文献类型：期刊文献

英文题名：Related and independent variable fault detection based on KPCA and SVDD

作者：Huang, Jian[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：2016

卷号：39

起止页码：88

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20160501856003);WOS:【SCI-EXPANDED(收录号:WOS:000371101700008)】；

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：Independent variables; Related variables; Process monitoring; Kernel principal component analysis; Support vector data description

摘要：This paper proposes a new independent and related variable monitoring based on kernel principal component analysis (KPCA) and support vector data description (SVDD) algorithm. Some process variables are considered independent from other variables and the monitoring of independent and related variables should be performed separately. First, an independent variable division strategy based on mutual information is presented. Second, SVDD and KPCA methods are adopted to monitor independent variable space and related variable space, respectively. Finally, a general statistic is built according to the monitoring results of SVDD and KPCA. The proposed KPCA-SVDD method considers the related and independent characters of variables. This method combines the advantages of KPCA in managing nonlinear related variables and those of SVDD in handling independent variables. A numerical system and the Tennessee Eastman process are used to examine the efficiency of the proposed method. Simulation results have proved the superiority of KPCA-SVDD method. (C) 2016 Elsevier Ltd. All rights reserved.