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《Journal of Zhejiang University of Technology》 2015-06
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Fault diagnosis for automobile coating equipment based on SVM

YE Yongwei;REN Shedong;LU Junjie;YANG Chao;Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology,Ministry of Education,Zhejiang University of Technology;  
Aiming at the difficulty in discovering and eliminating the system faults of automobile coating equipment promptly,a new method of fault diagnosis based on SVM was proposed.The method was created by the structural risk minimization principle in the statistical learning theory,which overcame the weakness of the traditional learning method in asymptotic theory.And it was suitably used in pattern recognition with finite samples and made the output more precise.The classifiers were also structured according to the equipment monitoring parameters and fault types of the heating system.The optimal kernel parameters and punished parameters were searched by the cross validation grid search method in order to establish the SVM model for fault diagnosis.The samples of dimension reducing by the PCA were then taken into training in the model.It was finally reveals by the simulation experiment that it can successfully and precisely accomplish the fault diagnosis with the SVM method.
【Fund】: 浙江省自然科学基金资助项目(LY12E05025)
【CateGory Index】: U468.2;TP181
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