[1]QU Yanguang,ZHANG Qin,ZHU Qunxiong.Application of dynamic uncertain causality graph to dynamic fault diagnosis in chemical processes[J].CAAI Transactions on Intelligent Systems,2015,10(3):354-361.[doi:10.3969/j.issn.1673-4785.201503012]
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Application of dynamic uncertain causality graph to dynamic fault diagnosis in chemical processes

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 354-361 Column: 学术论文—智能系统 Public date: 2015-06-25
Title:
Application of dynamic uncertain causality graph to dynamic fault diagnosis in chemical processes
Author(s):
QU Yanguang1;  ZHANG Qin2;  ZHU Qunxiong1
1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2. School of Computer Science and Engineering, Beihang University, Beijing 100083, China
Keywords:
chemical process; dynamic uncertain causality graph; fault diagnosis; Tennessee Eastman (TE) process; probabilistic reasoning
CLC:
TP391.41
DOI:
10.3969/j.issn.1673-4785.201503012
Abstract:
In chemical processes, it is necessary to effectively diagnose the fault on time in order to avoid losses of economy and lives. Dynamic uncertain causality graph is a method, which represents and infers the dynamic, uncertain causalities of the process system according to directed graph. Based on the characteristics of processing information, DUCG has its own advantages for fault diagnosis in chemical processes on a large scale. Therefore, this article applies DUCG to realize fault diagnosis of chemical processes by constructing the object system knowledge base and probabilistic reasoning on fault data. The data transmission module of the former DUCG system is improved to deal with the vibrational signals in the chemical process, and to widen the scope of application. The Tennessee Eastman (TE) simulator is taken as the experimental subject to test the effectiveness of DUCG methodology and software. 54 variables and 114 causalities are included in the constructed DUCG knowledge model. According to this model, all the failures simulated by TE are diagnosed in a high probability of ranking. The correct diagnosis rate is 100%. In comparison of Bayesian Network (BN), the mean correct diagnosis rate is 79.71% reportedly, showing that DUCG is an effective method.
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