转油站是油田油气集输系统的核心枢纽，对于维持油田稳定生产和油气持续供应至关重要。鉴于其工艺系统的复杂性以及故障的多态性和故障关系的模糊不确定性，提出了融合T-S模糊故障树与贝叶斯网络（Bayesian network, BN）的系统可靠性评估方法。首先，基于T-S门及其描述规则建立T-S模糊故障树，并将其转化成贝叶斯网络模型；其次，结合有限的故障样本和通用数据源，基于贝叶斯更新估计确定基本事件故障率，以应对故障样本数据的不确定性；最后，协同运用T-S故障树和BN模型，正向推理预测工艺系统的可靠性和基本事件的贡献度，并反向诊断导致系统不同故障状态发生的关键致因。针对典型转油站工艺系统的应用研究表明，本文方法能够在基础数据和事件关系不确定性条件下实现系统故障率预测和薄弱环节诊断，从而为复杂油气工艺系统优化设计和可靠性维护提供决策支持。

Oil transfer station plays a crucial role in the oil and gas gathering and transportation system of an oilfield, ensuring stable production and continuous supply of oil and gas. However, given the complexity of its process system and the ambiguous uncertainty surrounding fault modes and relationships, a systematic reliability assessment method integrating T-S fuzzy fault trees with BNs(Bayesian networks) was proposed. Firstly, a T-S fuzzy fault tree was established based on T-S gates and their descriptive rules, which is subsequently converted into a Bayesian network model. Secondly, leveraging limited fault samples and general data sources, Bayesian updating estimation was employed to determine the failure rates of basic events, addressing the uncertainty inherent in fault sample data. Lastly, the T-S fault tree and BN model were synergistically utilized for forward reasoning to predict the reliability of the process system and the contribution of basic events, while reverse diagnosis is conducted to pinpoint the key factors causing different fault states of the system. Research conducted on typical oil transfer station process systems has demonstrated that the proposed method can effectively predict system failure rates and diagnose weak links even under conditions of uncertainty in basic data and event relationships. This provides crucial decision support for the optimal design and reliability maintenance of complex oil and gas process systems.