文献类型：期刊文献

英文题名：Multiobjective Two-Dimensional CCA-Based Monitoring for Successive Batch Processes With Industrial Injection Molding Application

作者：Jiang, Qingchao[1,2];Gao, Furong[2];Yan, Xuefeng[1];Yi, Hui[3]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Hong Kong Univ Sci & Technol, Dept Chem & Biomol Engn, Clear Water Bay, Hong Kong, Peoples R China;[3]Nanjing Univ Technol, Coll Elect Engn & Control Sci, Nanjing 211816, Jiangsu, Peoples R China

年份：2019

卷号：66

期号：5

起止页码：3825

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

收录：;EI(收录号：20183205663576);WOS:【SCI-EXPANDED(收录号:WOS:000455188700050)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61603138 and Grant 61503181, in part by the Guangdong Innovative and Entrepreneurial Research Team Program under Grant 2013G076, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Canonical correlation analysis (CCA); multiobjective evolutionary optimization; multivariate statistical fault detection; successive batch processes; two-dimensional monitoring

摘要：Successive batch processes generally involve within-batch and batch-to-batch correlations, and monitoring of such batch processes is imperative. This paper proposes a multiobjective two-dimensional canonical correlation analysis (M2D-CCA)-based fault detection scheme to achieve efficient monitoring of successive batch processes. First, three-way historical batch process data are unfolded into two-way time-slice data. Second, for each time-slice measurement, CCA is performed between the current measurement and previous measurements from both time and batch directions, which takes the within-batch and batch-to-batch correlations into account. To determine the involved measurements and eliminate the influence of unrelated variables, multiobjective evolutionary optimization is performed, which tries to maximize the preserved canonical correlation coefficients and minimize the number of involved variables. Finally, based on the established M2D-CCA model, an optimal fault detection residual is generated for each time-slice measurement. The M2D-CCA fault detection scheme performs fault detection using the current measurement and the information provided by its previous samples and batches, and therefore exhibits a superior monitoring performance. The M2D-CCA fault detection approach is tested on a numerical example and an industrial injection molding process. Monitoring results verify the feasibility and superiority of the proposed monitoring scheme.