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基于结合参数整定和特征选择策略的模糊支持向量机的故障诊断
引用本文:毛勇,夏铮,尹征,孙优贤,万征.基于结合参数整定和特征选择策略的模糊支持向量机的故障诊断[J].中国化学工程学报,2007,15(2):233-239.
作者姓名:毛勇  夏铮  尹征  孙优贤  万征
作者单位:[1]State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China [2]School of Information Technology, Jiangxi University of Finance & Economics, Nanchang 330013, China
基金项目:Supported by the Special Funds for Major State Basic Research Program of China (973 Program, No.2002CB312200), the National Natural Science Foundation of China (No.60574019, No.60474045), the Key Technologies R&D Program of Zhejiang Province (No.2005C21087), and the Academician Foundation of Zhejiang Province (No.2005A1001-13).
摘    要:This study describes a classification methodology based on support vector machines (SVMs), which offer superior classification performance for fault diagnosis in chemical process engineering. The method incorporates an efficient parameter tuning procedure (based on minimization of radius/margin bound for SVM's leave-one-out errors) into a multi-class classification strategy using a fuzzy decision factor, which is named fuzzy support vector machine (FSVM). The datasets generated from the Tennessee Eastman process (TEP) simulator were used to evaluate the classification performance. To decrease the negative influence of the auto-correlated and irrelevant variables, a key variable identification procedure using recursive feature elimination, based on the SVM is implemented, with time lags incorporated, before every classifier is trained, and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation. Performance comparisons are implemented among several kinds of multi-class decision machines, by which the effectiveness of the proposed approach is proved.

关 键 词:模糊支持向量机  参数调谐  特征提取  故障诊断
收稿时间:6 January 2006
修稿时间:2006-01-062006-09-18

Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection
Yong MAO, Zheng XIA, Zheng YIN, Youxian SUN,Zheng WAN.Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection[J].Chinese Journal of Chemical Engineering,2007,15(2):233-239.
Authors:Yong MAO  Zheng XIA  Zheng YIN  Youxian SUN  Zheng WAN
Affiliation: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China  School of Information Technology, Jiangxi University of Finance & Economics, Nanchang 330013, China
Abstract:This study describes a classification methodology based on support vector machines(SVMs),which offer superior classification performance for fault diagnosis in chemical process engineering.The method incorporates an efficient parameter tuning procedure(based on minimization of radius/margin bound for SVM's leave-one-out errors)into a multi-class classification strategy using a fuzzy decision factor,which is named fuzzy support vector machine(FSVM).The datasets generated from the Tennessee Eastman process(TEP)simulator were used to evaluate the clas-sification performance.To decrease the negative influence of the auto-correlated and irrelevant variables,a key vari-able identification procedure using recursive feature elimination,based on the SVM is implemented,with time lags incorporated,before every classifier is trained,and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation.Performance comparisons are implemented among several kinds of multi-class decision machines,by which the effectiveness of the proposed approach is proved.
Keywords:fuzzy support vector machine  parameter tuning  fault diagnosis  key variable identification
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