ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION <italic>k</italic>-SUB CONVEX HULL
Feature selection and classifier design are often studied separately in rolling bearing fault diagnosis, so it is difficult to obtain satisfactory classification accuracy. An adaptive feature selection <italic>k</italic>-sub convex hull (AFSKCH) classificationmodel was proposed by combin...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-04-01
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| Series: | Jixie qiangdu |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.02.001 |
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| author | HU AiRu WU ZhanTao YANG Yu CHENG JunSheng |
| author_facet | HU AiRu WU ZhanTao YANG Yu CHENG JunSheng |
| author_sort | HU AiRu |
| collection | DOAJ |
| description | Feature selection and classifier design are often studied separately in rolling bearing fault diagnosis, so it is difficult to obtain satisfactory classification accuracy. An adaptive feature selection <italic>k</italic>-sub convex hull (AFSKCH) classificationmodel was proposed by combining feature selection and classifier optimization, which realized the integration of adaptive featureselection and classification. Firstly, the convex hull distance function was used to maintain the local neighborhood structure on the data manifold, and the feature weight matrix was obtained by alternately constructing <italic>k</italic>-sub convex hulls. Secondly, thedistance was solved by the method of linear programming proximity, and the adaptive feature space was obtained by using the multiplier alternating direction method. Finally, the classification was carried out according to the minimum reconstruction distance from the test point to the <italic>k</italic>-sub convex hull. The analysis results of rolling bearing fault vibration signals show that the feature selection performance of this method is better than other feature selection methods, and the classification accuracy is higher. |
| format | Article |
| id | doaj-art-919070a3692242e78de2613b9bde8480 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-04-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-919070a3692242e78de2613b9bde84802025-01-15T02:45:23ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-04-014625526363929580ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION <italic>k</italic>-SUB CONVEX HULLHU AiRuWU ZhanTaoYANG YuCHENG JunShengFeature selection and classifier design are often studied separately in rolling bearing fault diagnosis, so it is difficult to obtain satisfactory classification accuracy. An adaptive feature selection <italic>k</italic>-sub convex hull (AFSKCH) classificationmodel was proposed by combining feature selection and classifier optimization, which realized the integration of adaptive featureselection and classification. Firstly, the convex hull distance function was used to maintain the local neighborhood structure on the data manifold, and the feature weight matrix was obtained by alternately constructing <italic>k</italic>-sub convex hulls. Secondly, thedistance was solved by the method of linear programming proximity, and the adaptive feature space was obtained by using the multiplier alternating direction method. Finally, the classification was carried out according to the minimum reconstruction distance from the test point to the <italic>k</italic>-sub convex hull. The analysis results of rolling bearing fault vibration signals show that the feature selection performance of this method is better than other feature selection methods, and the classification accuracy is higher.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.02.001 |
| spellingShingle | HU AiRu WU ZhanTao YANG Yu CHENG JunSheng ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION <italic>k</italic>-SUB CONVEX HULL Jixie qiangdu |
| title | ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION <italic>k</italic>-SUB CONVEX HULL |
| title_full | ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION <italic>k</italic>-SUB CONVEX HULL |
| title_fullStr | ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION <italic>k</italic>-SUB CONVEX HULL |
| title_full_unstemmed | ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION <italic>k</italic>-SUB CONVEX HULL |
| title_short | ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION <italic>k</italic>-SUB CONVEX HULL |
| title_sort | rolling bearing fault diagnosis based on adaptive feature selection italic k italic sub convex hull |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.02.001 |
| work_keys_str_mv | AT huairu rollingbearingfaultdiagnosisbasedonadaptivefeatureselectionitalickitalicsubconvexhull AT wuzhantao rollingbearingfaultdiagnosisbasedonadaptivefeatureselectionitalickitalicsubconvexhull AT yangyu rollingbearingfaultdiagnosisbasedonadaptivefeatureselectionitalickitalicsubconvexhull AT chengjunsheng rollingbearingfaultdiagnosisbasedonadaptivefeatureselectionitalickitalicsubconvexhull |