文献类型：期刊文献

英文题名：Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA

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：2015

卷号：32

起止页码：38

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20152200888357);WOS:【SCI-EXPANDED(收录号:WOS:000359166100005)】；

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：Nonlinear plant-wide process monitoring; Multiblock kernel principal component analysis; Mutual information-spectral clustering; Bayesian inference

摘要：Multiblock or distributed strategies are generally used for plant-wide process monitoring, and the blocks are usually obtained based on prior process knowledge. However, process knowledge is not always available in practical application. This work aims to develop a totally data-driven distributed method for nonlinear plant-wide process monitoring. By performing mutual information-spectral clustering, the measured variables are automatically divided into sub-blocks that account for both linear and nonlinear relations among variables. Considering that the variables in the same sub-block can be nonlinearly related, kernel principal component analysis (KPCA) monitoring model is established in each sub-block. The sub-KPCA models reflect more local behaviors of a process, and the monitoring results of all blocks are combined together by Bayesian inference to provide an intuitionistic indication. The efficiency of the proposed method is demonstrated using a numerical example and the Tennessee Eastman benchmark process. (C) 2015 Elsevier Ltd. All rights reserved.