Predictive Modeling and Uncertainty Quantification in Condition Monitoring of Active Components: A Reactor Coolant Pump Use Case

This work develops data-driven models for predicting the onset of thermal barrier leakage in reac-tor
coolant pumps. It incorporates uncertainty quantification (UQ) to enhance the reliability and robustness of
predictions. Using synthetic data generated by the Generic Pressurized Water Reac-tor simulator, realistic
degradation scenarios were simulated across lifecycle stages—beginning, middle, and end of life. Key
variables, including differential pressure, flow rate, vibration, and temperatures, were analyzed using
machine learning framework. The fully connected neural net-work models demonstrated exceptional
performance, achieving R2 scores exceeding 0.99 and root mean square errors as low as around 8.23 × 10 −2 gallon per minute (gpm) for the three stages
of the lifecycle. UQ analysis further validated the model’s robustness, with narrow uncertainty bounds
during steady-state operations and appropriately wider bounds during transitional phases, reflecting the
physical behavior of the system. This work addresses important gaps in real-time condition monitoring
and regulatory compliance by integrating advanced condition monitoring technologies with UQ into IST
programs. The ability to detect thermal barrier leakage early and quantify prediction reliability supports
optimizing maintenance strategies while ensuring nuclear power plants’ safe and reliable operation.