FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTION
In order to diagnose fault effectively by using sensitive features contained in the feature set, KFDA was improved in this paper and a fault diagnosis method based on improved KFDA individual feature selection was proposed. Firstly, the mixed feature of the fault vibration signal was extracted from...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2019-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.03.004 |
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| _version_ | 1841535597806616576 |
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| author | CHEN Rui |
| author_facet | CHEN Rui |
| author_sort | CHEN Rui |
| collection | DOAJ |
| description | In order to diagnose fault effectively by using sensitive features contained in the feature set, KFDA was improved in this paper and a fault diagnosis method based on improved KFDA individual feature selection was proposed. Firstly, the mixed feature of the fault vibration signal was extracted from different angels, and the original high-dimensional and multi-domain feature set was constructed. Then, an improved kernel Fisher feature selection method was proposed and used to select individual sensitive feature subset for each pair of class. Finally, a one-against-one approach was applied to train several relevance vector machine（RVM） binary classifiers, and sensitive feature was input into the multi-class fault diagnosis model for recognizing the fault types. The experimental results of gear indicate that the proposed method is of high diagnostic accuracy. |
| format | Article |
| id | doaj-art-0c955ea49b904286bc949db389f14f30 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2019-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-0c955ea49b904286bc949db389f14f302025-01-15T02:29:49ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692019-01-014152753130604700FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTIONCHEN RuiIn order to diagnose fault effectively by using sensitive features contained in the feature set, KFDA was improved in this paper and a fault diagnosis method based on improved KFDA individual feature selection was proposed. Firstly, the mixed feature of the fault vibration signal was extracted from different angels, and the original high-dimensional and multi-domain feature set was constructed. Then, an improved kernel Fisher feature selection method was proposed and used to select individual sensitive feature subset for each pair of class. Finally, a one-against-one approach was applied to train several relevance vector machine（RVM） binary classifiers, and sensitive feature was input into the multi-class fault diagnosis model for recognizing the fault types. The experimental results of gear indicate that the proposed method is of high diagnostic accuracy.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.03.004KFDAIndividual feature selectionFault diagnosisGear |
| spellingShingle | CHEN Rui FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTION Jixie qiangdu KFDA Individual feature selection Fault diagnosis Gear |
| title | FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTION |
| title_full | FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTION |
| title_fullStr | FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTION |
| title_full_unstemmed | FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTION |
| title_short | FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTION |
| title_sort | fault diagnosis based on improved kfda individual feature selection |
| topic | KFDA Individual feature selection Fault diagnosis Gear |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.03.004 |
| work_keys_str_mv | AT chenrui faultdiagnosisbasedonimprovedkfdaindividualfeatureselection |