Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings
Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection. In this paper, a parameter optimization algorithm for the SVM is proposed based on mu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/3091618 |
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| _version_ | 1850159442657542144 |
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| author | Jianbin Xiong Qinghua Zhang Qiong Liang Hongbin Zhu Haiying Li |
| author_facet | Jianbin Xiong Qinghua Zhang Qiong Liang Hongbin Zhu Haiying Li |
| author_sort | Jianbin Xiong |
| collection | DOAJ |
| description | Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection. In this paper, a parameter optimization algorithm for the SVM is proposed based on multi-genetic algorithm. The algorithm optimizes the correlation kernel parameters of the SVM using evolutionary search principles of multiple swarm genetic algorithms to obtain a superior SVM prediction model. The experimental results demonstrate that by combining the genetic algorithm and SVM algorithm, fault diagnosis can be effectively realized for bearings of rotating machinery. |
| format | Article |
| id | doaj-art-b93293df2f044a2b9632a376d4c0a94f |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-b93293df2f044a2b9632a376d4c0a94f2025-08-20T02:23:32ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/30916183091618Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of BearingsJianbin Xiong0Qinghua Zhang1Qiong Liang2Hongbin Zhu3Haiying Li4School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaGuangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Maoming 525000, ChinaSchool of Computer, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaGuangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Maoming 525000, ChinaSchool of Computer, Jiaying University, Meizhou 514015, ChinaOverstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection. In this paper, a parameter optimization algorithm for the SVM is proposed based on multi-genetic algorithm. The algorithm optimizes the correlation kernel parameters of the SVM using evolutionary search principles of multiple swarm genetic algorithms to obtain a superior SVM prediction model. The experimental results demonstrate that by combining the genetic algorithm and SVM algorithm, fault diagnosis can be effectively realized for bearings of rotating machinery.http://dx.doi.org/10.1155/2018/3091618 |
| spellingShingle | Jianbin Xiong Qinghua Zhang Qiong Liang Hongbin Zhu Haiying Li Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings Shock and Vibration |
| title | Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings |
| title_full | Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings |
| title_fullStr | Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings |
| title_full_unstemmed | Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings |
| title_short | Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings |
| title_sort | combining the multi genetic algorithm and support vector machine for fault diagnosis of bearings |
| url | http://dx.doi.org/10.1155/2018/3091618 |
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