文献类型：期刊文献

英文题名：Non-Gaussian chemical process monitoring with adaptively weighted independent component analysis and its applications

作者：Jiang, Qingchao[1];Yan, Xuefeng[1]

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

年份：2013

卷号：23

期号：9

起止页码：1320

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20134216850328);WOS:【SCI-EXPANDED(收录号:WOS:000327005700009)】；

基金：The authors gratefully acknowledge the support from the following foundations: 973 project of China (2013CB733600), National Natural Science Foundation of China (21176073), Program for New Century Excellent Talents in University (NCET-09-0346) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Chemical processes; Weighted independent component analysis; Process monitoring; Fault detection

摘要：Chemical process monitoring based on independent component analysis (ICA) is among the most widely used multivariate statistical process monitoring methods and has progressed very quickly in recent years. Generally, ICA methods initially employ several independent components (ICs) that are ordered according to certain criteria for process monitoring. However, fault information has no definite mapping relationship to a certain IC, and useful information might be submerged under the retained ICs. Thus, weighted independent component analysis (WICA) for fault detection and identification is proposed to process useful submerged information and reduce missed detection rates of I-2 statistics. The main idea of WICA is to initially build the conventional ICA model and then use the change rate of the I-2 statistic (RI2) to evaluate the importance of each IC. The important ICs tend to have higher RI2; thus, higher weighting values are then adaptively set for these ICs to highlight the useful fault information. Case studies on both simple simulated and Tennessee Eastman processes demonstrate the effectiveness of the WICA method. Monitoring results indicate that the performance of I-2 statistics improved significantly compared with principal component analysis and conventional ICA methods. (C) 2013 Elsevier Ltd. All rights reserved.