Deep learning-based RUL prediction with uncertainty quantification for photocouplers in reactor protection systems

The reactor protection system (RPS), which is an integral component of the safety system in nuclear power plants, can pose a substantial risk to plant safety in the event of an unexpected failure. Although RPS integrity is currently evaluated via self-diagnostic functions, periodic tests, and overhaul procedures, these methods are limited in scope and are difficult to verify during normal operation. Therefore, this study proposes a deep learning-based approach for predicting potential RPS failures, even during normal operation. Accordingly, critical electronic components within the RPS were selected with a particular focus on predicting the remaining useful life of the photocoupler. A deep-learning-based prediction model was developed using data obtained from accelerated aging tests in conjunction with temperature change scenarios generated to reflect actual operating conditions. Specifically, a long short-term memory network integrated with Monte Carlo dropout was applied to estimate the remaining useful life and quantify the prediction uncertainty. In addition, the performance of the model was enhanced by incorporating physics-informed regularization into the loss function. The effectiveness and applicability of the proposed model were verified via performance comparisons with alternate models that utilize different deep learning methods and loss functions.