文献类型：期刊文献

英文题名：Performance-Indicator-Oriented Concurrent Subspace Process Monitoring Method

作者：Song, Bing[1];Zhou, Xinggui[2];Shi, Hongbo[1];Tao, Yang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China

年份：2019

卷号：66

期号：7

起止页码：5535

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

收录：;EI(收录号：20183805820444);WOS:【SCI-EXPANDED(收录号:WOS:000460663300054)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703161 and Grant 61673173, in part by Fundamental Research Funds for the Central Universities under Grant 222201714031, and in part by China Postdoctoral Science Foundation under Grant 2017M611472.

语种：英文

外文关键词：Concurrent subspace; performance indicator; process monitoring; process safety; product quality

摘要：Process monitoring is an effective means to ensure process safety and improve product quality. On the one hand, it is possible that the fault will not affect process safety or product quality. On the other hand, not every fault affects both process safety and product quality simultaneously. To make process monitoring more purposeful and more accurate, a novel performance-indicator-oriented concurrent subspace (PIOCS) process monitoring method containing three subspaces with different degrees of importance is proposed in this paper. The first one is safety-related subspace, the second one is safety-unrelated quality-related subspace, and the third one is safety quality unrelated subspace. Through subspace division and construction, the fault can be categorized as safety-related fault, safety-unrelated quality-related fault, and safety quality unrelated fault. For mining safety and quality related information, both mechanism analysis and data analysis are used. In order to show the effectiveness and superiority, the proposed PIOCS method is tested under a number example and an industrial case study. Compared with traditional process monitoring methods, the proposed PIOCS method can obtain richer information, which is easy to show process operation condition clearly.