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

Summary

This study presents a physics-constrained CNN framework for infrared thermographic assessment of high-voltage power equipment, integrating diffusion-based regularization and Monte Carlo dropout for physically consistent temperature prediction and uncertainty-aware thermal monitoring.

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.

Introduction

Reliable thermal monitoring is essential for the safe operation of high-voltage power equipment, because abnormal heating is often an early indicator of insulation degradation, loose connections, or incipient electrical faults. Infrared thermography (IRT) is widely used in this context because it provides non-contact, full-field surface-temperature measurements during routine inspections. However, practical thermographic analysis remains difficult because of emissivity variation, ambient interference, sensor noise, and limited labeled data¹.

Deep learning has improved automatic hotspot detection and temperature-field....

Protocol

This section describes the complete physics-constrained deep learning pipeline for thermal condition assessment using infrared thermographic imagery. The framework integrates (i) temperature field extraction, (ii) baseline CNN regression, (iii) diffusion-based physics regularization, (iv) adaptive parameter learning, and (v) uncertainty quantification. All the tools used in this study are described in the Table of Materials.

Thermal image acquisition and temperature field construction
Infrared (IR) images of high-voltage power equipment were acquired using a calibrated thermographic camera. Each image w....

Results

This section evaluates the proposed physics-constrained deep learning framework using quantitative metrics and visual analysis. The results demonstrate how integrating diffusion-based physical constraints and uncertainty modeling influences predictive accuracy and thermal interpretability.

Performance comparison of model configurations
Table 1 shows the performance comparison of different model configurations. The baseline CNN achieved an RMSE of 12.345 °C.......

Discussion

The present study investigated the integration of physics-based regularization and uncertainty-aware deep learning for thermal condition assessment of high-voltage power equipment using infrared thermographic imagery. The results show that embedding physical priors into CNN-based temperature prediction improves the robustness and physical consistency of the learned thermal fields relative to purely data-driven approaches. One of the most important findings is the role of the diffusion-based physics constraint in shaping .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CUDA / cuDNN	NVIDIA	GPU acceleration for training	GTX 1080 Ti
Infrared thermographic camera	FLIR thermal imaging camera	Acquisition of high-voltage equipment thermograms	portable professional FLIR C5 Infrared camera at different times of the day and load conditions.
Jupyter Notebook / IDE	JupyterLab 4.2.0	Experiment development and analysis	Used in implementation
Matplotlib	Matplotlib Developers	Visualization of predictions, residual maps, and uncertainty maps	Used for figures and plots
NumPy	NumPy Developers	Numerical computing and array operations	Data preprocessing and tensor handling
OpenCV	OpenCV Foundation	Image loading, resizing, and basic preprocessing	Used for thermographic image handling
Python	Python Software Foundation	Main programming environment	Version used for all experiments
PyTorch	PyTorch Foundation	CNN training, physics-constrained learning, and Monte Carlo dropout	Used for implementation of the regression network
Scikit-learn	scikit-learn Developers	Metric computation and train/validation/test evaluation	Used for (R^2), RMSE, and MAE
SciPy	SciPy Developers	Supporting numerical routines	Used in preprocessing or analysis
Thermographic dataset	In-house field inspection dataset	Source of 174 infrared images for model development	Images organized in labeled folders
Workstation / GPU computing system	GTX 1080 Ti	Model training and inference	12 GB RAM,  GTX 1080 Ti , and Windows 11 OS