Monte Carlo-informed reliability and availability framework for innovative inspection timelines: a case study of the PA01-BC01 heat exchanger

In nuclear facilities, predictive maintenance is essential to sustain safety, reliability, and availability, particularly for ageing components that are sensitive to downtime. Conventional strategies often rely on reliability indices or fixed timelines, which may not adequately reflect operational disturbances and uncertainty. To address this gap, this study proposes an inspection timeline based on a Monte Carlo-informed Reliability- and Availability-Centered Maintenance (RACM) framework. The framework integrates the Maintenance Priority Index (MPI) with key reliability and maintainability metrics: mean time between failures (MTBF), mean time to failure (MTTF), and mean time to repair (MTTR), to support data-informed decision making. The method is demonstrated on the PA01-BC01 heat exchanger; a 16.5 MW shell-and-tube unit in the GAS multi-purpose reactor’s secondary cooling system. Using Weibull-distributed failure data and Monte Carlo simulations, the component’s MTTF was 799.19 days, closely matching the analytical value of 800.78 days. With an MTTR of 3.84 days, the resulting MTBF was 803.02 days, confirming low failure frequency and high reliability. At a targeted reliability of 0.90, operational availability reached 0.995, underlining its critical role in reactor performance. By weighting reliability (w1 = 0.48) and availability (w2 = 0.52), the MPI-based approach produced innovative inspection timelines ({184, 276, 343} days) that balance downtime risk, labour, and cost more effectively than reliability-only or baseline timelines. This study demonstrates the integration of stochastic reliability and maintainability analyses with maintenance prioritization, providing direct impact for ageing nuclear systems and scalable applicability to other high-reliability industries.