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Handling epistemic uncertainty in fault trees: New proposal based on evidence theory and Kleene Ternary decision diagrams

机译:处理故障树中的认知不确定性:基于证据理论和Kleene三元决策图的新建议

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Fault tree (FT) is the most used approach in reliability and safety studies. In most cases, the quantification of the FT top event is carried out either (i) without considering uncertainties associated with the basic events probability distribution parameters (assuming single-valued parameters) or (ii) using Monte Carlo analysis (MC) to account for that uncertainties (using probability density function (pdf)). However, MC approach may be inappropriate to characterize parameter uncertainties (epistemic uncertainty) for the case where the available data are poor. For that case, interval-valued information (supplied by experts) related to the considered parameters is more suitable than the MC approach. Within this framework, the present paper propose a new approach addressing epistemic uncertainty in FT based on coupling Dempster-Shafer Theory (DST, also known as Evidence Theory) and the Kleene Ternary Decision Diagrams (Kleene-TDDs). Indeed, DST is used to characterize epistemic uncertainty at basic events level, whereas Kleene-TDDs make it possible to propagate that uncertainty through the fault tree gates up to the top event.
机译:故障树(FT)是可靠性和安全性研究中最常用的方法。在大多数情况下,对FT最高事件的量化是(i)不考虑与基本事件概率分布参数相关的不确定性(假设单值参数),或者(ii)使用蒙特卡洛分析(MC)进行解释不确定性(使用概率密度函数(pdf))。但是,对于可用数据较差的情况,MC方法可能不适用于表征参数不确定性(流行病不确定性)。在这种情况下,与考虑的参数相关的区间值信息(由专家提供)比MC方法更合适。在此框架内,本文提出了一种基于耦合Dempster-Shafer理论(DST,也称为证据理论)和Kleene三元决策图(Kleene-TDDs)的解决FT认知不确定性的新方法。确实,DST用于在基本事件级别上表征认知不确定性,而Kleene-TDD则可以通过故障树门直至最高事件来传播不确定性。

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