Discussion on the treatment of various dependencies in a time-dependent risk monitoring model of redundant systems

Abstract

This paper investigates the time-dependent modeling of the operating behaviors of systems in risk monitoring applications. The approach used focuses on the constructed concept of the risk monitoring time axis and on time-dependent failure probability modeling for dependencies based on this axis. Dependencies among the complex operating behaviors in nuclear power plants (NPPs) are often superimposed on each other. To ensure the safe and reliable operation of the system, a large number of redundant designs are used in NPPs. In this paper, we mainly focus on the dependencies that are common in redundant systems, such as state dependencies, sequential failures and common-cause failure (CCF). Studies are conducted on the rationality and applicability to the risk monitoring model of the model simplification assumptions for ignoring certain dependency problems in traditional static probabilistic safety assessment (PSA) modeling. Time-dependent mathematical models considering these dependencies in risk monitoring are proposed in this paper. Additionally, time-dependent mathematical models considering the superposition of these dependency problems in risk monitoring are developed, and the process of updating the above models with time and configuration changes is also studied in this paper to provide modelers with guidelines for representing such aspects in the risk monitoring application. Moreover, based on the above research results, a second-order redundant system is taken as an example to calculate and compare time-dependent system risk measurements of the model proposed in this paper and the time-independent system risk of the traditional risk monitoring model. These comparative studies demonstrate the effectiveness and benefits of the method proposed in this paper and provide some insightful conclusions. Only by accurately considering the time dependence and system dependencies can we obtain an accurate system risk and then make a reasonable and credible risk-informed decision.