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

Yuanyuan Yang, Md Muhie Menul Haque, Dongling Bai and Wei Tang
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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
Dongling Bai: School of Economics and Management, Chang’an University, Xi’an 710064, China
Wei Tang: School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China

Energies, 2021, vol. 14, issue 21, 1-26

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.

Keywords: electric motors; fault diagnosis; deep learning; deep belief network; autoencoders; convolutional neural networks; recurrent neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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