# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 1 Introduction  
The gas distribution station (GDS) constitutes a vital component of the long-distance gas transmission network, pivotal in ensuring the stable and secure supply of natural gas to residential, commercial, and industrial consumers situated along the transmission pipeline. Typically, numerous such stations are deployed: for instance, China's West-Eastgas pipeline boasts 17 GDSs, while the Sichuan-East gas pipeline comprises 11. The multifaceted functions of these stations encompass gas impurity separation, pressure regulation, and flow metering, as well as the reception and dispatch of pipeline cleaner. An unintended release of natural gas from a GDS could have serious consequences for the surrounding populace, infrastructure, and the environment alike. Consequently, assessing the risk level of these stations and pinpointing their vulnerabilities is paramount. Such an assessment can serve as a cornerstone for devising risk mitigation strategies tailored to the unique needs of each station. The majority of previous studies have focused on safety assessment of gas transmission pipelines [1; 2; 3; 4; 5], while few studies are concentrated on quantitative risk assessment (QRA) of gas transmission stations, including GDSs [6; 7; 8]. Previous studies have primarily utilized fault tree analysis (FTA), event tree analysis (ETA), and bow-tie analysis (BTA) as QRA methodologies. However, the conventional approaches encounter limitations in capturing the dynamic nature of risk and handling data scarcity or uncertainties, in addition to difficulties in representing common cause failures and conditional dependencies among failure events. To overcome these constraints, a series of integrated methodologies have emerged. Approaches based on fuzzy set theory (FST), such as fuzzy-FTA [9; 10], fuzzy-ETA [11; 12], and fuzzy-BTA [13; 14], seamlessly integrate expert judgment with fuzzy sets, effectively addressing the uncertainties inherent in traditional analysis methods that stem from the scarcity of foundational failure data. In other integrated methodologies, such as SHIPP [15], Bayesian-FTA [16], and Bayesian-BTA [7; 17], the Bayesian updating mechanism is employed to minimize uncertainty and enhance the accuracy of quantifying failure probability and accident occurrence probability by utilizing real-time abnormal event data from the plant. More recently, Bayesian network (BN)-based techniques have gained popularity for probabilistic risk assessment (PRA) in various engineering fields [18; 19; 20; 21; 22; 23]. The integration of BN with fuzzy set theory (FST) offers distinct advantages: (1) fuzzy numbers systematically quantify epistemic uncertainties arising from limited data or expert judgment [24; 25], while (2) BN enables dynamic probability updating through real-time data assimilation, effectively addressing both static uncertainties and evolving risk scenarios [26; 27]. Despite these advancements, critical gaps persist when applying existing methodologies to complex systems in data-scar and operationally complex contexts, such as China's GDSs:  
(1) In data-scarce scenarios, existing frameworks assume access to sufficient failure data for probabilistic modeling. However, China's GDSs lack localized failure databases due to their recent deployment and fragmented data collection practices. Generic reliability data cannot fully capture site-specific risks, leading to biased predictions. (2) Traditional QRA tools (e.g., FTA, BTA) are limited to unidirectional reasoning--either predicting accident probabilities or diagnosing root causes. Few studies have explored bidirectional dynamic analysis, which is essential for systems requiring both proactive risk mitigation and reactive incident learning. (3) While BNs enable probability updating [22], their application in China's GDSs is hindered by the absence of systematic data integration frameworks. This gap limits the ability to dynamically refine risk profiles as new failure modes emerge.