A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network
Abstract The safety and reliability of rotating machinery hinge significantly on the proper functioning of rolling bearings. In the last few years, there have been significant advances in the algorithms for intelligent fault diagnosis of bearings. However, the vibration signals collected by machines...
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| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83315-5 |
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| author | Xinyan Zhang Shaobin Cai Wanchen Cai Yuchang Mo Liansuo Wei |
| author_facet | Xinyan Zhang Shaobin Cai Wanchen Cai Yuchang Mo Liansuo Wei |
| author_sort | Xinyan Zhang |
| collection | DOAJ |
| description | Abstract The safety and reliability of rotating machinery hinge significantly on the proper functioning of rolling bearings. In the last few years, there have been significant advances in the algorithms for intelligent fault diagnosis of bearings. However, the vibration signals collected by machines are inevitably affected by irrelevant noise because of the complex working environments of bearings. So, an end-to-end bearing fault diagnosis method: GMSCNN, a bearing fault diagnosis method based on Gram Matrix (GM) and Multi scale Convolutional Neural Network (MSCNN), is proposed in this paper. In this method, first, GM is used to reduce the noise of the collected vibration signals; Secondly, MSCNN is used for feature extraction, and the characteristics of vibration signals at different frequencies and time scales can be captured by the convolutional kernels of different scales; thirdly, two feature enhancement branches are added, utilizing the undenoised vibration signal as input, to enrich and diversify features while enhancing the model’s expressive and generalization capabilities; Finally, the experimental analysis was conducted on two bearing datasets to indicates that the noise robustness of GMSCNN is strong. |
| format | Article |
| id | doaj-art-382db02e2fcc4338a0ab5f461e87d351 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-382db02e2fcc4338a0ab5f461e87d3512025-08-20T02:26:33ZengNature PortfolioScientific Reports2045-23222024-12-0114111710.1038/s41598-024-83315-5A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural networkXinyan Zhang0Shaobin Cai1Wanchen Cai2Yuchang Mo3Liansuo Wei4College of Information Engineering, Huzhou UniversityCollege of Information Engineering, Huzhou UniversityCollege of Management, National Taiwan UniversityCollege of Mathematics, Huaqiao UniversityCollege of Information Engineering, SuQian UniversityAbstract The safety and reliability of rotating machinery hinge significantly on the proper functioning of rolling bearings. In the last few years, there have been significant advances in the algorithms for intelligent fault diagnosis of bearings. However, the vibration signals collected by machines are inevitably affected by irrelevant noise because of the complex working environments of bearings. So, an end-to-end bearing fault diagnosis method: GMSCNN, a bearing fault diagnosis method based on Gram Matrix (GM) and Multi scale Convolutional Neural Network (MSCNN), is proposed in this paper. In this method, first, GM is used to reduce the noise of the collected vibration signals; Secondly, MSCNN is used for feature extraction, and the characteristics of vibration signals at different frequencies and time scales can be captured by the convolutional kernels of different scales; thirdly, two feature enhancement branches are added, utilizing the undenoised vibration signal as input, to enrich and diversify features while enhancing the model’s expressive and generalization capabilities; Finally, the experimental analysis was conducted on two bearing datasets to indicates that the noise robustness of GMSCNN is strong.https://doi.org/10.1038/s41598-024-83315-5Gram matrixMultiscale convolutional neural networkDenoisingRolling bearingFault diagnosis |
| spellingShingle | Xinyan Zhang Shaobin Cai Wanchen Cai Yuchang Mo Liansuo Wei A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network Scientific Reports Gram matrix Multiscale convolutional neural network Denoising Rolling bearing Fault diagnosis |
| title | A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network |
| title_full | A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network |
| title_fullStr | A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network |
| title_full_unstemmed | A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network |
| title_short | A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network |
| title_sort | fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network |
| topic | Gram matrix Multiscale convolutional neural network Denoising Rolling bearing Fault diagnosis |
| url | https://doi.org/10.1038/s41598-024-83315-5 |
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