| Support Vector Machine for mechanical faults classification |
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| 作者姓名： | 蒋志强 符寒光 李凌君 |
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| 作者单位： | Zhengzhou Aeronautical Institute of Industry Management，Zhengzhou 450015，China，Beijing Researching Institute for Metallurgical Equipment，Beijing 100029，China，School of Mechanical Engineering，Xi’an Jiaotong University，Xi’an 710049，China |
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| 摘 要： | INTRODUCTION Support Vector Machine (SVM) is a relativelynew soft computing method based on statisticallearning theory presented by Vapnik (1995). In SVM,original input space is mapped into a high dimen-sional dot product space called feature space in whichthe optimal hyperplane is determined to maximize thegeneralization ability of the classifier. The optimalhyperplane is found by exploiting a branch ofmathematics, called optimization theory, and re-specting the insights provided by …
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| 关 键 词： | SVM 支持向量装置 机械故障 故障分析 故障分类 智能诊断 |
Support Vector Machine for mechanical faults classification |
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| Jiang Zhi-qiang,Fu Hang-guang,Li Ling-jun.Support Vector Machine for mechanical faults classification[J].Journal of Zhejiang University Science,2005,6(5):433-439. |
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| Authors: | Jiang Zhi-qiang Fu Hang-guang Li Ling-jun |
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| Institution: | (1) Zhengzhou Aeronautical Institute of Industry Management, 450015 Zhengzhou, China;(2) Beijing Researching Institute for Metallurgical Equipment, 100029 Beijing, China;(3) School of Mechanical Engineering, Xi’an Jiaotong University, 710049 Xi’an, China |
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| Abstract: | Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based on binary classifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for online diagnosis for mechanical system. |
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| Keywords: | Support Vector Machine (SVM) Fault diagnosis Multi-fault classification Intelligent diagnosis |
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