Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.

定量风险评估（QRA）方法可系统地评估不良事件的可能性，影响和风险。使用故障树分析（FTA）的QRA基于以下假设：故障事件具有明确的概率，并且在统计上是独立的。通常不存在事件的明确概率，这导致数据不确定性。但是，独立性假设导致模型不确定。可以利用专家的知识来获取未知的故障数据；但是，此过程本身会遇到不同的问题，例如不精确，不完整和缺乏共识。因此，为了最大程度地减少QRA的总体不确定性，除了解决知识中的不确定性之外，将多位专家的意见合并并根据新证据更新先前的信念也同样重要。在本文中，通过将模糊集理论和证据理论与贝叶斯网络相结合，提出了一种用于QRA的新颖方法，以描述不确定性，聚集专家的意见并在有新证据时更新先验概率。此外，执行敏感性分析以识别FTA中最关键的事件。通过应用于实际系统已经证明了所提出的方法的有效性。