Lightweight Convolutional Network for Bearing Fault Diagnosis
In the field of bearing fault diagnosis, many convolutional models with excellent performance face challenges in industrial applications due to deployment cost constraints. This paper aims to develop a lightweight diagnostic method with reduced parameters. We investigate the feasibility of using de...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2024-08-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2352 |
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| author | LIU Hui LI Yang HOU Yimin |
| author_facet | LIU Hui LI Yang HOU Yimin |
| author_sort | LIU Hui |
| collection | DOAJ |
| description | In the field of bearing fault diagnosis, many convolutional models with excellent performance face challenges in industrial applications due to deployment cost constraints. This paper aims to develop a lightweight diagnostic method with reduced parameters. We investigate the feasibility of using depthwise separable convolution to construct a lightweight bearing fault diagnosis model, thereby proposing a strategy to compress the parameters of the convolutional network while ensuring diagnostic accuracy. The effectiveness of the proposed method is validated on both publicly available and custom vibration signal datasets. The results demonstrate that compressing convolutional models using depthwise separable convolution allows for lightweight requirements while maintaining a high diagnostic accuracy (96. 20 ± 2. 81% ). |
| format | Article |
| id | doaj-art-e2c235d8a5fe4eabb787f199717b3952 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-e2c235d8a5fe4eabb787f199717b39522025-08-20T03:28:02ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832024-08-012904808810.15938/j.jhust.2024.04.009Lightweight Convolutional Network for Bearing Fault DiagnosisLIU Hui0LI Yang1HOU Yimin2China Nuclear Industry Maintenance Co. , Ltd, Shanghai 201103 , ChinaSchool of Mechanical Engineering, Northeast Electric Power University, Jilin 132012 , ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin 132012 , ChinaIn the field of bearing fault diagnosis, many convolutional models with excellent performance face challenges in industrial applications due to deployment cost constraints. This paper aims to develop a lightweight diagnostic method with reduced parameters. We investigate the feasibility of using depthwise separable convolution to construct a lightweight bearing fault diagnosis model, thereby proposing a strategy to compress the parameters of the convolutional network while ensuring diagnostic accuracy. The effectiveness of the proposed method is validated on both publicly available and custom vibration signal datasets. The results demonstrate that compressing convolutional models using depthwise separable convolution allows for lightweight requirements while maintaining a high diagnostic accuracy (96. 20 ± 2. 81% ).https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2352fault detectionconvolutional neural networksmodel structuresvibrations (mechanical) |
| spellingShingle | LIU Hui LI Yang HOU Yimin Lightweight Convolutional Network for Bearing Fault Diagnosis Journal of Harbin University of Science and Technology fault detection convolutional neural networks model structures vibrations (mechanical) |
| title | Lightweight Convolutional Network for Bearing Fault Diagnosis |
| title_full | Lightweight Convolutional Network for Bearing Fault Diagnosis |
| title_fullStr | Lightweight Convolutional Network for Bearing Fault Diagnosis |
| title_full_unstemmed | Lightweight Convolutional Network for Bearing Fault Diagnosis |
| title_short | Lightweight Convolutional Network for Bearing Fault Diagnosis |
| title_sort | lightweight convolutional network for bearing fault diagnosis |
| topic | fault detection convolutional neural networks model structures vibrations (mechanical) |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2352 |
| work_keys_str_mv | AT liuhui lightweightconvolutionalnetworkforbearingfaultdiagnosis AT liyang lightweightconvolutionalnetworkforbearingfaultdiagnosis AT houyimin lightweightconvolutionalnetworkforbearingfaultdiagnosis |