An Intelligent Thermographic Framework for Automated Diagnosis and Health Monitoring of Photovoltaic Modules

Reliable automated monitoring of photovoltaic modules is essential for improving energy efficiency, operational safety, and predictive maintenance. Infrared thermography is one of the most effective solutions for identifying localised thermal anomalies, such as hotspots, micro-cracks, connection faults, shading effects and other conditions of degradation that can compromise the performance of the photovoltaic system. The interpretation of thermographic images is still frequently reliant on the operator’s experience or on automated procedures based exclusively on image processing techniques or artificial intelligence models often regarded as black-box models, thereby limiting their reliability, robustness and interpretability. This study presents an integrated diagnostic framework combining infrared thermography, computer vision, and artificial intelligence for the automated diagnosis and health monitoring of photovoltaic modules operating under real-world conditions. The proposed methodology extends beyond hotspot detection by integrating thermal image preprocessing, anomaly detection and segmentation, extraction of thermal and geometric descriptors, and intelligent fault classification. The resulting diagnostic information enables automated fault-type classification and quantitative severity assessment, providing interpretable condition indicators for photovoltaic module monitoring. The methodology was validated using a database comprising 1560 thermographic images acquired from photovoltaic modules under representative operating conditions. The experimental evaluation demonstrated an overall classification accuracy of 97.6%, an F1-score of 97.0%, and an area under the ROC curve (AUC) of 0.991 for the fault-type classification task. The proposed framework therefore provides an interpretable and computationally efficient decision-support methodology for photovoltaic condition assessment, while its integration into longitudinal predictive-maintenance systems remains a subject for future investigation.