DUCG在核电站故障诊断中的推理机设计与实现

DUCG Reasoning Machine Design and Implementation in Nuclear Power Plant Fault Diagnosis

  • 摘要: 核电站一旦发生故障,将导致不可估量的严重后果,对核电站进行参数检测、故障预报、诊断及发展预测具有重大意义。DUCG(动态不确定因果图)理论模型以图形化方式简洁表达了任何情况下不确定因果关系,并基于证据化简图形和展开事件,以得到所关注的假设事件及其状态概率表达。本文以DUCG理论模型为基础,进行核电站故障诊断系统的推理机软件实现及算法优化,重点介绍DUCG展开、化简及表达式运算的推理程序实现过程,该推理机软件系统的实现可用于进一步的理论模型研究。

     

    Abstract: The incalculable serious consequences may be caused by nuclear power plant accident. Therefore the parameters evaluation, fault forecast and diagnosis in nuclear power plant are of great significance. DUCG (dynamical uncertainty causality graph) is able to compactly and graphically represent uncertainty causality in any cases, simplify graphics, outspread event based on evidence observed, and calculate the updated probabilities of the queries still in concern. This paper discusses the DUCG algorithm implementation and optimization in nuclear power plant fault diagnosis, especially the software realization of DUCG simplification and expression calculation. The realization of DUCG fault diagnosis reasoning machine can be used for further theoretical studies and algorithm analysis.

     

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