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Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review

Author

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  • Yuanyuan Yang

    (Department of Science and Technology, Zhengzhou Railway Vocational and Technical College, Zhengzhou 451460, China)

  • Md Muhie Menul Haque

    (School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
    Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh)

  • Dongling Bai

    (School of Economics and Management, Chang’an University, Xi’an 710064, China
    School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450015, China)

  • Wei Tang

    (School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China)

Abstract

Electric motors are used extensively in numerous industries, and their failure can result not only in machine damage but also a slew of other issues, such as financial loss, injuries, etc. As a result, there is a significant scope to use robust fault diagnosis technology. In recent years, interesting research results on fault diagnosis for electric motors have been documented. Deep learning in the fault detection of electric equipment has shown comparatively better results than traditional approaches because of its more powerful and sophisticated feature extraction capabilities. This paper covers four traditional types of deep learning models: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN), and highlights their use in detecting faults of electric motors. Finally, the issues and obstacles that deep learning encounters in the fault detection mechanism as well as the prospects are discussed and summarized.

Suggested Citation

  • Yuanyuan Yang & Md Muhie Menul Haque & Dongling Bai & Wei Tang, 2021. "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review," Energies, MDPI, vol. 14(21), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7017-:d:665418
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    References listed on IDEAS

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    1. Dengyu Xiao & Chengjin Qin & Honggan Yu & Yixiang Huang & Chengliang Liu, 2021. "Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 377-391, February.
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    5. Peng Guo & Jian Fu & XiYun Yang, 2018. "Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model," Energies, MDPI, vol. 11(9), pages 1-16, August.
    6. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
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    Cited by:

    1. Attallah, Omneya & Ibrahim, Rania A. & Zakzouk, Nahla E., 2023. "CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection," Renewable Energy, Elsevier, vol. 203(C), pages 870-880.

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