贫数据条件下城市配气站事故风险预测与诊断方法研究文献综述

Research Literature Review on Accident Risk Prediction and Diagnosis Methods for Urban Gas Distribution Stations under Data-Sparse Conditions

摘要: 城市配气站作为天然气输配系统的关键节点，其安全运行对保障城市能源供应和公共安全至关重要。然而，由于其历史失效数据稀缺、运行工况复杂、致因因素耦合关联，传统的基于大量统计数据的安全风险评估方法在此“贫数据”条件下面临巨大挑战。本文系统梳理了国内外关于天然气设施(特别是配气站)风险预测与诊断的研究现状。首先，分析了配气站传统风险评价方法及其在贫数据条件下的局限性；其次，重点综述了基于贝叶斯网络、模糊理论、灰色系统理论等处理不确定性信息的方法在风险定量分析与诊断中的研究进展；再次，探讨了动态风险预测、情景构建与推演、安全屏障效用建模以及复杂网络风险传播在风险建模中的应用；复次，专门讨论了安全仪表系统(SIS)在贫数据下的安全完整性等级(SIL)评估这一特殊而重要的课题；最后，在综合述评的基础上，指出了现有研究的不足，并展望了贫数据条件下城市配气站事故风险预测与诊断的未来研究方向，特别强调了构建集成动态数据驱动与物理机理的智能预测诊断框架的必要性。

Abstract: Urban gas distribution stations, as critical nodes in the natural gas transmission and distribution system, are crucial for ensuring urban energy supply and public safety. However, their safety risk assessment faces significant challenges under “data-sparse” conditions due to scarce historical failure data, complex operating conditions, and coupled causative factors. Traditional safety risk assessment methods relying on extensive statistical data are thus difficult to apply effectively. This paper systematically reviews the domestic and international research status on risk prediction and diagnosis for natural gas facilities, particularly distribution stations. Firstly, it analyzes traditional risk assessment methods for distribution stations and their limitations under data-sparse conditions. Secondly, it focuses on reviewing the research progress of methods handling uncertain information—such as Bayesian networks, fuzzy theory, and grey system theory—in quantitative risk analysis and diagnosis. Thirdly, it explores the application of dynamic risk prediction, scenario construction and deduction, safety barrier effectiveness modeling, and complex network risk propagation in risk modeling. Furthermore, it specifically discusses the special and important topic of Safety Integrity Level (SIL) assessment for Safety Instrumented Systems (SIS) under data scarcity. Finally, based on a comprehensive review, the paper points out the shortcomings of existing research and prospects future research directions for accident risk prediction and diagnosis of urban distribution stations under data-sparse conditions, particularly emphasizing the necessity of constructing an intelligent prediction and diagnosis framework integrating dynamic data-driven approaches and physical mechanisms.