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6 February 2022 Research on simulation of motor bearing fault diagnosis based on auto-encoder
Fu-lin Chi, Xin-yu Yang
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Proceedings Volume 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021); 1208105 (2022) https://doi.org/10.1117/12.2623848
Event: Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 2021, Chongqing, China
Abstract
With the rise of artificial intelligence algorithm, it is possible to realize rapid intelligent diagnosis of bearing fault. In this paper, combined with simulation, the application of Autoencoder in motor bearing fault diagnosis is studied. In this paper, the validity of bearing fault simulation using time - frequency analysis method is verified. The data representation and clustering capability of the Autoencoder are fully verified by simulation signals and exposed data sets. Compared with BP network, the Autoencoder network has lower training costs and more stable diagnosis results, which can realize motor bearing fault diagnosis more effectively.
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Fu-lin Chi and Xin-yu Yang "Research on simulation of motor bearing fault diagnosis based on auto-encoder", Proc. SPIE 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 1208105 (6 February 2022); https://doi.org/10.1117/12.2623848
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KEYWORDS
Feature extraction
Data modeling
Signal processing
Data processing
Diagnostics
Interference (communication)
Neural networks
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