文献类型：期刊文献

英文题名：Process monitoring method based on correlation variable classification and vine copula

作者：Cui, Qun[1];Li, Shaojun[1]

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

年份：2020

卷号：98

期号：6

起止页码：1411

外文期刊名：CANADIAN JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20200608137729);WOS:【SCI-EXPANDED(收录号:WOS:000510639000001)】；

基金：Fundamental Research Funds for the Central Universities, Grant/Award Number: 222201917006; the National Natural Science Foundation of China, Grant/Award Number: 21676086

语种：英文

外文关键词：correlation; fault detection; variable classification; vine copula

摘要：Chemical processes are becoming increasingly complicated, leading to an increase in process variables and more complex relationships among them. The vine copula has a significant advantage in portraying the dependence of high-dimensional variables. However, as the dimensions increase, the vine copula model incurs a high computational load; such pressure greatly reduces model efficiency. Relationships among variables in the industrial process are complex. Different variables may be strongly or weakly associated or even independent. This paper proposes a process monitoring method based on correlation variable classification and vine copula. The weighted correlation measure is first used to divide variables into a correlated subspace and weakly correlated subspace. Then, two vine structures, C-vine and D-vine, are applied to the correlated and weakly correlated subspaces, respectively. This method takes advantage of C-vine for correlated variables and the flexibility of D-vine for weakly correlated variables. Finally, comprehensive statistics are established based on different subspaces. Monitoring results of the numerical system and the Tennessee Eastman process demonstrate the effectiveness and validity of the proposed method.