Research on Bearing Fault Diagnosis Based on GMNR and ResNet-CABA-MAGRU
Focusing on the problem that it is difficult to maintain a high diagnostic accuracy rate, short running time, and robust generalization capability in the face of a strong-noise environment in rolling bearing fault diagnosis, a bearing fault diagnosis model (GMNR-CABA-MAGRU) founded upon a new attent...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3458 |
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| author | Longfa Chen Na Meng Wenzheng Sun Sen Yang Shuo Tian Yuguo Li |
| author_facet | Longfa Chen Na Meng Wenzheng Sun Sen Yang Shuo Tian Yuguo Li |
| author_sort | Longfa Chen |
| collection | DOAJ |
| description | Focusing on the problem that it is difficult to maintain a high diagnostic accuracy rate, short running time, and robust generalization capability in the face of a strong-noise environment in rolling bearing fault diagnosis, a bearing fault diagnosis model (GMNR-CABA-MAGRU) founded upon a new attention-mechanism-improved residual network (ResNet-CABA) and a Gram denoising module (GMNR) is proposed, and the CWRU bearing dataset is used for verification. Under the 0-load condition in a noise-free environment, the diagnostic accuracy of this model reached 99.66%, and the running time was only 52.74 s. Then, a bearing dataset with added Gaussian noise from −4 db to 4 db was verified, and this model was still able to maintain a diagnostic accuracy of 90.32% under the strong-noise environment of −4 db SNR. And migration experiments were carried out under different load conditions, and this model was also able to maintain a very high accuracy rate. Moreover, in all the above experiments, this model performed better than various comparative models. The developed framework demonstrated superior diagnostic precision, enhanced robustness, and improved generalization capability. |
| format | Article |
| id | doaj-art-c13611f08bf14e888fbc91b3f3fd6b62 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c13611f08bf14e888fbc91b3f3fd6b622025-08-20T03:11:32ZengMDPI AGSensors1424-82202025-05-012511345810.3390/s25113458Research on Bearing Fault Diagnosis Based on GMNR and ResNet-CABA-MAGRULongfa Chen0Na Meng1Wenzheng Sun2Sen Yang3Shuo Tian4Yuguo Li5School of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaFocusing on the problem that it is difficult to maintain a high diagnostic accuracy rate, short running time, and robust generalization capability in the face of a strong-noise environment in rolling bearing fault diagnosis, a bearing fault diagnosis model (GMNR-CABA-MAGRU) founded upon a new attention-mechanism-improved residual network (ResNet-CABA) and a Gram denoising module (GMNR) is proposed, and the CWRU bearing dataset is used for verification. Under the 0-load condition in a noise-free environment, the diagnostic accuracy of this model reached 99.66%, and the running time was only 52.74 s. Then, a bearing dataset with added Gaussian noise from −4 db to 4 db was verified, and this model was still able to maintain a diagnostic accuracy of 90.32% under the strong-noise environment of −4 db SNR. And migration experiments were carried out under different load conditions, and this model was also able to maintain a very high accuracy rate. Moreover, in all the above experiments, this model performed better than various comparative models. The developed framework demonstrated superior diagnostic precision, enhanced robustness, and improved generalization capability.https://www.mdpi.com/1424-8220/25/11/3458deep learningfault diagnosisattention mechanismGram denoising module (GMNR) |
| spellingShingle | Longfa Chen Na Meng Wenzheng Sun Sen Yang Shuo Tian Yuguo Li Research on Bearing Fault Diagnosis Based on GMNR and ResNet-CABA-MAGRU Sensors deep learning fault diagnosis attention mechanism Gram denoising module (GMNR) |
| title | Research on Bearing Fault Diagnosis Based on GMNR and ResNet-CABA-MAGRU |
| title_full | Research on Bearing Fault Diagnosis Based on GMNR and ResNet-CABA-MAGRU |
| title_fullStr | Research on Bearing Fault Diagnosis Based on GMNR and ResNet-CABA-MAGRU |
| title_full_unstemmed | Research on Bearing Fault Diagnosis Based on GMNR and ResNet-CABA-MAGRU |
| title_short | Research on Bearing Fault Diagnosis Based on GMNR and ResNet-CABA-MAGRU |
| title_sort | research on bearing fault diagnosis based on gmnr and resnet caba magru |
| topic | deep learning fault diagnosis attention mechanism Gram denoising module (GMNR) |
| url | https://www.mdpi.com/1424-8220/25/11/3458 |
| work_keys_str_mv | AT longfachen researchonbearingfaultdiagnosisbasedongmnrandresnetcabamagru AT nameng researchonbearingfaultdiagnosisbasedongmnrandresnetcabamagru AT wenzhengsun researchonbearingfaultdiagnosisbasedongmnrandresnetcabamagru AT senyang researchonbearingfaultdiagnosisbasedongmnrandresnetcabamagru AT shuotian researchonbearingfaultdiagnosisbasedongmnrandresnetcabamagru AT yuguoli researchonbearingfaultdiagnosisbasedongmnrandresnetcabamagru |