Surveillance Interval Determination for RSG-GAS Components Using Maintenance Priority Index and Weibull–Monte Carlo Simulation: Monitoring of the KLD-BRV10 Generator Set

The RSG-GAS reactor depends on the reliability and timely maintenance of its critical components to ensure safe and continuous operation. This study proposes a data-driven framework for determining the surveillance intervals of selected critical components by integrating the maintenance priority index (MPI) with a Weibull–Monte Carlo simulation. The approach leverages key reliability and maintainability metrics such as mean time between failures (MTBF), Mean Time To Failure, and mean time to repair to establish surveillance intervals, with a focus on inspection timelines tailored to each component’s aging behavior. Moreover, a virtual monitoring approach, combining remote condition inspection with performance profiling, is applied to the KLD-BRV10 generator set as a representative case. The results indicate an MTBF of 171.16 days and a 44.2% improvement in thermal efficiency, with a targeted reliability of 0.9. Weighted factors (w1 = 0.26; w2 = 0.74) are used to support tailored reliability and availability considerations for strategic surveillance. The proposed approaches enhance predictive maintenance planning through virtual monitoring and data-driven decision making. The proposed approach, developed based on RSG-GAS components, offers a novel contribution by providing a flexible and data-driven framework that is expected to be adaptable to other critical components requiring systematic surveillance intervals.