文献类型：期刊文献

英文题名：Chemical processes monitoring based on weighted principal component analysis and its application

作者：Jiang, Qingchao[1];Yan, Xuefeng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2012

卷号：119

起止页码：11

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242716585007);WOS:【SCI-EXPANDED(收录号:WOS:000311661200002)】；

基金：The authors gratefully acknowledge the supports from the following foundations: the National Natural Science Foundation of China (21176073), Doctoral Fund of Ministry of Education of China (20090074110005), Program for New Century Excellent Talents in University (NCET-09-0346), "Shu Guang" project (09SG29), 973 project (2012CB721006) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Chemical process monitoring; Fault detection; Fault diagnosis; Weighted principal component

摘要：Conventional principal component analysis (PCA)-based methods employ the first several principal components (PCs) which indicate the most variances information of normal observations for process monitoring. Nevertheless, fault information has no definite mapping relationship to a certain PC and useful information might be submerged under the retained PCs. A new version of weighted PCA (WPCA) for process monitoring is proposed to deal with the situation of useful information being submerged and reduce missed detection rates of T-2 statistic. The main idea of WPCA is building conventional PCA model and then using change rate of T-2 statistic along every PC to capture the most useful information in process, and setting different weighting values for PCs to highlight useful information when online monitoring. Case studies on Tennessee Eastman process demonstrate the effectiveness of the proposed scheme and monitoring results are compared with conventional PCA method. (C) 2012 Elsevier B.V. All rights reserved.