Analysis of cascading probabilistic common cause failures

Common cause failures may contribute significantly to the reliability of a system and has attracted considerable research attentions. However, the existing models often assumed deterministic failures of components affected by the common cause (CC) and no cascading effects. This paper contributes by modeling cascading probabilistic common cause failures (CPCCFs), where a CC may cause multiple system components to fail with different probabilities and some components may fail in a domino chain manner. An explicit, analytical method is proposed for the reliability analysis of systems subject to CPCCFs, which may be triggered by external or internal CCs. The complex cascading effects are modeled using a directed acyclic graph structure. The proposed method has no limitation on the component time-to-failure distributions. Applications and advantages of the proposed method are illustrated through a detailed case study of a safety instrumented system for oil and gas transportation. Correctness of the proposed method is verified by Monte Carlo simulations. The time and space complexity of the method is also studied.

Automated summary

This paper studies common cause failures that can significantly affect the reliability of a system. It proposes a model for analyzing probabilistic common cause failures, where a common cause can lead to multiple system component failures with different probabilities, and some failures can occur in a cascading manner. The model uses a directed acyclic graph structure to capture complex cascading effects, and an explicit analytical method is proposed for reliability analysis. The method is not limited to specific component time-to-failure distributions. The application and advantages of the proposed method are demonstrated through a detailed case study of a safety instrumented system for oil and gas transportation. The correctness of the method is verified using Monte Carlo simulations, and the time and space complexity of the method are also studied.