Abstract
Safety-critical systems (SCSs) of nuclear power plants (NPPs) are being designed and developed to meet high dependability requirements. Fault tree analysis (FTA) is widely used for risk and reliability analysis of NPPs. However, fault trees (FTs) are static and have only limited capability to represent dynamic systems. FTA is also not capable of modeling non-binary logic and or modelling the system's evolution in time. Dynamic reliability methods are being developed to deal with such limitations. Time series Markov chains and dynamic flowgraph methodology are the dynamic reliability methods alternate to traditional FTA, which can be used for the system performance analysis. In this article, Time series Markov chains and DFM methods, for the system reliability predictions for the SCS of NPP are compared. The benefits of the proposed method are brought out to the traditional methods. The approach is applied on passive residual heat removal system of pressurized heavy water reactor under the station blackout scenario.
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