文献类型：期刊文献

英文题名：Vine Copula-Based Dependence Description for Multivariate Multimode Process Monitoring

作者：Ren, Xiang[1];Tian, Ying[1,2];Li, Shaojun[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Univ Sci & Technol, Sch Opt Elect & Comp Engn, Shanghai 200093, Peoples R China

年份：2015

卷号：54

期号：41

起止页码：10001

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20154401488882);WOS:【SCI-EXPANDED(收录号:WOS:000363438500010)】；

基金：The material is based upon work supported by the National Natural Science Foundation of China (under project No. 21176072) and the Fundamental Research Funds for the Central Universities. The authors would like to thank Warren D. Seider and Ian H. Moskowitz for their invaluable comments on this work.

语种：英文

外文关键词：Bayesian networks - Process control - Inference engines

摘要：A novel vine copula-based dependence description (VCDD) process monitoring approach is proposed. The main contribution is to extract the complex dependence among process variables rather than perform dimensionality reduction or other decoupling processes. For a multimode chemical process, the C-vine copula model of each mode is initially created, in which a multivariate optimization problem is simplified as coping with a series of bivariate copulas listed in a sparse matrix. To measure the distance of the process data from each non-Gaussian mode, a generalized local probability (GLP) index is defined. Consequently, the generalized Bayesian inference-based probability (GBIP) index under a given control limit can be further calculated in real time via searching the density quantile table created offline. The validity and effectiveness of the proposed approach are illustrated using a numerical example and the Tennessee Eastman benchmark process. The results show that the proposed VCDD approach achieves good performance in both monitoring results and computation load.