From Prediction to Diagnosis: Reasoning-Aware AI for Photovoltaic Defect Inspection

2026-03-24

AI Summary

Current photovoltaic defect detection systems predominantly rely on black-box models lacking interpretability, which hinders their suitability for high-reliability operation and maintenance. This work proposes REVL-PV, a novel framework that integrates causal reasoning into the defect identification pipeline for the first time. By fusing electroluminescence, thermal, and visible-light images and incorporating domain knowledge to construct a vision–language reasoning chain, the method explicitly links visual evidence with underlying defect mechanisms prior to classification, thereby generating structured diagnostic reports. Evaluated on 1,927 real-world modules, REVL-PV achieves 93% classification accuracy, produces explanations highly consistent with those of certified experts, and demonstrates strong robustness under image degradation conditions, marking a paradigm shift from mere prediction to interpretable diagnosis.

Abstract

Reliable photovoltaic defect identification is essential for maintaining energy yield, ensuring warranty compliance, and enabling scalable inspection of rapidly expanding solar fleets. Although recent advances in computer vision have improved automated defect detection, most existing systems operate as opaque classifiers that provide limited diagnostic insight for high-stakes energy infrastructure. Here we introduce REVL-PV, a vision-language framework that embeds domain-specific diagnostic reasoning into multimodal learning across electroluminescence, thermal, and visible-light imagery. By requiring the model to link visual evidence to plausible defect mechanisms before classification, the framework produces structured diagnostic reports aligned with professional photovoltaic inspection practice. Evaluated on 1,927 real-world modules spanning eight defect categories, REVL-PV achieves 93\% classification accuracy while producing interpretable diagnostic rationales and maintaining strong robustness under realistic image corruptions. A blind concordance study with a certified solar inspection expert shows strong semantic alignment between model explanations and expert assessments across defect identification, root-cause attribution, and visual descriptions. These results demonstrate that reasoning-aware multimodal learning establishes a general paradigm for trustworthy AI-assisted inspection of photovoltaic energy infrastructure.

Problem

• photovoltaic defect inspection
• diagnostic reasoning
• interpretable AI
• multimodal learning
• trustworthy AI

Innovation

• reasoning-aware AI
• multimodal learning
• photovoltaic defect diagnosis
• interpretable AI
• vision-language framework