文献类型：期刊文献

英文题名：Multivariate Statistical Monitoring of Key Operation Units of Batch Processes Based on Time-Slice CCA

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

机构：[1]Hong Kong Univ Sci & Technol, Dept Chem & Biomol Engn, Hong Kong 999077, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Nanjing Univ Technol, Coll Elect Engn & Control Sci, Nanjing 211816, Jiangsu, Peoples R China

年份：2019

卷号：27

期号：3

起止页码：1368

外文期刊名：IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY

收录：;EI(收录号：20181004860647);WOS:【SCI-EXPANDED(收录号:WOS:000464919700041)】；

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, in part by Fundamental Research Funds for the Central Universities under Grant 222201717006 and Grant 222201714027, in part by Science Foundation of Jiangsu Province under Grant BK20140953, and in part by the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017. Recommended by Associate Editor A. Serrani. (Corresponding author: Furong Gao.)

语种：英文

外文关键词：Batch processes; canonical correlation analysis (CCA); key operation unit monitoring; multivariate statistical process monitoring

摘要：A modern batch process can be characterized by a large scale and multiple operation units, and local fault detection for the key units of such a batch process is imperative. A time-slice canonical correlation analysis (CCA)-based multivariate statistical monitoring scheme for the key operation units of batch processes is proposed. First, the three-way batch process data are unfolded into the time-slice data. Second, CCA modeling is performed at each time instant to explore the correlation between the key units and the entire process. Then, a fault detection residual is generated and monitoring statistics are constructed. The statistics discriminate both the process status and the type of a detected fault, a fault relevant or irrelevant to the other units. The feasibility and superiority of the proposed fault detection scheme are demonstrated by case studies on a numerical example and an industrial injection molding process.