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

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, interestin...

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Autores principales:	Yuanyuan Yang, Md Muhie Menul Haque, Dongling Bai, Wei Tang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electric motors fault diagnosis deep learning deep belief network autoencoders convolutional neural networks Technology T
Acceso en línea:	https://doaj.org/article/0e5aae5a74ea4ff79792e1b994efad83
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spelling	oai:doaj.org-article:0e5aae5a74ea4ff79792e1b994efad832021-11-11T15:50:09ZFault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review10.3390/en142170171996-1073https://doaj.org/article/0e5aae5a74ea4ff79792e1b994efad832021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7017https://doaj.org/toc/1996-1073Electric 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.Yuanyuan YangMd Muhie Menul HaqueDongling BaiWei TangMDPI AGarticleelectric motorsfault diagnosisdeep learningdeep belief networkautoencodersconvolutional neural networksTechnologyTENEnergies, Vol 14, Iss 7017, p 7017 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electric motors fault diagnosis deep learning deep belief network autoencoders convolutional neural networks Technology T
spellingShingle	electric motors fault diagnosis deep learning deep belief network autoencoders convolutional neural networks Technology T Yuanyuan Yang Md Muhie Menul Haque Dongling Bai Wei Tang Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review
description	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.
format	article
author	Yuanyuan Yang Md Muhie Menul Haque Dongling Bai Wei Tang
author_facet	Yuanyuan Yang Md Muhie Menul Haque Dongling Bai Wei Tang
author_sort	Yuanyuan Yang
title	Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review
title_short	Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review
title_full	Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review
title_fullStr	Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review
title_full_unstemmed	Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review
title_sort	fault diagnosis of electric motors using deep learning algorithms and its application: a review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0e5aae5a74ea4ff79792e1b994efad83
work_keys_str_mv	AT yuanyuanyang faultdiagnosisofelectricmotorsusingdeeplearningalgorithmsanditsapplicationareview AT mdmuhiemenulhaque faultdiagnosisofelectricmotorsusingdeeplearningalgorithmsanditsapplicationareview AT donglingbai faultdiagnosisofelectricmotorsusingdeeplearningalgorithmsanditsapplicationareview AT weitang faultdiagnosisofelectricmotorsusingdeeplearningalgorithmsanditsapplicationareview
_version_	1718433554336055296

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

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