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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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dc.citedby83
dc.contributor.authorHakim M.en_US
dc.contributor.authorOmran A.A.B.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAl-Waily M.en_US
dc.contributor.authorAbdellatif A.en_US
dc.contributor.authorid58938943800en_US
dc.contributor.authorid55212152300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55385828500en_US
dc.contributor.authorid57304215000en_US
dc.date.accessioned2024-10-14T03:19:02Z
dc.date.available2024-10-14T03:19:02Z
dc.date.issued2023
dc.description.abstractRolling 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 AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo101945
dc.identifier.doi10.1016/j.asej.2022.101945
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85138043870
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85138043870&doi=10.1016%2fj.asej.2022.101945&partnerID=40&md5=ddf39da82d3f43639ca2560823eee3df
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34322
dc.identifier.volume14
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.subjectDeep learning
dc.subjectFault diagnosis
dc.subjectRolling bearing
dc.subjectSystematic review
dc.subjectTransfer learning
dc.subjectConvolutional neural networks
dc.subjectFault detection
dc.subjectProduction efficiency
dc.subjectRecurrent neural networks
dc.subjectRoller bearings
dc.subjectAccident rate
dc.subjectBearing fault
dc.subjectBearing fault detection
dc.subjectBearing fault diagnosis
dc.subjectDeep learning
dc.subjectFaults diagnosis
dc.subjectProduction efficiency
dc.subjectRolling bearings
dc.subjectSystematic Review
dc.subjectTransfer learning
dc.subjectFailure analysis
dc.titleA systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendationsen_US
dc.typeReviewen_US
dspace.entity.typePublication
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