Fuzzy support vector machine and its application to mechanical condition monitoring

Authors:
Zhousuo Zhang;Qiao Hu;Zhengjia He
Affiliations:
School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China;School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China;School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

Fuzzy support vector machine (FSVM) is applied in this paper, in order to resolve problem on bringing different loss for classification error to different fault type in mechanical fault diagnosis. Based on basic principle of FSVM, a method of determining numerical value range of fuzzy coefficient is proposed. Classification performance of FSVM is tested and verified by means of simulation data samples. A fuzzy fault classifier is constructed, and applied to condition monitoring of flue-gas turbine set. The results show that fuzzy coefficient can indicate importance degree of data sample, and classification error rate of important data sample can be decreased.