A Fault Diagnosis Framework for Pressurized Water Reactor Nuclear Power Plants Based on an Improved Deep Subdomain Adaptation Network - Zhaohui Liu; Enhong Hu; Hua Liu (Energies)
Fault diagnosis in pressurized water reactor nuclear power plants faces the challenges of limited labeled data and severe class imbalance, particularly under Design Basis Accident (DBA) conditions. To address these issues, this study proposes a novel framework integrating three key stages: (1) feature selection via a signed directed graph to identify key parameters within datasets; (2) temporal feature encoding using Gramian Angular Difference Field (GADF) imaging; and (3) an improved Deep Subdomain Adaptation Network (DSAN) using weighted Focal Loss and confidence-based pseudo-label calibration. The improved DSAN uses the Hadamard product to achieve feature fusion of ResNet-50 outputs from multiple GADF images, and then aligns both global and class-wise subdomains. Experimental results show that, on the transfer task from the NPPAD source set to the PcTran-simulated AP-1000 target set across five DBA scenarios, the framework raises the overall accuracy from 72.5% to 80.5%, increases macro-F1 to 0.75 and AUC-ROC to 0.84, and improves average minority-class recall to 74.5%, outperforming the original DSAN and four baselines by explicitly prioritizing minority-class samples and mitigating pseudo-label noise. However, our evaluation is confined to simulated data, and validating the framework on actual plant operational logs will be addressed in future work.