文献类型：期刊文献

英文题名：Complex dynamic process monitoring method based on slow feature analysis model of multi-subspace partitioning

作者：Li, Zhichao[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2019

卷号：95

起止页码：68

外文期刊名：ISA TRANSACTIONS

收录：;EI(收录号：20192206991471);WOS:【SCI-EXPANDED(收录号:WOS:000504512700008)】；

基金：The authors are grateful for the support of National Natural Science Foundation of China (21878081) and Fundamental Research Funds for the Central Universities under Grant of China (222201917006).

语种：英文

外文关键词：Ensemble monitoring model; Slow feature analysis; Hierarchical clustering; SVDD; Multi-subspace partitioning

摘要：This study presents an ensemble monitoring strategy based on slow feature analysis (SFA) model of multi-subspace partitioning for dynamic large-scale process. SFA can effectively extract the various dynamics of process data, where the relationship between process data and slow features (SFs) can be revealed by transformation matrix. The similar projecting directions represent similar importance of variables, and corresponding latent variables (LVs) will show similar monitoring behavior. Several LV subspaces are obtained by dividing the transformation vectors with higher similarity into the same sub-block automatically based on the defined process variable related index and hierarchical clustering, which can avoid the problems of information loss and the selection of SFs. Then, the S-2 statistics constructed in each subspaces are integrated by support vector data description to show an intuitive detection results. Experiments on Tennessee Eastman benchmark process and wastewater treatment process have validated the proposed strategy's effectiveness and excellence. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.