A Sparsity-Assisted Fault Diagnosis Method Based on Nonconvex Sparse Regularization
Sparse representation theory can be adopted for fault feature extraction and classification. Inspired by these two capabilities of sparse representation theory, this paper proposes a novel collaborative sparsity-assisted fault diagnosis (CSFD) method. Specifically, due to the repeatability and spars...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9404164/ |
| _version_ | 1828890495589285888 |
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| author | Yijie Niu Jiyou Fei |
| author_facet | Yijie Niu Jiyou Fei |
| author_sort | Yijie Niu |
| collection | DOAJ |
| description | Sparse representation theory can be adopted for fault feature extraction and classification. Inspired by these two capabilities of sparse representation theory, this paper proposes a novel collaborative sparsity-assisted fault diagnosis (CSFD) method. Specifically, due to the repeatability and sparsity of fault feature signal in the whole signal, the feature extraction capability of sparse representation is utilized to extract fault features and construct a feature matrix. Subsequently, owing to the sparsity of fault classification problem itself, the classification capability of sparse representation is employed to achieve fault classification. Moreover, fault feature extraction is a key issue for fault diagnosis. Therefore, in order to improve the effectiveness of fault feature extraction, a RC-adjustment strategy with the ability to adaptively adjust regularization parameter is designed to assist the generalized minimax-concave (GMC) penalty for feature extraction. The advantage of the GMC penalty is that it can establish a nonconvex sparse regularization model with convex optimization solution. Finally, the proposed CSFD method is tested and verified by Case Western Reserve University (CWRU) bearing dataset and actual engineering dataset. Especially, the diagnostic accuracy of this method can reach 99.92%, which is nearly 7.68% higher than the traditional method based on L1 norm penalty. Enormous experiment results have thoroughly demonstrated the effectiveness of the proposed CSFD method for fault diagnosis. |
| first_indexed | 2024-12-13T13:00:02Z |
| format | Article |
| id | doaj.art-fbcabc1ebcb84a03ad1e1145beaa385a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:00:02Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fbcabc1ebcb84a03ad1e1145beaa385a2022-12-21T23:45:05ZengIEEEIEEE Access2169-35362021-01-019590275903710.1109/ACCESS.2021.30730729404164A Sparsity-Assisted Fault Diagnosis Method Based on Nonconvex Sparse RegularizationYijie Niu0https://orcid.org/0000-0003-2324-5804Jiyou Fei1College of Software Engineering, Dalian Jiaotong University, Dalian, ChinaCollege of Locomotive and Rolling, Dalian Jiaotong University, Dalian, ChinaSparse representation theory can be adopted for fault feature extraction and classification. Inspired by these two capabilities of sparse representation theory, this paper proposes a novel collaborative sparsity-assisted fault diagnosis (CSFD) method. Specifically, due to the repeatability and sparsity of fault feature signal in the whole signal, the feature extraction capability of sparse representation is utilized to extract fault features and construct a feature matrix. Subsequently, owing to the sparsity of fault classification problem itself, the classification capability of sparse representation is employed to achieve fault classification. Moreover, fault feature extraction is a key issue for fault diagnosis. Therefore, in order to improve the effectiveness of fault feature extraction, a RC-adjustment strategy with the ability to adaptively adjust regularization parameter is designed to assist the generalized minimax-concave (GMC) penalty for feature extraction. The advantage of the GMC penalty is that it can establish a nonconvex sparse regularization model with convex optimization solution. Finally, the proposed CSFD method is tested and verified by Case Western Reserve University (CWRU) bearing dataset and actual engineering dataset. Especially, the diagnostic accuracy of this method can reach 99.92%, which is nearly 7.68% higher than the traditional method based on L1 norm penalty. Enormous experiment results have thoroughly demonstrated the effectiveness of the proposed CSFD method for fault diagnosis.https://ieeexplore.ieee.org/document/9404164/Fault diagnosissparse representationgeneralized minimax-concave (GMC) penaltyfault feature extraction |
| spellingShingle | Yijie Niu Jiyou Fei A Sparsity-Assisted Fault Diagnosis Method Based on Nonconvex Sparse Regularization IEEE Access Fault diagnosis sparse representation generalized minimax-concave (GMC) penalty fault feature extraction |
| title | A Sparsity-Assisted Fault Diagnosis Method Based on Nonconvex Sparse Regularization |
| title_full | A Sparsity-Assisted Fault Diagnosis Method Based on Nonconvex Sparse Regularization |
| title_fullStr | A Sparsity-Assisted Fault Diagnosis Method Based on Nonconvex Sparse Regularization |
| title_full_unstemmed | A Sparsity-Assisted Fault Diagnosis Method Based on Nonconvex Sparse Regularization |
| title_short | A Sparsity-Assisted Fault Diagnosis Method Based on Nonconvex Sparse Regularization |
| title_sort | sparsity assisted fault diagnosis method based on nonconvex sparse regularization |
| topic | Fault diagnosis sparse representation generalized minimax-concave (GMC) penalty fault feature extraction |
| url | https://ieeexplore.ieee.org/document/9404164/ |
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