The triethylene glycol (TEG) dehydration system is an integral component of the natural gas processing flow, and its proper functioning is essential for maintaining the efficiency and stability of the overall process. Any failure within this system can have substantial and detrimental effects on the entire natural gas processing operation. To overcome the limitations of traditional diagnostic methods, such as incomplete fault identification, lack of probabilistic quantification, and difficulty in tracing causal factors, this study proposes an advanced fault diagnosis approach based on fault tree analysis (FTA) and fuzzy Bayesian networks (FBN). Initially, a fault tree model is constructed utilizing process knowledge and expert insights to identify potential failure modes. Subsequently, the mapping relationship between the fault tree model and the Bayesian network is established to define the network's structure. To address the uncertainty inherent in expert judgments, fuzzy set theory is employed to derive the prior probabilities for basic events. The conditional probability tables of the Bayesian network are then determined through logic gates, completing the parameter learning phase. Forward inference of the Bayesian network reveals a current fault probability of 0.79, indicating a high likelihood of system failure, which necessitates enhanced operational and maintenance efforts. Additionally, backward inference pinpoints TEG foaming and improper human operations as the primary contributors to the elevated risk. Sensitivity analysis further identifies the most probable causal chains of events. In conclusion, the proposed fuzzy Bayesian network-based fault diagnosis approach provides a robust and accurate methodology for identifying critical fault factors and causal relationships, offering valuable support for accident prevention and informed maintenance decision-making. This approach holds significant practical implications for engineering operations and maintenance management.

fuzzy Bayesian network,fault tree,natural gas TEG dehydration system,fault diagnosis