RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM
A method for optimizing support vector machine（ SVM） parameters was proposed based on isometric feature mapping（ Isomap） and improved genetic algorithm（ IGA）,to solve the problem that parameters are difficult to choose in the process of gearbox fault diagnosis and recognition. Firstly Isomap was use...
| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2016-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.01.008 |
| _version_ | 1797767934508007424 |
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| author | LIU ZhiChuan TANG LiWei CAO LiJun |
| author_facet | LIU ZhiChuan TANG LiWei CAO LiJun |
| author_sort | LIU ZhiChuan |
| collection | DOAJ |
| description | A method for optimizing support vector machine（ SVM） parameters was proposed based on isometric feature mapping（ Isomap） and improved genetic algorithm（ IGA）,to solve the problem that parameters are difficult to choose in the process of gearbox fault diagnosis and recognition. Firstly Isomap was used for high-dimensional feature data of gearbox vibration signal under the automatic optimal neighborhood parameter value. Then optimized the castigation parameter and nucleus function parameter of SVM by improved genetic algorithm. Finally the recognition and classification of lower dimensional data were realized by SVM. The proposed method was applied to gearbox fault diagnosis. The result shows that the method has higher diagnosis accuracy,and it has better diagnosis effect compared with traditional SVM method. |
| first_indexed | 2024-03-12T20:47:11Z |
| format | Article |
| id | doaj.art-7b2718ad78a64c419acfb1e18ac769c7 |
| institution | Directory Open Access Journal |
| issn | 1001-9669 |
| language | zho |
| last_indexed | 2024-03-12T20:47:11Z |
| publishDate | 2016-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj.art-7b2718ad78a64c419acfb1e18ac769c72023-08-01T07:41:27ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692016-01-0138384330593751RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVMLIU ZhiChuanTANG LiWeiCAO LiJunA method for optimizing support vector machine（ SVM） parameters was proposed based on isometric feature mapping（ Isomap） and improved genetic algorithm（ IGA）,to solve the problem that parameters are difficult to choose in the process of gearbox fault diagnosis and recognition. Firstly Isomap was used for high-dimensional feature data of gearbox vibration signal under the automatic optimal neighborhood parameter value. Then optimized the castigation parameter and nucleus function parameter of SVM by improved genetic algorithm. Finally the recognition and classification of lower dimensional data were realized by SVM. The proposed method was applied to gearbox fault diagnosis. The result shows that the method has higher diagnosis accuracy,and it has better diagnosis effect compared with traditional SVM method.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.01.008Isomap;Genetic algorithm;Support vector machine（SVM）;Gearbox;Fault diagnosis |
| spellingShingle | LIU ZhiChuan TANG LiWei CAO LiJun RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM Jixie qiangdu Isomap;Genetic algorithm;Support vector machine（SVM）;Gearbox;Fault diagnosis |
| title | RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM |
| title_full | RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM |
| title_fullStr | RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM |
| title_full_unstemmed | RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM |
| title_short | RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM |
| title_sort | research on gearbox fault diagnosis base on isomap and iga svm |
| topic | Isomap;Genetic algorithm;Support vector machine（SVM）;Gearbox;Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.01.008 |
| work_keys_str_mv | AT liuzhichuan researchongearboxfaultdiagnosisbaseonisomapandigasvm AT tangliwei researchongearboxfaultdiagnosisbaseonisomapandigasvm AT caolijun researchongearboxfaultdiagnosisbaseonisomapandigasvm |