Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain
In order to improve reliability of wind power unit drivetrain,a fault diagnosis model based on quantum genetic algorithm and support vector machine（ SVM） is presented. The model of SVM is conformed,and the penalty parameter and Kernel function coefficient are optimized by quantum genetic algorithm,w...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2018-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.09.032 |
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| _version_ | 1841547518164336640 |
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| author | Liu Zhigang Zhao Xiaoyan Zhang Tao Ao Baolin Wang Juntao Dang Qiqian |
| author_facet | Liu Zhigang Zhao Xiaoyan Zhang Tao Ao Baolin Wang Juntao Dang Qiqian |
| author_sort | Liu Zhigang |
| collection | DOAJ |
| description | In order to improve reliability of wind power unit drivetrain,a fault diagnosis model based on quantum genetic algorithm and support vector machine（ SVM） is presented. The model of SVM is conformed,and the penalty parameter and Kernel function coefficient are optimized by quantum genetic algorithm,which coding and renewal of initial population are completed with quantum encoding and rotation gate,the accuracy of optimal solution is improved. Through using the optimized SVM model,with the test and calculation for drivetrain in three types of normal condition,surface wear and missing teeth,the accuracy rate of fault diagnosis can be effectively solved. |
| format | Article |
| id | doaj-art-bb3e37486205411484d77f55e427b31e |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-bb3e37486205411484d77f55e427b31e2025-01-10T14:40:12ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392018-01-014216416729938458Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit DrivetrainLiu ZhigangZhao XiaoyanZhang TaoAo BaolinWang JuntaoDang QiqianIn order to improve reliability of wind power unit drivetrain,a fault diagnosis model based on quantum genetic algorithm and support vector machine（ SVM） is presented. The model of SVM is conformed,and the penalty parameter and Kernel function coefficient are optimized by quantum genetic algorithm,which coding and renewal of initial population are completed with quantum encoding and rotation gate,the accuracy of optimal solution is improved. Through using the optimized SVM model,with the test and calculation for drivetrain in three types of normal condition,surface wear and missing teeth,the accuracy rate of fault diagnosis can be effectively solved.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.09.032Wind power unitDrivetrainFault diagnosisSupport vector machineQuantum genetic algorithm |
| spellingShingle | Liu Zhigang Zhao Xiaoyan Zhang Tao Ao Baolin Wang Juntao Dang Qiqian Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain Jixie chuandong Wind power unit Drivetrain Fault diagnosis Support vector machine Quantum genetic algorithm |
| title | Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain |
| title_full | Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain |
| title_fullStr | Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain |
| title_full_unstemmed | Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain |
| title_short | Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain |
| title_sort | study on the intelligent fault recognition algorithm for wind power unit drivetrain |
| topic | Wind power unit Drivetrain Fault diagnosis Support vector machine Quantum genetic algorithm |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.09.032 |
| work_keys_str_mv | AT liuzhigang studyontheintelligentfaultrecognitionalgorithmforwindpowerunitdrivetrain AT zhaoxiaoyan studyontheintelligentfaultrecognitionalgorithmforwindpowerunitdrivetrain AT zhangtao studyontheintelligentfaultrecognitionalgorithmforwindpowerunitdrivetrain AT aobaolin studyontheintelligentfaultrecognitionalgorithmforwindpowerunitdrivetrain AT wangjuntao studyontheintelligentfaultrecognitionalgorithmforwindpowerunitdrivetrain AT dangqiqian studyontheintelligentfaultrecognitionalgorithmforwindpowerunitdrivetrain |