Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm
For mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source separation. Shift invariant K-means singular v...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/9/8/144 |
| _version_ | 1827684932503207936 |
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| author | Haodong Yuan Nailong Wu Xinyuan Chen |
| author_facet | Haodong Yuan Nailong Wu Xinyuan Chen |
| author_sort | Haodong Yuan |
| collection | DOAJ |
| description | For mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source separation. Shift invariant K-means singular value decomposition (shift invariant K-SVD) dictionary learning is suitable to extract the periodic and repeated fault features of a rotating machinery fault, hence in this article a single-channel compound fault analysis method is put forward which combines shift invariant K-SVD with improved fast independent component analysis (improved FastICA) algorithm. Firstly, based on single-channel compound fault signal, the shift invariant K-SVD algorithm can be used for learning multiple latent components that can be constructed as a virtual multi-channel signal. Then the improved FastICA algorithm is utilized to realize the separation of multiple fault source signals. With regard to the FastICA algorithm, the third-order convergence Newton iteration method is adopted to improve convergence speed. Moreover, in order to address the problem that FastICA is very sensitive to initialization, a steepest descent method can be applied. The experimental analysis of the compound fault of rolling bearing verifies that the presented method is effective to separate multiple fault source signals and the improved FastICA algorithm can increase convergence rate and overcome the problem of sensitivity to initialization. |
| first_indexed | 2024-03-10T08:40:00Z |
| format | Article |
| id | doaj.art-61c9f1ad8a4a4305bf4d706e19120b7f |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T08:40:00Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-61c9f1ad8a4a4305bf4d706e19120b7f2023-11-22T08:24:20ZengMDPI AGMachines2075-17022021-07-019814410.3390/machines9080144Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA AlgorithmHaodong Yuan0Nailong Wu1Xinyuan Chen2College of Information Science and Technology, Donghua University, Shanghai 201620, ChinaCollege of Information Science and Technology, Donghua University, Shanghai 201620, ChinaCollege of Information Science and Technology, Donghua University, Shanghai 201620, ChinaFor mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source separation. Shift invariant K-means singular value decomposition (shift invariant K-SVD) dictionary learning is suitable to extract the periodic and repeated fault features of a rotating machinery fault, hence in this article a single-channel compound fault analysis method is put forward which combines shift invariant K-SVD with improved fast independent component analysis (improved FastICA) algorithm. Firstly, based on single-channel compound fault signal, the shift invariant K-SVD algorithm can be used for learning multiple latent components that can be constructed as a virtual multi-channel signal. Then the improved FastICA algorithm is utilized to realize the separation of multiple fault source signals. With regard to the FastICA algorithm, the third-order convergence Newton iteration method is adopted to improve convergence speed. Moreover, in order to address the problem that FastICA is very sensitive to initialization, a steepest descent method can be applied. The experimental analysis of the compound fault of rolling bearing verifies that the presented method is effective to separate multiple fault source signals and the improved FastICA algorithm can increase convergence rate and overcome the problem of sensitivity to initialization.https://www.mdpi.com/2075-1702/9/8/144shift invariant K-SVDimproved FastICAsingle-channel blind source separationcompound fault analysisrolling bearing |
| spellingShingle | Haodong Yuan Nailong Wu Xinyuan Chen Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm Machines shift invariant K-SVD improved FastICA single-channel blind source separation compound fault analysis rolling bearing |
| title | Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm |
| title_full | Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm |
| title_fullStr | Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm |
| title_full_unstemmed | Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm |
| title_short | Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm |
| title_sort | mechanical compound fault analysis method based on shift invariant dictionary learning and improved fastica algorithm |
| topic | shift invariant K-SVD improved FastICA single-channel blind source separation compound fault analysis rolling bearing |
| url | https://www.mdpi.com/2075-1702/9/8/144 |
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