文献类型：期刊文章

作　　者：陈国金[1] 梁军[1] 钱积新[1]

机构地区：[1]浙江大学系统工程研究所,浙江杭州310027

出　　处：《化工学报》

基　　金：国家高技术研究发展计划 (No 863 -5 11-92 0 -0 11;No 2 0 0 1AA4112 3 0 )资助项目~~

年　　份：2003

卷　　号：54

期　　号：10

起止页码：1474-1477

语　　种：中文

收录情况：AJ、BDHX、BDHX2000、CAS、CSCD、CSCD2011_2012、EI(收录号：2004088028988)、IC、JST、RCCSE、SCOPUS、ZGKJHX、核心刊

摘　　要：Multivariate statistical process control (MSPC) has been successfully applied to performance monitoring and fault diagnosis for chemical processes However, traditional MSPC are based upon the assumption that the separated latent variables must be subject to normal probability distribution, which sometimes can not be satisfied In this paper, a novel method combining principal component analysis (PCA) and independent component analysis (ICA) is proposed to model non Gaussian data from industry and improve the monitoring performance of process In order to deal with the uncertainty of probability distribution within the independent component, a kind of classifier referred to as support vector classifier is used for classifying the abnormal modes Simulation result for a nonisothermal continuous stirred tank reactor (CSTR) by the presented method verifies the effectiveness of ICA based

关 键 词：独立元分析 支持向量分类器 过程监控  故障诊断

分 类 号：TP277] O212.4]