Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBM
It is always a hot and challenging problem to extract the characteristic information of roller bearings from strong noise interference. Conventional Hilbert-Huang Transform (HHT), Local Mean Decomposition (LMD), Local Feature-Scale Decomposition (LCD), and so on have some issues like overenvelope, u...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10318087/ |
| _version_ | 1797454687939592192 |
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| author | Yanfei Liu Junsheng Cheng Yu Yang Guangfu Bin Yiping Shen Yanfeng Peng |
| author_facet | Yanfei Liu Junsheng Cheng Yu Yang Guangfu Bin Yiping Shen Yanfeng Peng |
| author_sort | Yanfei Liu |
| collection | DOAJ |
| description | It is always a hot and challenging problem to extract the characteristic information of roller bearings from strong noise interference. Conventional Hilbert-Huang Transform (HHT), Local Mean Decomposition (LMD), Local Feature-Scale Decomposition (LCD), and so on have some issues like overenvelope, under-envelope, frequency-chaos, end-point effect, and so on. Symplectic Geometry Mode Decomposition (SGMD) is one of the most efficient approaches to reconstruct this model. But SGMD has a drawback that the computation efficiency is reduced quickly with an increase in the quantity of data, and the degradation precision is influenced by the non-valid Symplectic Geometric Component (SGC). On this basis, a Regularized Composite Multiscale Fuzzy Entropy (RCMFE) is proposed, which is used to estimate the complexity of the reconstructed original individual parts and restrict the minimum amount of remaining power. This paper presents a Partial Reconstruction Symplectic Geometry Mode Decomposition (PRSGMD) approach. The simulation results indicate that PRSGMD can not only enhance the precision of SGMD but also enhance its robustness and validity. Finally, a maximal distance evaluation technique (DET) is employed in combination with a more interpretable tree-based Light Gradient Boosting Machine (LightGBM) for the intelligence fault diagnosis for rolling bearings. |
| first_indexed | 2024-03-09T15:41:42Z |
| format | Article |
| id | doaj.art-460e160d2bbb4debb84f89e56d05546d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T15:41:42Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-460e160d2bbb4debb84f89e56d05546d2023-11-25T00:01:20ZengIEEEIEEE Access2169-35362023-01-011112906012907610.1109/ACCESS.2023.333302310318087Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBMYanfei Liu0Junsheng Cheng1https://orcid.org/0009-0009-1587-6324Yu Yang2Guangfu Bin3Yiping Shen4Yanfeng Peng5https://orcid.org/0000-0002-6658-3750State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, ChinaHunan Key Laboratory of Mechanical Equipment Health Maintenance, Hunan University of Science and Technology, Xiangtan, ChinaHunan Key Laboratory of Mechanical Equipment Health Maintenance, Hunan University of Science and Technology, Xiangtan, ChinaHunan Key Laboratory of Mechanical Equipment Health Maintenance, Hunan University of Science and Technology, Xiangtan, ChinaIt is always a hot and challenging problem to extract the characteristic information of roller bearings from strong noise interference. Conventional Hilbert-Huang Transform (HHT), Local Mean Decomposition (LMD), Local Feature-Scale Decomposition (LCD), and so on have some issues like overenvelope, under-envelope, frequency-chaos, end-point effect, and so on. Symplectic Geometry Mode Decomposition (SGMD) is one of the most efficient approaches to reconstruct this model. But SGMD has a drawback that the computation efficiency is reduced quickly with an increase in the quantity of data, and the degradation precision is influenced by the non-valid Symplectic Geometric Component (SGC). On this basis, a Regularized Composite Multiscale Fuzzy Entropy (RCMFE) is proposed, which is used to estimate the complexity of the reconstructed original individual parts and restrict the minimum amount of remaining power. This paper presents a Partial Reconstruction Symplectic Geometry Mode Decomposition (PRSGMD) approach. The simulation results indicate that PRSGMD can not only enhance the precision of SGMD but also enhance its robustness and validity. Finally, a maximal distance evaluation technique (DET) is employed in combination with a more interpretable tree-based Light Gradient Boosting Machine (LightGBM) for the intelligence fault diagnosis for rolling bearings.https://ieeexplore.ieee.org/document/10318087/Fault diagnosispart reconstruction of symplectic geometric pattern decompositionregularized composite multiscale fuzzy entropyrolling bearingssymplectic geometric mode components |
| spellingShingle | Yanfei Liu Junsheng Cheng Yu Yang Guangfu Bin Yiping Shen Yanfeng Peng Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBM IEEE Access Fault diagnosis part reconstruction of symplectic geometric pattern decomposition regularized composite multiscale fuzzy entropy rolling bearings symplectic geometric mode components |
| title | Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBM |
| title_full | Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBM |
| title_fullStr | Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBM |
| title_full_unstemmed | Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBM |
| title_short | Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBM |
| title_sort | roller bearing fault diagnosis based on partial reconstruction symplectic geometry mode decomposition and lightgbm |
| topic | Fault diagnosis part reconstruction of symplectic geometric pattern decomposition regularized composite multiscale fuzzy entropy rolling bearings symplectic geometric mode components |
| url | https://ieeexplore.ieee.org/document/10318087/ |
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