Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox
The selection of variational mode decomposition (VMD) parameters usually adopts the empirical method, trial-and-error method, or single-objective optimization method. The above-mentioned method cannot achieve the global optimal effect. Therefore, a multi-objective particle swarm optimization (MOPSO)...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8681549/ |
| _version_ | 1818603330491908096 |
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| author | Zhijian Wang Gaofeng He Wenhua Du Jie Zhou Xiaofeng Han Jingtai Wang Huihui He Xiaoming Guo Junyuan Wang Yanfei Kou |
| author_facet | Zhijian Wang Gaofeng He Wenhua Du Jie Zhou Xiaofeng Han Jingtai Wang Huihui He Xiaoming Guo Junyuan Wang Yanfei Kou |
| author_sort | Zhijian Wang |
| collection | DOAJ |
| description | The selection of variational mode decomposition (VMD) parameters usually adopts the empirical method, trial-and-error method, or single-objective optimization method. The above-mentioned method cannot achieve the global optimal effect. Therefore, a multi-objective particle swarm optimization (MOPSO) algorithm is proposed to optimize the parameters of VMD, and it is applied to the composite fault diagnosis of the gearbox. The specific steps are: first, symbol dynamic entropy (SDE) can effectively remove background noise, and use state mode probability and state transition to preserve fault information. Power spectral entropy (PSE) reflects the complexity of signal frequency composition. Therefore, the SDE and PSE are selected as fitness functions and then the Pareto frontier optimal solution set is obtained by the MOPSO algorithm. Finally, the optimal combination of VMD parameters (k, a) is obtained by normalization. The improved VMD is used to analyze the simulation signal and gearbox fault signal. The effectiveness of the proposed method is verified by comparing with the ensemble empirical mode decomposition (EEMD). |
| first_indexed | 2024-12-16T13:21:28Z |
| format | Article |
| id | doaj.art-845ae29d818549f9a88d7970da6ec68e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T13:21:28Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-845ae29d818549f9a88d7970da6ec68e2022-12-21T22:30:20ZengIEEEIEEE Access2169-35362019-01-017448714488210.1109/ACCESS.2019.29093008681549Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of GearboxZhijian Wang0https://orcid.org/0000-0002-6794-2065Gaofeng He1Wenhua Du2Jie Zhou3Xiaofeng Han4Jingtai Wang5Huihui He6Xiaoming Guo7Junyuan Wang8Yanfei Kou9College of Mechanical Engineering, North University of China, Taiyuan, ChinaCollege of Mechanical Engineering, North University of China, Taiyuan, ChinaCollege of Mechanical Engineering, North University of China, Taiyuan, ChinaCollege of Mechanical Engineering, North University of China, Taiyuan, ChinaCollege of Mechanical Engineering, North University of China, Taiyuan, ChinaCollege of Mechanical Engineering, North University of China, Taiyuan, ChinaCollege of Mechanical Engineering, North University of China, Taiyuan, ChinaCollege of Mechanical Engineering, North University of China, Taiyuan, ChinaCollege of Mechanical Engineering, North University of China, Taiyuan, ChinaCollege of Mechanical Engineering, North University of China, Taiyuan, ChinaThe selection of variational mode decomposition (VMD) parameters usually adopts the empirical method, trial-and-error method, or single-objective optimization method. The above-mentioned method cannot achieve the global optimal effect. Therefore, a multi-objective particle swarm optimization (MOPSO) algorithm is proposed to optimize the parameters of VMD, and it is applied to the composite fault diagnosis of the gearbox. The specific steps are: first, symbol dynamic entropy (SDE) can effectively remove background noise, and use state mode probability and state transition to preserve fault information. Power spectral entropy (PSE) reflects the complexity of signal frequency composition. Therefore, the SDE and PSE are selected as fitness functions and then the Pareto frontier optimal solution set is obtained by the MOPSO algorithm. Finally, the optimal combination of VMD parameters (k, a) is obtained by normalization. The improved VMD is used to analyze the simulation signal and gearbox fault signal. The effectiveness of the proposed method is verified by comparing with the ensemble empirical mode decomposition (EEMD).https://ieeexplore.ieee.org/document/8681549/Variational mode decompositionmulti-objective particle swarmsymbol dynamic entropypower spectral entropyfault diagnosis of the gearbox |
| spellingShingle | Zhijian Wang Gaofeng He Wenhua Du Jie Zhou Xiaofeng Han Jingtai Wang Huihui He Xiaoming Guo Junyuan Wang Yanfei Kou Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox IEEE Access Variational mode decomposition multi-objective particle swarm symbol dynamic entropy power spectral entropy fault diagnosis of the gearbox |
| title | Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox |
| title_full | Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox |
| title_fullStr | Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox |
| title_full_unstemmed | Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox |
| title_short | Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox |
| title_sort | application of parameter optimized variational mode decomposition method in fault diagnosis of gearbox |
| topic | Variational mode decomposition multi-objective particle swarm symbol dynamic entropy power spectral entropy fault diagnosis of the gearbox |
| url | https://ieeexplore.ieee.org/document/8681549/ |
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