In the system reliability evaluation of the process industries, it is sometimes difficult to get precise and sufficient failure data of system components utilized to calculate the failure probability. In this study, a Noisy-OR gate Bayesian network method based on intuitionistic fuzzy theory is proposed in cases of imprecise and insufficient historical data. The main contributes of this method include: a set of triangular intuitionistic fuzzy numbers considering uncertainty and hesitation is defined based on the standards and industry practices, meanwhile, a corresponding probability conversion method is also proposed; an improved similarity aggregation method is employed for less uncertainty accumulation and reducing the deviation caused by individual differences during the aggregation; the uncertain causal relationship among the relevant nodes is determined by applying the Noisy-OR gate in the Bayesian network. Furthermore, a case study of the crude oil tank fire and explosion accident is performed to illustrate the applicability of proposed approach. The comparison between the obtained results and that from pre-existing methods shows that the proposed method can provide a more suitable result in an uncertain environment. The weak links of the crude oil tank system are identified through Bayesian reasoning and sensitivity analysis, which can aid decision-making and improve the security execution of the crude oil tank system.

在过程工业的系统可靠性评估中，有时难以获得用于计算故障概率的准确且足够的系统组件故障数据。本研究提出了一种基于直觉模糊理论的Noisy-OR门贝叶斯网络方法，用于不精确和历史数据不足的情况。该方法的主要贡献包括：根据标准和行业惯例，定义了一组考虑不确定性和犹豫性的三角直觉模糊数，同时提出了一种相应的概率转换方法。采用改进的相似度聚合方法，减少了不确定性的积累，减少了聚合过程中个体差异引起的偏差。相关节点之间的不确定因果关系是通过在贝叶斯网络中应用Noisy-OR门来确定的。此外，对原油罐起火和爆炸事故进行了案例研究，以说明所提出方法的适用性。所得结果与已有方法的比较表明，所提出的方法可以在不确定的环境下提供更合适的结果。通过贝叶斯推理和敏感性分析，可以识别出原油储罐系统的薄弱环节，有助于决策，提高原油储罐系统的安全性。以原油罐火灾爆炸事故为例，说明了该方法的适用性。所得结果与已有方法的比较表明，所提出的方法可以在不确定的环境下提供更合适的结果。通过贝叶斯推理和敏感性分析，可以识别出原油储罐系统的薄弱环节，有助于决策，提高原油储罐系统的安全性。以原油罐火灾爆炸事故为例，说明了该方法的适用性。所得结果与已有方法的比较表明，所提出的方法可以在不确定的环境下提供更合适的结果。通过贝叶斯推理和敏感性分析，可以识别出原油储罐系统的薄弱环节，有助于决策，提高原油储罐系统的安全性。