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The oil pipeline network system (OPNS) is an essential part of the critical infrastructure networks (CINs), and is vulnerable to earthquakes. Assessing and enhancing the resilience of the OPNS can improve its capability to cope with earthquakes or to recover the system’s performance quickly after the disturbance. This study defines the concept of OPNS resilience in the resistive ability, the adaptive ability, and the recovery ability. Then, the quantitative resilience assessment model is established considering the earthquake intensities, the role of safety barriers, the time-variant reliability, and the importance coefficient of each subsystem via a Monte Carlo simulation. Combining the model with GIS technology, a new methodology to evaluate OPNS resilience is proposed, and the resilience partition technology platform is developed, which can visualize the results of the resilience assessment. Finally, a case study is implemented to demonstrate the developed methodology, and a discussion is provided to identify the sensitive variables. The proposed resilience methodology can provide a framework for the probabilistic resilience assessment of OPNS, and could be expanded to other lifeline network systems.

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Many dangerous chemicals are involved in the operation of fluid machinery, and the leakage of hazardous chemicals could trigger serious accidents, so the leakage probability prediction of fluid machinery and the effective maintenance before the equipment breakdowns are the critical factors in preventing accidents. A leakage probability prediction method based on a cloud model is proposed to aim at the standard fluid machinery with a frequent leakage probability in a chemical industry park. Due to the basic leakage probability of fluid machinery at home and abroad, the index system of leakage influencing factors is constructed and its influence degree is analysed. The modified typical fluid machinery leakage probability is then obtained using the cloud model to eliminate the subjectivity of quantitative results. The proposed method is utilized to estimate the leakage probability of Ethylene pipeline leakage of a pump in 1300# cracking gas compression area. The outcome 4.27 × 10−5 proves that this method can effectively solve the deviation problem in the leakage prediction process of domestic fluid machinery and guide the process of leakage probability prediction or accident prevention.