Explainable Artificial Intelligence for Thermal Condition Assessment of High-Voltage Power Equipment Using Infrared...

Infrared thermography is widely used for non-contact thermal monitoring of high-voltage power equipment, where abnormal temperature patterns may indicate developing faults or insulation degradation. However, purely data-driven deep learning models may produce temperature predictions that are not fully consistent with heat-transfer physics. This study investigates a physics-constrained convolutional neural network (CNN) framework for estimating spatial temperature fields from thermographic images. A diffusion-based Laplacian residual loss derived from the heat equation was incorporated to improve physical consistency in the predicted thermal fields. Experimental evaluation on the available dataset showed that the physics-constrained model achieved improved performance compared with the baseline CNN, with the best configuration obtaining an RMSE of 12.013 °C and a CAP R-squared value of 0.4646, indicating moderate predictive capability. A learnable thermal diffusivity parameter was also explored to improve interpretability, although it did not outperform the fixed-parameter formulation. In addition, a source-term-augmented model was evaluated, but it did not provide any further improvement for the snapshot-based thermographic data. Monte Carlo dropout was applied for uncertainty estimation, revealing higher predictive variance near hotspot boundaries and regions with steep thermal gradients. Overall, the findings suggest that physics-based regularization and uncertainty estimation can improve the physical coherence and interpretability of thermographic prediction models, although the results remain limited to the current dataset and validation setting.