Component- and system-level degradation modeling of digital Instrumentation and Control systems based on a Multi-State Physics Modeling Approach - Wang, Wei; Di Maio, Francesco; Zio, Enrico (Annals of nuclear energy, 2016)
•A Multi-State Physics Modeling (MSPM) framework for reliability assessment is proposed.•Monte Carlo (MC) simulation is utilized to estimate the degradation state probability.•Due account is given to stochastic uncertainty and deterministic degradation progression.•The MSPM framework is applied to the reliability assessment of a digital I&C system.•Results are compared with the results obtained with a Markov Chain Model (MCM). A system-level degradation modeling is proposed for the reliability assessment of digital Instrumentation and Control (I&C) systems in Nuclear Power Plants (NPPs). At the component level, we focus on the reliability assessment of a Resistance Temperature Detector (RTD), which is an important digital I&C component used to guarantee the safe operation of NPPs. A Multi-State Physics Model (MSPM) is built to describe this component degradation progression towards failure and Monte Carlo (MC) simulation is used to estimate the probability of sojourn in any of the previously defined degradation states, by accounting for both stochastic and deterministic processes that affect the degradation progression. The MC simulation relies on an integrated modeling of stochastic processes with deterministic aging of components that results to be fundamental for estimating the joint cumulative probability distribution of finding the component in any of the possible degradation states. The results of the application of the proposed degradation model to a digital I&C system of literature are compared with the results obtained by a Markov Chain Model (MCM). The integrated stochastic-deterministic process here proposed to drive the MC simulation is viable to integrate component-level models into a system-level model that would consider inter-system or/and inter-component dependencies and uncertainties.