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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Ain Shams University
2024
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| author | Hakim M. Omran A.A.B. Ahmed A.N. Al-Waily M. Abdellatif A. |
| author2 | 58938943800 |
| author_facet | 58938943800 Hakim M. Omran A.A.B. Ahmed A.N. Al-Waily M. Abdellatif A. |
| author_sort | Hakim M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| format | Review |
| id | my.uniten.dspace-34322 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2024 |
| publisher | Ain Shams University |
| 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 |
| 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 |
| 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_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_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_sort | systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: taxonomy, overview, application, open challenges, weaknesses and recommendations |
| 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 |
| url_provider | http://dspace.uniten.edu.my/ |