文献类型：期刊文献

英文题名：Multisubspace Orthogonal Canonical Correlation Analysis for Quality-Related Plant-Wide Process Monitoring

作者：Song, Bing[1];Shi, Hongbo[1];Tan, Shuai[1];Tao, Yang[1]

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

年份：2021

卷号：17

期号：9

起止页码：6368

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20213310765364);WOS:【SCI-EXPANDED(收录号:WOS:000663538800052)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61673173, Grant 61703161, Grant 61673178, and Grant 61673177, in part by the National Natural Science Foundation of Shanghai under Grant 19ZR1473200 and Grant 17ZR1444700, and in part by Fundamental Research Funds for the Central Universities under Grant 222201717006. Paper no. TII-20-0973.

语种：英文

外文关键词：Monitoring; Correlation; Quality assessment; Product design; Real-time systems; Process control; Informatics; Data-driven; plant-wide; process monitoring; quality related; real-time

摘要：Plant-wide processes often have the characteristics of large-scale and multiple operating units. Moreover, due to the closed-loop control, it is possible that the fault never affects product quality. In this article, a novel data-driven method called multisubspace orthogonal canonical correlation analysis (CCA) is proposed, which can not only tell whether the fault occurs but can also judge whether the fault affects the product quality in real time. First, to reduce process analysis complexity and to construct an accurate monitoring model, the original process variable space is divided into four subspaces. Second, the developed orthogonal CCA is conducted on process data and quality data for correlation feature extraction. Then, the quality-related and quality-unrelated features are obtained. Afterward, a total of six monitoring statistics are constructed and integrated to four statistics with physical interpretation via the Bayesian fusion strategy. Finally, the developed method is tested under an industrial case.