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

TL;DR

This paper presents REVL-PV, a multimodal AI framework that links visual evidence to defect mechanisms for photovoltaic inspection, achieving high accuracy and interpretability in real-world defect classification.

Contribution

The introduction of REVL-PV, a reasoning-aware vision-language model that provides structured diagnostic reports and aligns with professional inspection practices.

Findings

Achieves 93% classification accuracy on real-world modules.

Produces interpretable diagnostic rationales aligned with expert assessments.

Maintains robustness under realistic image corruptions.

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.