AI-based condition monitoring of photocouplers to enhance the maintenance strategy for digital reactor protection systems

A reactor protection system (RPS) is a core safety system for the stable operation of nuclear power plants (NPPs). Modern RPSs adopt digital platforms based on programmable logic controllers, offering enhanced reliability and maintainability compared with analog systems. Despite these advancements, current practices such as self-diagnosis functions, periodic operational tests, and scheduled maintenance remain limited in assessing component conditions during normal operation. To address this limitation, this study proposes an artificial intelligence (AI)-based condition-monitoring framework to improve the maintenance strategy for the internal electronic components of digital RPSs. The framework combines a rule-based model to ensure data integrity with an AI-based model to monitor component condition. The methodology was specifically applied to the photocoupler, a critical electronic component whose failure can significantly affect the digital RPS. To train and validate the AI-based model, a scenario dataset was generated using accelerated aging data for photocouplers to simulate the actual operating environment. In addition, a conceptual monitoring interface was designed to evaluate the practical applicability of this approach. The framework early detected the faults of photocouplers in RPS and accurately diagnosed their conditions. It is expected that the proposed framework can enable condition-based maintenance, reduce unnecessary inspections, and improve system availability.