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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

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, Mohammed, Omran, Abdoulhdi A. Borhana, Ahmed, Ali Najah, Al-Waily, Muhannad, Abdellatif, Abdallah
Format: Article
Published: Elsevier 2023
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.um.edu.my/39164/
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spelling my.um.eprints.391642024-11-24T04:34:10Z http://eprints.um.edu.my/39164/ A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations Hakim, Mohammed Omran, Abdoulhdi A. Borhana Ahmed, Ali Najah Al-Waily, Muhannad Abdellatif, Abdallah TJ Mechanical engineering and machinery 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.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/). Elsevier 2023-04 Article PeerReviewed Hakim, Mohammed and Omran, Abdoulhdi A. Borhana and Ahmed, Ali Najah and Al-Waily, Muhannad and Abdellatif, Abdallah (2023) A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations. Ain Shams Engineering Journal, 14 (4). ISSN 20904479, DOI https://doi.org/10.1016/j.asej.2022.101945 <https://doi.org/10.1016/j.asej.2022.101945>. 10.1016/j.asej.2022.101945
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Hakim, Mohammed
Omran, Abdoulhdi A. Borhana
Ahmed, Ali Najah
Al-Waily, Muhannad
Abdellatif, Abdallah
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.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).
format Article
author Hakim, Mohammed
Omran, Abdoulhdi A. Borhana
Ahmed, Ali Najah
Al-Waily, Muhannad
Abdellatif, Abdallah
author_facet Hakim, Mohammed
Omran, Abdoulhdi A. Borhana
Ahmed, Ali Najah
Al-Waily, Muhannad
Abdellatif, Abdallah
author_sort Hakim, Mohammed
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 Elsevier
publishDate 2023
url http://eprints.um.edu.my/39164/
_version_ 1817841942903390208
score 13.226497

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