智能系统学报Issue(3)：354-361,8.DOI:10.3969/j.issn.1673-4785.201503012
动态不确定因果图在化工系统动态故障诊断中的应用
Application of dynamic uncertain causality graph to dynamic fault diagnosis in chemical processes
摘要
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 ( DUCG) is a method, which represents and infers the dy⁃namic, uncertain causalities of the process system according to directed graph. Based on the characteristics of pro⁃cessing information, DUCG has its own advantages for fault diagnosis in chemical processes on a large scale. There⁃fore, 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 methodol⁃ogy and software. 54 variables and 114 causalities are included in the constructed DUCG knowledge model. Accord⁃ing to this model, all the failures simulated by TE are diagnosed in a high probability of ranking. The correct diag⁃nosis rate is 100%. In comparison of Bayesian Network ( BN) , the mean correct diagnosis rate is 79.71% reported⁃ly, showing that DUCG is an effective method.关键词
化工过程/动态不确定因果图/故障诊断/TE过程/概率推理Key words
chemical process/dynamic uncertain causality graph/fault diagnosis/Tennessee Eastman ( TE ) process/probabilistic reasoning分类
信息技术与安全科学引用本文复制引用
曲彦光,张勤,朱群雄..动态不确定因果图在化工系统动态故障诊断中的应用[J].智能系统学报,2015,(3):354-361,8.基金项目
国家自然科学基金资助项目（61273330；61473026）． （）
