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System risk quantification and decision making support using functional modeling and dynamic Bayesian network

机译：使用功能建模和动态贝叶斯网络的系统风险量化和决策支持

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摘要

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. The MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.
机译：风险明智的决策需要对系统状况的概率评估控制行动成功的可能性。本文介绍了一种系统的状态转换建模方法，其使用具有功能建模的动态贝叶斯网络（DBN）与决策过程相结合的动态概率风险评估。使用具有多级流模型（MFM）技术设计的功能模型来构建由能量，质量，信息流推断的系统状态结构，使得可以验证开发的模型相对于系统功能。 MFM模型表示节点之间的因果关系，其捕获过程参数和控制单元的结构。每个节点可以具有多个可能的状态，并且由MFM模型结构的DBN表示定义状态之间的时域转换。 MFM-DBN集成状态转换建模是一种白盒方法，允许通过更新系统状态并通过物理推断支持概率的决策来绘制系统的风险概况。简单加热系统的示例已经用于说明该过程，包括基于定量风险概况的决策支持。为了证明其适用于复杂的系统运营决策，介绍了导致核电厂中冷却液事故密封丢失的驻地停电事故情景的案例研究。所提出的方法有效地为每个选项的时间提供了风险简介，以便操作员能够做出最佳决定，这最大限度地减少了植物风险。

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