A discrete-time Bayesian network approach for reliability analysis of dynamic systems with common cause failures

The dynamic and dependant behaviors are typical characteristics of modern complex systems, whose reliability is often improved through the design of multichannel parallel structures. The existence of common cause failures (CCFs) has a significant impact on system reliability. A reliability analysis model is proposed for dynamic systems with CCFs based on discrete-time Bayesian networks (DTBNs). The system operating time is dispersed into several time intervals, and the component failures are divided into independent and CCF states. Dynamic systems with cold and warm spare parts are examined to determine the modelling methodology and conditional probability tables (CPTs) of Bayesian network (BN) nodes. The reliability calculation is realised through the Bayesian inference mechanism. The model is applied to the CCF analysis and fault diagnosis of a digital safety-level distributed control system (DCS) of nuclear power plants (NPPs) to prove the effectiveness and feasibility of the method.

Automated summary

The study proposes a reliability analysis model for dynamic systems with common cause failures based on discrete-time Bayesian networks, which investigates the impact of common cause failures on system reliability and calculates reliability through a Bayesian inference mechanism. The model is applied to a digital safety-level distributed control system in nuclear power plants, demonstrating the effectiveness and feasibility of the method.