对比CNN与视觉变换器在热成像光伏故障检测中的表现，用物理可解释性验证模型决策。
Benchmarking Vision Transformers and CNNs for Thermal Photovoltaic Fault Detection with Explainable AI Validation
- 比较ResNet、EfficientNet与ViT、Swin在热成像故障检测中的性能与可解释性。
- Swin-Tiny达94%二分类准确率，多类识别73%，电气故障F1超0.90，但积尘等环境因素仅0.20-0.33。
- 通过XRAI分析证明模型学习到真实热物理特征，适合能源监测领域可信AI部署。
人工智能在光伏（PV）自动化监控中的应用面临可解释性障碍，限制了其在能源基础设施中的落地。尽管深度学习在热成像故障检测中表现优异，但缺乏对模型决策是否符合热物理原理的验证，导致实际部署时因无法理解推理过程而犹豫。本研究首次系统比较了卷积神经网络（ResNet-18、EfficientNet-B0）与视觉变换器（ViT-Tiny、Swin-Tiny）在热成像光伏故障检测中的表现，并采用XRAI显著性分析验证其与热物理原则的一致性。在涵盖正常运行及11类故障的20,000张红外图像上评估显示，Swin Transformer表现最优（二分类准确率94%；多分类准确率73%）。XRAI分析揭示模型捕捉到局部热点（电池缺陷）、线性热路径（二极管故障）和热边界（植被遮挡）等物理意义明确的特征，与预期热信号一致。然而，不同故障类型间差异明显：电气故障检测能力强（F1>0.90），而积尘等环境因素仍具挑战性（F1 0.20–0.33），表明热成像分辨率存在局限。该热物理引导的可解释性方法为能源监控中的AI决策验证提供了可行路径，有助于克服可信赖性壁垒。
原文摘要 · Abstract (English)
Artificial intelligence deployment for automated photovoltaic (PV) monitoring faces interpretability barriers that limit adoption in energy infrastructure applications. While deep learning achieves high accuracy in thermal fault detection, validation that model decisions align with thermal physics principles remains lacking, creating deployment hesitancy where understanding model reasoning is critical. This study provides a systematic comparison of convolutional neural networks (ResNet-18, EfficientNet-B0) and vision transformers (ViT-Tiny, Swin-Tiny) for thermal PV fault detection, using XRAI saliency analysis to assess alignment with thermal physics principles. This represents the first systematic comparison of CNNs and vision transformers for thermal PV fault detection with physics-validated interpretability. Evaluation on 20,000 infrared images spanning normal operation and 11 fault categories shows that Swin Transformer achieves the highest performance (94% binary accuracy; 73% multiclass accuracy) compared to CNN approaches. XRAI analysis reveals that models learn physically meaningful features, such as localized hotspots for cell defects, linear thermal paths for diode failures, and thermal boundaries for vegetation shading, consistent with expected thermal signatures. However, performance varies significantly across fault types: electrical faults achieve strong detection (F1-scores >0.90) while environmental factors like soiling remain challenging (F1-scores 0.20-0.33), indicating limitations imposed by thermal imaging resolution. The thermal physics-guided interpretability approach provides methodology for validating AI decision-making in energy monitoring applications, addressing deployment barriers in renewable energy infrastructure.
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