Rolling bearing is widely used in rotating mechanical system, and its operating state has great effects on availability, reliability and the life cycle of whole mechanical system. Therefore, fault diagnosis of rolling bearing is indispensable for the health monitoring in rotating machinery system. In this paper, a method based on multi-scale entropy (MSE) and ensembled artificial neural network (EANN) is proposed for feature extraction and fault recognition in rolling bearings respectively. MSE is mainly in charge for quantizing the complexity of the nonlinear time series in different scales. Then, EANN is employed to identify various faults of rolling bearing after overcoming the two disadvantages like local minimization and slow convergence speed in back propagation neural network (BPNN). The experimental results indicate that the method based on MSE and EANN is feasible and effective to classify different categories of faults and to identify the severity level of fault in the rolling bearings. Therefore, it is available for fault detection and diagnosis in rolling bearings with good performance.
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ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference
June 9–13, 2014
Detroit, Michigan, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-4581-3
PROCEEDINGS PAPER
Fault Diagnosis of Rolling Bearings Based on Multi-Scale Entropy and Ensembled Artificial Neural Network Available to Purchase
Fen Chen,
Fen Chen
Wuhan University of Technology, Wuhan, Hubei, China
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Quan Liu,
Quan Liu
Wuhan University of Technology, Wuhan, Hubei, China
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Qin Wei,
Qin Wei
Wuhan University of Technology, Wuhan, Hubei, China
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Deng Ting,
Deng Ting
Wuhan University of Technology, Wuhan, Hubei, China
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Yan Ting,
Yan Ting
Wuhan University of Technology, Wuhan, Hubei, China
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Su Wenqin,
Su Wenqin
Wuhan University of Technology, Wuhan, Hubei, China
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Peng Bingjie,
Peng Bingjie
Wuhan University of Technology, Wuhan, Hubei, China
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Lei Zhao
Lei Zhao
Wuhan University of Technology, Wuhan, Hubei, China
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Fen Chen
Wuhan University of Technology, Wuhan, Hubei, China
Quan Liu
Wuhan University of Technology, Wuhan, Hubei, China
Qin Wei
Wuhan University of Technology, Wuhan, Hubei, China
Deng Ting
Wuhan University of Technology, Wuhan, Hubei, China
Yan Ting
Wuhan University of Technology, Wuhan, Hubei, China
Su Wenqin
Wuhan University of Technology, Wuhan, Hubei, China
Peng Bingjie
Wuhan University of Technology, Wuhan, Hubei, China
Lei Zhao
Wuhan University of Technology, Wuhan, Hubei, China
Paper No:
MSEC2014-4033, V002T02A041; 9 pages
Published Online:
October 3, 2014
Citation
Chen, F, Liu, Q, Wei, Q, Ting, D, Ting, Y, Wenqin, S, Bingjie, P, & Zhao, L. "Fault Diagnosis of Rolling Bearings Based on Multi-Scale Entropy and Ensembled Artificial Neural Network." Proceedings of the ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. Volume 2: Processing. Detroit, Michigan, USA. June 9–13, 2014. V002T02A041. ASME. https://doi.org/10.1115/MSEC2014-4033
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