%0 Journal Article
%T 动态故障诊断中的立体因果建模与不确定性推理方法<br>Cubic causality modeling and uncertain inference method for dynamic fault diagnosis
%A 董春玲
%A 赵越
%A 张勤
%J 清华大学学报（自然科学版）
%D 2018
%R 10.16511/j.cnki.qhdxxb.2018.26.029
%X 为满足复杂系统的动态、实时和高可靠性的故障诊断需求，克服动态不确定因果图（dynamic uncertain causality graph，DUCG）及其他概率图模型的局限，该文在DUCG理论的基础上扩展其时序因果表达与推理方法，建立了立体DUCG （Cubic DUCG）理论模型。采用动态的手段处理动态问题，以"逐步生长"的立体因果建模取消了时序模型中常见的Markov假设限制，以穿越式因果连接准确地表达动态系统下故障的产生、演变和发展；直观地刻画和处理动态负反馈等复杂故障逻辑因果关系；给出了严谨、高效的动态推理算法。宁德核电站1号机组CPR1000模拟机二回路系统上的故障实验结果表明：Cubic DUCG诊断推理准确、高效，能有效处理负反馈等复杂动态情形。<br>Abstract：Complex systems need dynamic, real-time, reliable fault diagnostics but current methods have some shortcomings. This paper expands the dynamic uncertain causality graph method (DUCG) for temporal causality modeling and reasoning theory to correct the limits of the DUCG method and other probabilistic graphical models. A Cubic DUCG is developed that is characterized by a true dynamic model of dynamic problems. The cubic causality graph abandons the restriction of the Markov assumption usually used in temporal models with the fault formation, evolution, and development in dynamic systems represented by allowing causal connections to penetrate among any number of time-slices. The negative feedback dynamics is modelled intuitively combined with a reliable dynamic inference algorithm. Fault tests on the secondary loop of Ningde Nuclear Power Plant Unit 1 (CPR1000) simulator show that Cubic DUCG is accurate, efficient, and capable of dealing with the complex dynamics including negative feedback.
%K 故障诊断
%K 时序因果建模
%K 概率推理
%K 动态不确定性
%K 动态负反馈
%K &lt
%K br&gt
%K fault diagnosis
%K temporal causality modeling
%K probabilistic reasoning
%K dynamics and uncertainties
%K dynamic negative feedback
%U http://jst.tsinghuajournals.com/CN/Y2018/V58/I7/614