A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges
Rolling bearing fault diagnosis is an important technology for health monitoring and pre-maintenance of mechanical equipment, which is of great significance for improving equipment operation reliability and reducing maintenance costs. This article reviews the research progress of fault diagnosis met...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-02-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0255451 |
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| _version_ | 1850027012697096192 |
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| author | Jiangdong Zhao Wenming Wang Ji Huang Xiaolu Ma |
| author_facet | Jiangdong Zhao Wenming Wang Ji Huang Xiaolu Ma |
| author_sort | Jiangdong Zhao |
| collection | DOAJ |
| description | Rolling bearing fault diagnosis is an important technology for health monitoring and pre-maintenance of mechanical equipment, which is of great significance for improving equipment operation reliability and reducing maintenance costs. This article reviews the research progress of fault diagnosis methods for rolling bearings, with a focus on analyzing the applications, advantages, and disadvantages of traditional data-driven methods, deep learning methods, graph embedding methods, and Transformer methods in this field. In addition, further analysis was conducted on the main issues of current research, including complex network structures, insufficient information attention, difficulties in graph data processing, and challenges in long-term dependency modeling. In response to these challenges, future research should focus on designing more lightweight and efficient models, improving computational efficiency, robustness of the models, and strengthening attention and deep mining of fault features. |
| format | Article |
| id | doaj-art-41fb42dec8454ab6a8da0740e07e0c9f |
| institution | DOAJ |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-41fb42dec8454ab6a8da0740e07e0c9f2025-08-20T03:00:21ZengAIP Publishing LLCAIP Advances2158-32262025-02-01152020702020702-1710.1063/5.0255451A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challengesJiangdong Zhao0Wenming Wang1Ji Huang2Xiaolu Ma3Experimental Training Teaching Management Department, West Anhui University, Lu’an 237012, People’s Republic of ChinaExperimental Training Teaching Management Department, West Anhui University, Lu’an 237012, People’s Republic of ChinaExperimental Training Teaching Management Department, West Anhui University, Lu’an 237012, People’s Republic of ChinaCollege of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243002, People’s Republic of ChinaRolling bearing fault diagnosis is an important technology for health monitoring and pre-maintenance of mechanical equipment, which is of great significance for improving equipment operation reliability and reducing maintenance costs. This article reviews the research progress of fault diagnosis methods for rolling bearings, with a focus on analyzing the applications, advantages, and disadvantages of traditional data-driven methods, deep learning methods, graph embedding methods, and Transformer methods in this field. In addition, further analysis was conducted on the main issues of current research, including complex network structures, insufficient information attention, difficulties in graph data processing, and challenges in long-term dependency modeling. In response to these challenges, future research should focus on designing more lightweight and efficient models, improving computational efficiency, robustness of the models, and strengthening attention and deep mining of fault features.http://dx.doi.org/10.1063/5.0255451 |
| spellingShingle | Jiangdong Zhao Wenming Wang Ji Huang Xiaolu Ma A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges AIP Advances |
| title | A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges |
| title_full | A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges |
| title_fullStr | A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges |
| title_full_unstemmed | A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges |
| title_short | A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges |
| title_sort | comprehensive review of deep learning based fault diagnosis approaches for rolling bearings advancements and challenges |
| url | http://dx.doi.org/10.1063/5.0255451 |
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