DOI: 10.58559/ijes.1936625 ISSN: 2717-7513

A comparative study of CNN and vision transformer architectures for fault detection in small wind turbine blades

Muhammet Fatih Aslan, Büşra Aslan, Selami Balcı
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Automated fault detection in wind turbine blades is critical for ensuring the reliability and operational efficiency of wind energy systems. This study presents a systematic comparative analysis of five state-of-the-art deep learning architectures for binary fault classification (healthy versus faulty) on the CAI-SWTB dataset, comprising 6,000 RGB images of small wind turbine blades. The evaluated architectures span three distinct design paradigms: a classical Convolutional Neural Network (CNN) (ResNet-50), a modern CNN (EfficientNetV2-S), and three Vision Transformer (ViT)-based models (ViT-B/16, Data-efficient Image Transformer (DeiT)-S/16, and Swin-Tiny). All models were trained using a two-stage transfer learning protocol with ImageNet-pretrained weights, employing the AdamW optimizer and a cosine annealing learning rate schedule. EfficientNetV2-S achieved the highest classification performance with 99.75% accuracy, followed by ResNet-50 at 99.42%. Among the transformer-based models, Swin-Tiny outperformed both ViT-B/16 (65.33%) and DeiT-S/16 (82.75%), achieving 88.25% accuracy. Grad-CAM analysis confirmed that the best-performing models correctly localize structural defect regions in blade images, supporting their interpretability and suitability for real-world inspection applications.

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