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.