文献类型：期刊文献

英文题名：Modeling and application of industrial process fault detection based on pruning vine copula

作者：Wan, Junxia[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：2019

卷号：184

起止页码：1

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616305519);WOS:【SCI-EXPANDED(收录号:WOS:000456903800001)】；

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

语种：英文

外文关键词：Dependence modeling; Vine copula; Tree pruning; Fault detection

摘要：Industrial processes are usually nonlinear multivariate stochastic systems. Describing the distribution characteristics of variables through a vine copula model can well define complex correlation information. However, modeling based on vine copula involves computational complexity. This study proposes a method to prune vine copula and introduces an indicator to conduct pruning process. This technique constructs a simplified model by removing the weakly correlated component from the copula structure without reducing the accuracy of model. Lastly, fault detection for industrial processes based on pruning vine copula is conducted based on a generalized local probability monitoring index. Experimental results of the monitoring process show that the method can reduce the time of modeling and improve the effect of fault detection.