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

Authors

Kanaka Raju Kalla, R Ramya Swetha, Mahesh K, Ijjada Ramesh, Ch V V Ramana, Sara Abdelwahab Ghorashi, Faten Khalid Karim, Samih M Mostafa

Published in

Journal of visualized experiments : JoVE. Issue 233. Jul 21, 2026. Epub Jul 21, 2026.

Abstract

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.

PMID:
42574503
Bibliographic data and abstract were imported from PubMed on 11 Aug 2026.

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