文献类型：期刊文献

英文题名：Independent component analysis-based non-Gaussian process monitoring with preselecting optimal components and support vector data description

作者：Jiang, Qingchao[1];Yan, Xuefeng[1];Lv, Zhaomin[1];Guo, Meijin[2]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2014

卷号：52

期号：11

起止页码：3273

外文期刊名：INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

收录：;EI(收录号：20141617571946);WOS:【SCI-EXPANDED(收录号:WOS:000333885400008)】；

基金：The authors gratefully acknowledge the support from the following foundations: 973 project of China [2013CB733605]; National Natural Science Foundation of China [21176073] and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：independent component analysis; multivariate statistics; process monitoring; support vector data description; optimal component selection

摘要：Independent component analysis (ICA)-based process monitoring methods have rapidly progressed, but independent components (ICs) selection remains an open question. Subjective ICs selection would lead to useful information dispersion and affect the ICA monitoring performance. A novel ICA-based method integrated with preselecting optimal components and support vector machine data description (SVDD) technique is proposed to improve the non-Gaussian process monitoring performance. The proposed method first concentrates the informative ICs into one subspace for each fault and then the SVDD is employed to examine the variations in all subspaces. Case studies on a simulated process and Tennessee Eastman benchmark process demonstrate the effectiveness of the proposed scheme. The monitoring performances are significantly improved compared with the conventional ICA method.