文献类型：期刊文献

英文题名：Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring

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：2013

卷号：127

起止页码：121

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20240915629269);WOS:【SCI-EXPANDED(收录号:WOS:000324011300015)】；

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：Weighted kernel principal; component analysis; Nonlinear process monitoring; Probability density estimation; Moving window

摘要：Kernel principal component analysis (KPCA) has been widely used in nonlinear process monitoring: however, KPCA does not always perform efficiently because useful information may be submerged under retained KPCs. To address this shortcoming, probability density estimation- and moving weighted window-based KPCA (PM-WKPCA) is proposed. PM-WKPCA is used mainly to estimate the probability and evaluate the importance of each KPC by kernel density estimation and then set different weighting values on KPCs to highlight the useful information. The status of the process is also evaluated comprehensively using weighted statistics within a moving window. The efficiency of the proposed method is demonstrated by the following: case studies on a numerical nonlinear system, the simulated continuously stirred tank reactor process, and the Tennessee Eastman process. Monitoring results indicate that the proposed method is superior to the conventional PCA, KPCA, and some typical extension methods. (C) 2013 Elsevier B.V. All rights reserved.