文献类型：期刊文献

英文题名：Hierarchical Fault Root Cause Identification in Plant-Wide Processes Using Distributed Direct Causality Analysis

作者：Jiang, Qingchao[1];Wang, Wenjing[1];Chen, Shutian[1];Pan, Chunjian[2];Zhong, Weimin[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Shanghai Univ Elect Power, Coll Automat Engn, Shanghai 200090, Peoples R China

年份：2024

卷号：20

期号：3

起止页码：3232

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20233514640898);WOS:【SCI-EXPANDED(收录号:WOS:001077230900001)】；

基金：This work was supported in part by the National Natural Science Fund for Distinguished Young Scholars under Grant 61925305, in part by the National Natural Science Foundation of China under Grant 61973119, and in part by Shanghai Rising-Star Program under Grant 20QA1402600.

语种：英文

外文关键词：Causal analysis; distributed process monitoring; fault location; partial cross mapping (PCM); root cause analysis

摘要：Process monitoring and fault root cause analysis of industrial processes play a critical role to inform timely maintenance and ensure safe production. Existing distributed monitoring frameworks are able to determine the fault occurrence units, but often fall short on fault causality analysis for deeper insights. Therefore, it is necessary to conduct further analysis on variable correlations to locate the fault variable. Conventional fault root cause analysis methods, such as Granger causality and transfer entropy, ignore the distinction between indirect and direct causations between variables, resulting in unsatisfactory results of fault root cause analysis. Therefore, this article proposes a distributed process monitoring and fault root cause analysis framework via direct causality analysis based on partial cross mapping (PCM). First, fault units are located through distributed process monitoring. Then, PCM is used to locate the root cause variables hierarchically. This framework makes full use of the fault unit information obtained by a distributed monitoring method so that the number of variables for causality analysis is reduced, which increases computational efficiency as well as accuracy of the PCM method. The validity of the proposed framework is verified on the Tennessee-Eastman process and a wastewater treatment process.