文献类型：期刊文献

英文题名：Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

作者：Jiang, Qingchao[1];Yan, Xuefeng[1];Huang, Biao[2]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada

年份：2019

卷号：58

期号：29

起止页码：12899

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20193307322461);WOS:【SCI-EXPANDED(收录号:WOS:000477787000001)】；

基金：The authors gratefully acknowledge the support from National Natural Science Foundation of China (61603138 and 21878081), the Programme of Introducing Talents of Discipline to Universities (the 111 Project, B17017), and the Natural Science and Engineering Research Council of Canada.

语种：英文

外文关键词：Decomposition - Statistical process control - Multivariant analysis - Industrial plants

摘要：Process monitoring is crucial for maintaining favorable operating conditions and has received considerable attention in previous decades. Currently, a plant-wide process generally consists of multiple operational units and a large number of measured variables. The correlation among the variables and units is complex and results in the imperative but challenging monitoring of such plant-wide processes. With the rapid advancement of industrial sensing techniques, process data with meaningful process information are collected. Data-driven multivariate statistical plant-wide process monitoring (DMSPPM) has become popular. The key idea of DMSPPM is first decomposing a plant-wide process into multiple subprocesses and then establishing a data-driven model for monitoring the process, in which process variable decomposition is important for guaranteeing the monitoring performance. In the current review, we first introduce the basics of multivariate statistical process monitoring and highlight the necessity of designing a distributed monitoring scheme. Then state-of-the-art DMSPPM methods are revisited. Finally, opportunities of and challenges to the DMSPPM methods are discussed.