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. Copyright © 2015 John Wiley & Sons, Ltd.

[1]  Georgios A. Papadakis,et al.  EU initiative on the control of major accident hazards arising from pipelines , 1999 .

[2]  Wenxing Zhou,et al.  Impact of dependent stochastic defect growth on system reliability of corroding pipelines , 2012 .

[3]  Y.-D. Jo,et al.  Analysis of hazard areas associated with high-pressure natural-gas pipelines , 2002 .

[4]  Young-Do Jo,et al.  A method of quantitative risk assessment for transmission pipeline carrying natural gas. , 2005, Journal of hazardous materials.

[5]  Tak Kuen Siu,et al.  Markov Chains: Models, Algorithms and Applications , 2006 .

[6]  Chiara Vianello,et al.  Quantitative risk assessment of the Italian gas distribution network , 2014 .

[7]  Georgios A. Papadakis,et al.  Major Hazard Pipelines: A Comparative Study of Onshore Transmission Accidents. , 1999 .

[8]  Stephen P. Brooks,et al.  Review of Markov chain Monte Carlo in practice by WR Gilks, S Richardson & DJ Spiegelhalter , 1997 .

[9]  Wenguo Weng,et al.  An integrated quantitative risk analysis method for natural gas pipeline network , 2010 .

[10]  Jian-Hua Li,et al.  Predicting corrosion remaining life of underground pipelines with a mechanically-based probabilistic model , 2009 .

[11]  G A Papadakis Assessment of requirements on safety management systems in EU regulations for the control of major hazard pipelines. , 2000, Journal of hazardous materials.

[12]  Reza Zanjirani Farahani,et al.  Risk management in gas networks: a survey , 2011 .

[13]  Gintautas Dundulis,et al.  Development of approach for reliability assessment of pipeline network systems , 2012 .

[14]  Wenxing Zhou,et al.  System reliability of corroding pipelines considering stochastic process-based models for defect growth and internal pressure , 2013 .

[15]  Kong Fah Tee,et al.  Application of subset simulation in reliability estimation of underground pipelines , 2014, Reliab. Eng. Syst. Saf..