Sensor fault diagnosis of nonlinear processes based on structured kernel principal component analysis

查看全文 作　　者：Kechang [1,2]FU;Liankui [2]DAI;Tiejun [2]WU;Ming [1]ZHU

机构地区：[1]Department of Control Engineering, Chengdu University of Information Technology, Chengdu Sichuan 610225, China;[2]National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou Zhejiang 310027, China

出　　处：《控制理论与应用(英文版)》索引2009年第7卷第3期,共7页

基　　金：supported by Scientific Reserch Fund of SiChuan Provincial Education Department (No.07ZB013);by the Scientific ResearchFoundation of CUIT (No.CSRF200704) 摘　　要：A new sensor fault diagnosis method based on structured kernel principal component analysis (KPCA) is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor (CSTR) process. 关键词：传感器故障核主成分分析故障诊断系统非线性过程基础连续搅拌反应釜 KPCA 故障诊断方法

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Sensor fault diagnosis of nonlinear processes based on structured kernel principal component analysis

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