Work

Hybrid GNN–Transformer Architectures for Reliable Remaining Useful Life Prediction in Nuclear Power Plants

Year: 2026

Type: article

Abstract: Accurate prediction of the Remaining Useful Life (RUL) of nuclear power plant systems can support more informed maintenance strategies and help reduce unplanned outages, motivating continued research into reliable, physically grounded prognostic models. This study evaluates machine-learning-based prognostic models, focusing on their ability to learn degradation patterns directly from operational data. Baseline Feedforward Neural Networks (FNNs), Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), and a hybrid GNN–Transformer architecture are assessed using data generated from the ASHERAH dynamic Pressurized Water Reactor simulator, which incorporates realistic degradation mechanisms, including condenser fouling and pump head loss. The proposed hybrid model integrates graph-based representations of component interactions with Transformer-based temporal attention to capture both system-level dependencies and long-term degradation dynamics. Model performance is evaluated using error-based and tolerance-based metrics, including Prediction Within Bounds Accuracy (PWBA20%), alongside uncertainty calibration via Prediction Interval Coverage Probability (PICP), with uncertainty quantified through deep ensembles. Results show that baseline NNs exhibit limited predictive accuracy and poor uncertainty calibration, while models incorporating temporal modeling and system topology achieve substantial improvements. The GNN–Transformer attains the strongest performance, yielding the highest PWBA20% (87.3%) and significantly improved uncertainty calibration, with an average PICP of 90.0%. These findings demonstrate the effectiveness of topology-aware, attention-based architectures for robust and reliable RUL prediction in complex nuclear systems.

Source: Energies

DOI: 10.3390/en19143359

Authors: Davide Rotilio, Mattia Zanotelli, Lauren Bailey, Jamie Baalis Coble, Xingang Zhao

Institution: University of Tennessee at Knoxville

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137