Combining Statistical Models and AI for Predictive Maintenance: RUL Estimation of Reactor Protection System Components
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Abstract
Reliable operation of digital instrumentation in nuclear power plants depends heavily on accurate prediction of component degradation. This study proposes a hybrid framework for estimating the remaining useful life of photo-couplers used in reactor protection systems. Accelerated aging tests were performed under elevated thermal conditions to generate representative degradation data. Both statistical models and a neural network were developed to analyze long-term performance decline.
The AI model incorporates polynomial features and custom loss functions to reflect realistic monotonic and exponential degradation behavior. Its predictions closely matched those of the statistical models, with projected lifespans ranging from 22 to 24 years. A user-oriented software tool was also implemented to support real-time remaining useful life forecasting using field data, demonstrating the practical value of combining traditional and AI-based approaches for predictive maintenance in nuclear systems.
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Nuclear Power Plant Instrumentation and Control, Statistical Degradation Analysis, AI-Based Remaining Useful Life Prediction, Accelerated Aging Test, Maintenance Decision Support Software
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