Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization
The failure of rotating machinery can result in fatal damage and economic loss since rotating machinery plays an important role in the modern manufacturing industry. The development of a reliable and efficient intelligent fault diagnosis approach is an ongoing attempt. Support vector machine (SVM) i...
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| Published in: | Neurocomputing (Amsterdam) Vol. 167; pp. 260 - 279 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.11.2015
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| Subjects: | |
| ISSN: | 0925-2312, 1872-8286 |
| Online Access: | Get full text |
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| Abstract | The failure of rotating machinery can result in fatal damage and economic loss since rotating machinery plays an important role in the modern manufacturing industry. The development of a reliable and efficient intelligent fault diagnosis approach is an ongoing attempt. Support vector machine (SVM) is a widely used machine learning method in intelligent fault diagnosis. But finding out good features that can discriminate different fault conditions and optimizing parameters for support vector machine can be regarded as the most two important problems that can highly affect the final diagnosis accuracy of support vector machine. Until now, the two issues of feature selection and parameter optimization are usually treated separately, weakening the effects of both efforts. Therefore, an ant colony algorithm for synchronous feature selection and parameter optimization for support vector machine in intelligent fault diagnosis of rotating machinery is presented. Comparing with other methods, the advantages of the proposed method are evaluated on an experiment of rotor system and an engineering application of locomotive roller bearings, which proves it can attain much better results. |
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| AbstractList | The failure of rotating machinery can result in fatal damage and economic loss since rotating machinery plays an important role in the modern manufacturing industry. The development of a reliable and efficient intelligent fault diagnosis approach is an ongoing attempt. Support vector machine (SVM) is a widely used machine learning method in intelligent fault diagnosis. But finding out good features that can discriminate different fault conditions and optimizing parameters for support vector machine can be regarded as the most two important problems that can highly affect the final diagnosis accuracy of support vector machine. Until now, the two issues of feature selection and parameter optimization are usually treated separately, weakening the effects of both efforts. Therefore, an ant colony algorithm for synchronous feature selection and parameter optimization for support vector machine in intelligent fault diagnosis of rotating machinery is presented. Comparing with other methods, the advantages of the proposed method are evaluated on an experiment of rotor system and an engineering application of locomotive roller bearings, which proves it can attain much better results. |
| Author | Chen, Wei Zhang, XiaoLi Wang, BaoJian Chen, XueFeng |
| Author_xml | – sequence: 1 givenname: XiaoLi surname: Zhang fullname: Zhang, XiaoLi organization: State Key Laboratory for Manufacturing Systems Engineering, Xi׳an Jiaotong University, Xi׳an 710049, PR China – sequence: 2 givenname: Wei surname: Chen fullname: Chen, Wei organization: Air Force Engineering University, Xi׳an 710038, PR China – sequence: 3 givenname: BaoJian surname: Wang fullname: Wang, BaoJian organization: State Key Laboratory for Manufacturing Systems Engineering, Xi׳an Jiaotong University, Xi׳an 710049, PR China – sequence: 4 givenname: XueFeng surname: Chen fullname: Chen, XueFeng email: chenxf@mail.xjtu.edu.cn organization: State Key Laboratory for Manufacturing Systems Engineering, Xi׳an Jiaotong University, Xi׳an 710049, PR China |
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| Keywords | Roller bearing Support vector machine Intelligent fault diagnosis Rotating machinery Rotor Ant colony algorithm |
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| SubjectTerms | Ant colony algorithm Intelligent fault diagnosis Roller bearing Rotating machinery Rotor Support vector machine |
| Title | Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization |
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