文献类型：期刊文献

英文题名：Nonlinear and Non-Gaussian Process Monitoring Based on Simplified R-Vine Copula

作者：Zhou, Nan[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：2018

卷号：57

期号：22

起止页码：7566

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20182205245081);WOS:【SCI-EXPANDED(收录号:WOS:000434895300026)】；

基金：The authors of this paper appreciate the National Natural Science Foundation of China (under Project No. 21676086 and Project No. 21406064).

语种：英文

外文关键词：Gaussian noise (electronic) - Gaussian distribution - Distillation columns - Process control - Distillation

摘要：In the field of chemical process monitoring, the vine copula model provides a new idea for describing the interdependence between high-dimensional complex variables, and directly characterizes the correlation without dimensional reduction However, in actual industrial processes, the number of pair copulas to be optimized and the parameters to be estimated increase rapidly when the dimensionality of the variables is large This greatly increases the computational load and reduces the detection efficiency. In this paper, a fault diagnosis method based on a simplified R-vine (SRV) model is proposed. Without reducing the precision of the model significantly, the simplified level is set to reduce the complexity of the workload and calculations. The simplified level of an R-vine model is obtained by a Vuong test. Then, the generalized local probability (GLP) of the non-Gaussian state is constructed by using the theory of highest density region (HDR) and a density quantile table. The monitoring results of the Tennessee Eastman (TE) process and a real acetic acid dehydration distillation system show that the proposed SRV approach achieves good performance in monitoring results and computational load for chemical process fault monitoring.