Risk evolution of crude oil pipeline under periodic maintenance based on dynamic bayesian network

Pipeline failure can result in a chain reaction of accidents, including oil and gas leaks. Traditional risk analysis methods fail to fully capture the degradation and imperfect maintenance processes of multi-state systems. The model utilizes a hybrid failure rate recursive rule to accurately depict the changes in system reliability under varying maintenance intervals. The robustness of the model was validated through a case study of crude oil pipelines and the probability of pipeline failure under different maintenance strategies was predicted. The results suggest that semi-annual maintenance can significantly reduce the probability of pipeline failure compared to annual maintenance. If the equipment has experienced multiple instances of imperfect maintenance, it will accumulate excessive fatigue damage, leading to a gradual increase in the growth rate of the hazard rate function. Therefore, after 36 months, the advantage of semi-annual incomplete maintenance gradually diminishes, indicating that there is an upper limit to the number of equipment maintenance actions. To maximize failure risk reduction and minimize losses, a combined maintenance strategy (semi-annual and annual maintenance) should be implemented. Finally, through sensitivity and diagnostic analysis, key factors leading to pipeline failure were identified and appropriate measures to prevent pipeline failure were proposed.