文献类型：期刊文献

英文题名：Mixture of D-Vine copulas for chemical process monitoring

作者：Zheng, Wenjing[1];Ren, Xiang[1];Zhou, Nan[1];Jiang, Da[1];Li, Shaojun[1]

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

年份：2017

卷号：169

起止页码：19

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20240915629564);WOS:【SCI-EXPANDED(收录号:WOS:000413127100003)】；

基金：The authors of this paper appreciate the National Natural Science Foundation of China (under Project No. 21676086 and No. 21406064) and the Fundamental Research Funds for the Central Universities under Grant 222201717006 for their financial support.

语种：英文

外文关键词：Multivariate data; Dependence structures; D-vine copula; Fault detection

摘要：Complex dependence structures often exist in chemical process multivariate data. Although they are difficult to capture, vine copula shows good performance and stronger flexibility in depicting the highly nonlinear dependencies. To identify and fully understand the complex dependence patterns in multivariate data, a mixture of D-vine copulas (MDVC) is proposed. By using the expectation-maximization (EM) algorithm and stepwise semi parametric (SSP) estimation for parameter estimation, the proposed model can depict the complex dependence structures in multivariate data. The highest density region (HDR) and the density quantile approach (DQA) are both used to construct the highest density region distance-based probability (HDRP) index to achieve a real-time process fault monitoring. The effectiveness and benefits of the proposed model are illustrated with a numerical example, the Tennessee Eastman (TE) benchmark process and a real acetic acid dehydration distillation system for fault detection. The results show that the proposed mixture of D-vine copulas can achieve good performance in chemical process fault monitoring.