文献类型：期刊文献

英文题名：Data-Driven Communication Efficient Distributed Monitoring for Multiunit Industrial Plant-Wide Processes

作者：Jiang, Qingchao[1,2];Chen, Shutian[1];Yan, Xuefeng[1];Kano, Manabu[2];Huang, Biao[3]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Kyoto Univ, Dept Syst Sci, Kyoto 6068501, Japan;[3]Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada

年份：2022

卷号：19

期号：3

起止页码：1913

外文期刊名：IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

收录：;EI(收录号：20212310473568);WOS:【SCI-EXPANDED(收录号:WOS:000732355600001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61973119, in part by the China Scholarship Council under Grant 201906745007, and in part by the Shanghai Rising-Star Program under Grant 20QA1402600.

语种：英文

外文关键词：Monitoring; Correlation; Process monitoring; Fault detection; Distributed databases; Principal component analysis; Computational modeling; Absolute shrinkage and selection operator; canonical correlation analysis (CCA); distributed process monitoring; latent variable correlation analysis (LVCA); variable communication

摘要：This study develops a novel data-driven latent variable correlation analysis (LVCA) framework to achieve communication efficient distributed monitoring for industrial plant-wide processes. Process data of a local unit are first projected into a dominant latent variable subspace and a residual subspace to characterize the correlation within the local unit. Then, least absolute shrinkage and selection operator is used to determine communication variables from neighboring units that are beneficial for monitoring the local unit. Thereafter, canonical correlation analysis is performed between the dominant subspace and communication variables to characterize the correlation between units. Finally, a distributed monitor is established for each unit, which considers the correlation within the local unit and the correlation between different operation units. The proposed LVCA-based distributed monitoring scheme is applied on a numerical example, the Tennessee Eastman benchmark process, and a lab-scale distillation process. Comparison results with some state-of-the-art methods verify the effectiveness.