There Search of LS-SVM Based on LMD Morphology Filter
In the diagnosis of bearing, the LS-SVM method research with LMD morphological filtering was put out in order to solve the problem about the kernel function parameter selection and the bad sparsity of least squares vector machine (LS-SVM).First, the LMD was used to decompose the measured signal and...
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Harbin University of Science and Technology Publications
2022-02-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2060 |
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| author | MENG Liang XU Tong-le MA Jin-ying CAI Dao-yong |
| author_facet | MENG Liang XU Tong-le MA Jin-ying CAI Dao-yong |
| author_sort | MENG Liang |
| collection | DOAJ |
| description | In the diagnosis of bearing, the LS-SVM method research with LMD morphological filtering was put out in order to solve the problem about the kernel function parameter selection and the bad sparsity of least squares vector machine (LS-SVM).First, the LMD was used to decompose the measured signal and PF components were obtained.The correlation analysis was carried out to remove the false components, and the noise of PF components was reduced by morphological filtering.The LMD decomposed the recombinational signal and obtained new PF components, and energy characteristics were got from the new PF component.Secondly, the kernel function of LS-SVM is improved to solve the problem of kernel parameter selection. Lagrange parameters were weighted by feature weighting method, and their weighted average value was taken as the threshold of pruning method to reduce the sparsity. Finally, energy characteristics were put into LS-SVM to train and predict.Experiments showed that this new method could fulfil adaptive classification of bearing fault signals and definite fault conclusion quickly and effectively. |
| format | Article |
| id | doaj-art-e21d594c823d4ba8bbc8752e1e132e6c |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2022-02-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-e21d594c823d4ba8bbc8752e1e132e6c2025-08-20T03:16:19ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832022-02-012701929910.15938/j.jhust.2022.01.012There Search of LS-SVM Based on LMD Morphology FilterMENG Liang0XU Tong-le1MA Jin-ying2CAI Dao-yong3School Mechanical Engineering, Shandong University of Technology, Zibo 255049, ChinaSchool Mechanical Engineering, Shandong University of Technology, Zibo 255049, ChinaSchool of Agriculture Engineering and Food Science, Shandong University of Technology, Zibo 255049, ChinaShandong Keda M&E Technology Co.,Ltd., Jining 272000, ChinaIn the diagnosis of bearing, the LS-SVM method research with LMD morphological filtering was put out in order to solve the problem about the kernel function parameter selection and the bad sparsity of least squares vector machine (LS-SVM).First, the LMD was used to decompose the measured signal and PF components were obtained.The correlation analysis was carried out to remove the false components, and the noise of PF components was reduced by morphological filtering.The LMD decomposed the recombinational signal and obtained new PF components, and energy characteristics were got from the new PF component.Secondly, the kernel function of LS-SVM is improved to solve the problem of kernel parameter selection. Lagrange parameters were weighted by feature weighting method, and their weighted average value was taken as the threshold of pruning method to reduce the sparsity. Finally, energy characteristics were put into LS-SVM to train and predict.Experiments showed that this new method could fulfil adaptive classification of bearing fault signals and definite fault conclusion quickly and effectively.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2060local mean decompositionmorphological filteringpruning methodleast squares support vector machinefault diagnosis |
| spellingShingle | MENG Liang XU Tong-le MA Jin-ying CAI Dao-yong There Search of LS-SVM Based on LMD Morphology Filter Journal of Harbin University of Science and Technology local mean decomposition morphological filtering pruning method least squares support vector machine fault diagnosis |
| title | There Search of LS-SVM Based on LMD Morphology Filter |
| title_full | There Search of LS-SVM Based on LMD Morphology Filter |
| title_fullStr | There Search of LS-SVM Based on LMD Morphology Filter |
| title_full_unstemmed | There Search of LS-SVM Based on LMD Morphology Filter |
| title_short | There Search of LS-SVM Based on LMD Morphology Filter |
| title_sort | there search of ls svm based on lmd morphology filter |
| topic | local mean decomposition morphological filtering pruning method least squares support vector machine fault diagnosis |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2060 |
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