文献类型：期刊文献

英文题名：Plant-wide process monitoring based on mutual information-multiblock principal component analysis

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：2014

卷号：53

期号：5

起止页码：1516

外文期刊名：ISA TRANSACTIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000344127800020)】；

基金：The authors gratefully acknowledge the support from the following foundations: 973 project of China (Grant no. 2013CB733600), National Natural Science Foundation of China (Grant no. 21176073), Program for New Century Excellent Talents in University (Grant no. NCET-09-0346) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Plant-wide process monitoring; Mutual information; Multiblock PCA; Support vector data description

摘要：Multiblock principal component analysis (MBPCA) methods are gaining increasing attentions in monitoring plant-wide processes. Generally, MBPCA assumes that some process knowledge is incorporated for block division; however, process knowledge is not always available. A new totally data-driven MBPCA method, which employs mutual information (MI) to divide the blocks automatically, has been proposed. By constructing sub-blocks using MI, the division not only considers linear correlations between variables, but also takes into account non-linear relations thereby involving more statistical information. The PCA models in sub-blocks reflect more local behaviors of process, and the results in all blocks are combined together by support vector data description. The proposed method is implemented on a numerical process and the Tennessee Eastman process. Monitoring results demonstrate the feasibility and efficiency. (C) 2014 ISA. Published by Elsevier Ltd. All rights reserved.