Recently, a new fuzzy fault tree analysis (FFTA) has been developed to propagate and quantify the epistemic uncertainties occurring in qualitative data such as expert opinions or judgments. It is well known that the weakest triangular norm (Tw) based fuzzy arithmetic operations preserve the shape of the fuzzy numbers, provide more exact fuzzy results and effectively reduce uncertainty range. The objective of this paper is to develop a novel Tw-based fuzzy importance measure to identify the critical basic events in FFTA. The proposed approach has been demonstrated by applying it to a case study to identify the critical components of the Group 1 of the U.S. Combustion Engineering Reactor Protection System (CERPS). The obtained results are then compared to the results computed by the existing well-known importance measures of conventional as well as FFTA. The computed results confirm that the proposed Tw -based importance measure is feasible to identify the critical basic events in FFTA in more exact way.

中文翻译：

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燃烧工程反应堆保护系统模糊故障树分析的一种新的基于最弱t-范数的模糊重要性测度

最近，一种新的模糊故障树分析（FFTA）被开发出来，以传播和量化专家意见或判断等定性数据中出现的认知不确定性。众所周知，最弱的三角范数（Tw) 基于模糊算术运算保留了模糊数的形状，提供更精确的模糊结果并有效减少不确定性范围。本文的目的是开发一种新型 Tw-基于模糊重要性度量来识别FFTA中的关键基本事件。所提出的方法已通过将其应用于案例研究来确定美国燃烧工程反应堆保护系统 (CERPS) 第 1 组的关键组件而得到证明。然后将获得的结果与通过现有众所周知的常规以及 FFTA 重要性度量计算的结果进行比较。计算结果证实了所提出的 Tw基于重要性度量的方法可以更准确地识别 FFTA 中的关键基本事件。