavelet Transform; bearing fault detection
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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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Gao, Z.; Lin, J.; Wang, X.; Xu, X. Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission. Materials 2017, 10, 571.

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