文献类型：期刊文献

英文题名：Process Monitoring and Fault Diagnosis Based on a Regular Vine and Bayesian Network

作者：Jia, Qiong[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：59

期号：26

起止页码：12144

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20202908954428);WOS:【SSCI(收录号:WOS:000547326800024)，SCI-EXPANDED(收录号:WOS:000547326800024)】；

基金：The authors of this paper appreciate the support from the National Natural Science Foundation of China (under project no. 21676086).

语种：英文

外文关键词：Statistical tests - Fault detection - Failure analysis - Process control - Numerical methods - Process monitoring

摘要：This paper proposes a process monitoring and fault diagnosis method based on a regular vine (R vine) and Bayesian network. The R vine model structure is determined by searching for the maximum sum of combinations of correlations among variables, which makes the model more robust and able to describe data more flexibly. A double-space strategy based on the R vine is used to detect the process fault, which can improve the ability to detect weak faults. Furthermore, a Bayesian network is built according to the first tree of the R vine model to diagnose the detected fault and find the root cause. The causality between the nodes of the Bayesian network is determined via the Granger test. The effectiveness of the proposed method is verified by numerical examples and industrial examples.