文献类型：期刊文献

英文题名：Multivariate statistical process monitoring using an improved independent component analysis

作者：Wang, Li[1];Shi, Hongbo[1]

机构：[1]E China Univ Chem Technol, Sch Informat Sci & Engn, Inst Automat, Shanghai 200237, Peoples R China

年份：2010

卷号：88

期号：4A

起止页码：403

外文期刊名：CHEMICAL ENGINEERING RESEARCH & DESIGN

收录：;EI(收录号：20101712890921);WOS:【SCI-EXPANDED(收录号:WOS:000277748900002)】；

基金：The authors gratefully acknowledge the support of the Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education and Shanghai Leading Academic Discipline Project (B504).

语种：英文

外文关键词：Process monitoring; Fault detection; Kernel independent component analysis; Kernel density estimation

摘要：An approach for multivariate statistical monitoring based on kernel independent component analysis (Kernel ICA) is presented. Different from the recently developed KICA which means kernel principal component analysis (KPCA) plus independent component analysis (ICA), Kernel ICA is an improvement of ICA and uses contrast functions based on canonical correlations in a reproducing kernel Hilbert space. The basic idea is to use Kernel ICA to extract independent components and later to provide enhanced monitoring of multivariate processes. I-2 (the sum of the squared independent scores) and squared prediction error (SPE) are adopted as statistical quantities. Besides, kernel density estimation (KDE) is described to calculate the confidence limits. The proposed monitoring method is applied to fault detection in the simulation benchmark of the wastewater treatment process and the Tennessee Eastman process, the simulation results clearly show the advantages of Kernel ICA monitoring in comparison to ICA and KICA monitoring. (C) 2009 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.