Bayesian Reliability of Gas Network Under Varying Incident Registration Criteria

Gas transmission pipeline network is of great importance to any country using natural gases in its various technological processes. However, the usefulness cannot overshadow the threat posed to people and property by the grid failures. In order to quantify the reliability of the grid, se veral widely recognized pipeline incident databases have been established. However, each database contains data about pipelines operated in remote geographical regions with varying soil types, under different incident registration criterion. For a longer time period even in single database, there is variation of these incident registration criteria. Therefore, analysis of an entire sample without regard to the incident criteria change raises suspicions about the validity of resulting inferences. Authors move beyond the qualitative pipeline incident database comparison and provide a methodology for quantitative integration of all available statistical information to improve gas pipeline network reliability evaluation. We develop a new model called Criteria‐dependent Poisson model, which takes into account various incident data collection criteria and extend it to the hierarchical (Bayesian) case when different databases with differing incident registration criteria can be joined in the same analysis. With the real data examples, we demonstrate the applicability of our method, which unfolds itself to be of great usefulness in reliability prediction. The Lithuanian pipeline network failure rate assessment shows the advantages of hierarchical structuring of Criteria‐dependent Poisson model in small sample problems.