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.
Full text not available from this repository.Abstract
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/).
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Rolling bearing; Deep learning; Transfer learning; Fault diagnosis; Systematic review |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 24 Nov 2024 04:34 |
| Last Modified: | 24 Nov 2024 04:34 |
| URI: | http://eprints.um.edu.my/id/eprint/39164 |
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