Modeling nuclear power plant piping reliability by coupling a human reliability analysis-based maintenance model with a physical degradation model

John Beal, Seyed Reihani, Tatsuya Sakurahara, Ernie Kee and Zahra Mohaghegh

Reliability Engineering and System Safety, 2025, vol. 255, issue C

Abstract: Reliability and availability analysis for repairable components, considering the underlying physical degradation and maintenance, is crucial in support of risk assessment and management. In nuclear power plants (NPPs), reactor coolant piping is a representative example of safety-critical repairable components that are subjected to long-term physical degradation interacting with maintenance activities. The existing methods for piping reliability analysis suffer from a limitation in their capability to analyze the time-dependent physics-maintenance interactions that could occur during the component lifetime and alter the underlying maintenance processes, for instance, an enhancement of maintenance programs based on condition monitoring data or an observed defect. To address this limitation, this paper develops a new piping reliability analysis methodology that couples a physics-of-failure (PoF) model with a maintenance performance analysis model. The contributions of this paper are two-fold: (i) developing a human reliability analysis (HRA)-based maintenance performance analysis model for NPP piping that can quantify maintenance outcomes under multiple types of maintenance programs, including time-based and condition-based preventive maintenance; and (ii) developing a computational methodology to couple the HRA-based maintenance performance analysis model with PoF models. The proposed physics-maintenance coupling methodology is applied to an NPP piping case study.

Keywords: Condition-based maintenance; Predictive maintenance; Physics-maintenance coupling; Nuclear Power Plants (NPPs); Piping reliability (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:255:y:2025:i:c:s0951832024007269

DOI: 10.1016/j.ress.2024.110655

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