文献类型：期刊文献

英文题名：Fault detection in dynamic plant-wide process by multi-block slow feature analysis and support vector data description

作者：Huang, Jian[1,2];Ersoy, Okan K.[3];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing, Peoples R China;[3]Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA

年份：2019

卷号：85

起止页码：119

外文期刊名：ISA TRANSACTIONS

收录：;EI(收录号：20184406020776);WOS:【SCI-EXPANDED(收录号:WOS:000459520400011)】；

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 (222201717006).

语种：英文

外文关键词：Multi-block algorithm; Slow feature analysis; Support vector data description; Fault detection

摘要：This study describes a dynamic large-scale process fault detection algorithm based on multi-block slow feature analysis by taking advantages of both multi-block algorithms in highlighting the local information and slow feature analysis in extracting the different dynamics of process data. A mutual information-based relevance matrix is first calculated to measure the correlation between any two variables, and then K-means clustering is used to cluster the original variables into several blocks by gathering the variables with similar relevance vectors into the same block. Slow feature analysis is applied in each block. A support vector data description is utilized to give a final decision. The proposed algorithm is tested with a well-known Tennessee Eastman (TE) process. The fault detection results show the efficiency and the superiority of the proposed method as compared to other related methods. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.