A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations

Rolling bearing fault detection is critical for improving production efficiency and lowering accident rates in complicated mechanical systems, as well as huge monitoring data, posing significant challenges to present fault diagnostic technology. Deep Learning is now an extraordinarily popular resear...

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Main Authors:	Hakim M., Omran A.A.B., Ahmed A.N., Al-Waily M., Abdellatif A.
Other Authors:	58938943800
Format:	Review
Published:	Ain Shams University 2024
Subjects:	Deep learning Fault diagnosis Rolling bearing Systematic review Transfer learning Convolutional neural networks Fault detection Production efficiency Recurrent neural networks Roller bearings Accident rate Bearing fault Bearing fault detection Bearing fault diagnosis Faults diagnosis Rolling bearings Systematic Review Failure analysis
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Institution:	Universiti Tenaga Nasional

id	my.uniten.dspace-34322
record_format	dspace
spelling	my.uniten.dspace-343222024-10-14T11:19:02Z A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations Hakim M. Omran A.A.B. Ahmed A.N. Al-Waily M. Abdellatif A. 58938943800 55212152300 57214837520 55385828500 57304215000 Deep learning Fault diagnosis Rolling bearing Systematic review Transfer learning Convolutional neural networks Fault detection Production efficiency Recurrent neural networks Roller bearings Accident rate Bearing fault Bearing fault detection Bearing fault diagnosis Deep learning Faults diagnosis Production efficiency Rolling bearings Systematic Review Transfer learning Failure analysis Rolling bearing fault detection is critical for improving production efficiency and lowering accident rates in complicated mechanical systems, as well as huge monitoring data, posing significant challenges to present fault diagnostic technology. Deep Learning is now an extraordinarily popular research topic in the field and a promising approach for detecting intelligent bearing faults. This paper aims to give a comprehensive overview of Deep Learning (DL) based on bearing fault diagnosis. The most widely used DL algorithms for detecting bearing faults include Convolutional Neural Network, Recurrent neural network, Autoencoder, and Generative Adversarial Network. It discusses a variety of transfer learning architectures and relevant theories while summarises, classifies, and explains several publications on the subject. The research area's applications and problems are also addressed. � 2022 THE AUTHORS Final 2024-10-14T03:19:02Z 2024-10-14T03:19:02Z 2023 Review 10.1016/j.asej.2022.101945 2-s2.0-85138043870 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138043870&doi=10.1016%2fj.asej.2022.101945&partnerID=40&md5=ddf39da82d3f43639ca2560823eee3df https://irepository.uniten.edu.my/handle/123456789/34322 14 4 101945 All Open Access Gold Open Access Ain Shams University Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Deep learning Fault diagnosis Rolling bearing Systematic review Transfer learning Convolutional neural networks Fault detection Production efficiency Recurrent neural networks Roller bearings Accident rate Bearing fault Bearing fault detection Bearing fault diagnosis Deep learning Faults diagnosis Production efficiency Rolling bearings Systematic Review Transfer learning Failure analysis
spellingShingle	Deep learning Fault diagnosis Rolling bearing Systematic review Transfer learning Convolutional neural networks Fault detection Production efficiency Recurrent neural networks Roller bearings Accident rate Bearing fault Bearing fault detection Bearing fault diagnosis Deep learning Faults diagnosis Production efficiency Rolling bearings Systematic Review Transfer learning Failure analysis Hakim M. Omran A.A.B. Ahmed A.N. Al-Waily M. Abdellatif A. A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
description	Rolling bearing fault detection is critical for improving production efficiency and lowering accident rates in complicated mechanical systems, as well as huge monitoring data, posing significant challenges to present fault diagnostic technology. Deep Learning is now an extraordinarily popular research topic in the field and a promising approach for detecting intelligent bearing faults. This paper aims to give a comprehensive overview of Deep Learning (DL) based on bearing fault diagnosis. The most widely used DL algorithms for detecting bearing faults include Convolutional Neural Network, Recurrent neural network, Autoencoder, and Generative Adversarial Network. It discusses a variety of transfer learning architectures and relevant theories while summarises, classifies, and explains several publications on the subject. The research area's applications and problems are also addressed. � 2022 THE AUTHORS
author2	58938943800
author_facet	58938943800 Hakim M. Omran A.A.B. Ahmed A.N. Al-Waily M. Abdellatif A.
format	Review
author	Hakim M. Omran A.A.B. Ahmed A.N. Al-Waily M. Abdellatif A.
author_sort	Hakim M.
title	A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
title_short	A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
title_full	A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
title_fullStr	A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
title_full_unstemmed	A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
title_sort	systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: taxonomy, overview, application, open challenges, weaknesses and recommendations
publisher	Ain Shams University
publishDate	2024
_version_	1814061175278665728

A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations

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