Graph Attention Network with Mutual k-Nearest Neighbor Strategy for Predictive Maintenance in Nuclear Power Plants

This study presents a graph-based framework for improving predictive maintenance in nuclear power plants (NPPs), integrating data balancing techniques with a proposed Graph Attention Network (GAT) with a Mutual k-Nearest Neighbor (Mk-NN) strategy, named GAT-Mk-NN. To enhance the system’s ability to discriminate between genuine faults and sensor anomalies, we introduce a novel procedure for generating synthetic false positives that simulate realistic sensor failures. To mitigate class imbalance, we employ structured oversampling and multiple synthetic data generation strategies. Our results demonstrate that our GAT-Mk-NN model achieves the best trade-off between accuracy and computational efficiency, reaching an F1-score of 0.882 and an accuracy of 0.884. Performance analysis reveals that low to moderate graph connectivity enhances both robustness and model generalization. Our GAT-Mk-NN model structure outperformed other state-of-the-art graph architectures (enhanced GCN, GraphSAGE, GIN, graph transformer, ChebNet, TAG, ARMA graph, simple GCN, GATv2, and hybrid GNN). The findings highlight the potential of graph-based learning for fault detection in sensor-dense industrial environments, offering actionable insights for deploying fault-tolerant diagnostics in critical systems.