Development of a Bayesian belief network model for software reliability quantification of digital protection systems in nuclear power plants

Abstract As the instrumentation and control (I&C) systems in nuclear power plants (NPPs) have been replaced with digital-based systems, the need has emerged to not only establish a basis for incorporating software behavior into digital I&C system reliability models, but also to quantify the software reliability used in NPP digital protection systems. Therefore, a Bayesian belief network (BBN) model which estimates the number of faults in a software considering its software development life cycle (SDLC) is developed in this study. The model structure and parameters are established based on the information applicable to safety-related systems and expert elicitation. The evidence used in the model was collected from three stages of expert elicitation. To assess the feasibility of using BBN in NPP digital protection software reliability quantification, the BBN model was applied to the Integrated Digital Protection System–Reactor Protection System and estimated the number of defects at each SDLC phase and further assessed the software failure probability. The developed BBN model can be employed to estimate the reliability of deployed safety-related NPP software and such results can be used to evaluate the quality of the digital I&C systems in addition to estimating the potential reactor risk due to software failure.

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