In quantitative fault tree analysis of a system, exact failure probability values of components are utilized to calculate the failure probability of the system. However, in many real world problems, it is problematic to get precise and sufficient failure data of system components due to insufficient or imprecise information about components, changing environment or new components. A methodology has already been developed by employing fuzzy set theory for the system reliability evaluation by utilizing qualitative failure data of system components when quantitative failure data of components are inaccessible or insufficient. This paper extends the concept of fuzzy set to intuitionistic fuzzy set and proposes a novel approach to evaluate system failure probability using intuitionistic fuzzy fault tree analysis with qualitative failure data of system components. The qualitative failure data such as expert opinions are collected as linguistic terms. These linguistic terms are then quantified by triangular intuitionistic fuzzy numbers in form of membership function and non-membership function. Additionally, a method is developed for combining the different opinions of experts. To illustrate the applicability of proposed approach, a case study of the crude oil tank fire and explosion accident is performed. The obtained results are very close to the results from pre-existing approaches which confirm that the proposed approach is a more realistic alternative for the study of system reliability in intuitionistic fuzzy environment when quantitative failure data of system components are not known. To help decision makers for improving the security execution of the crude oil tank system, importance measures including Fussell-Vesely importance and cut sets importance are also executed.

在系统的定量故障树分析中，利用组件的准确故障概率值来计算系统的故障概率。然而，在许多现实世界中的问题中，由于关于组件，变化的环境或新组件的信息不足或不精确，难以获得准确且足够的系统组件故障数据。当组件的定量故障数据无法访问或不足时，利用模糊集理论对系统可靠性进行评估，并利用系统组件的定性故障数据，已经开发出一种方法。本文将模糊集的概念扩展到直觉模糊集，并提出了一种基于直觉模糊故障树分析方法对系统组件定性故障数据进行评估的新方法。诸如专家意见之类的定性故障数据是作为语言术语收集的。然后用隶属函数和非隶属函数形式的三角直觉模糊数对这些语言术语进行量化。此外，还开发了一种将专家的不同意见相结合的方法。为了说明所提方法的适用性，以原油罐着火和爆炸事故为例进行了研究。所获得的结果与现有方法的结果非常接近，这证实了在不知道系统组件的定量故障数据的情况下，所提出的方法对于研究直觉模糊环境中的系统可靠性是一种更现实的选择。为了帮助决策者提高原油储罐系统的安全性，还执行了包括Fussell-Vesely重要性和割集重要性在内的重要措施。