A Few-Shot Wind Turbine Blade Surface Faults Detection via Enhanced Data Augmentation and Progressive Transfer Learning

Accurate and automated measurement of surface faults on wind turbine blades (WTBs) is essential for ensuring the structural integrity and operational safety of wind farms. Surface faults on WTBs not only shorten turbine lifespans but may also compromise the stability and accuracy of measurement and monitoring instruments in wind farms. Traditional measurement approaches demonstrated promising performance for WTB surface faults with a large amount of fault data, but acquiring such extensive data is often impractical in real industry situations. To overcome data scarcity and accuracy loss in fault detection, we propose an image augmentation and detection with adaptive learning framework (IADA-Net), consisting of a WTB image augmentation module and a surface fault detection module. The real blade image augmentation module designs an augmentation algorithm (HybridAug), which expands the blade faults dataset across varying acquisition scenarios and environments. The surface fault detection module (TransDetect) designs a detection algorithm based on transfer learning to address limited model generalization and diminished detection accuracy in few-shot scenarios. We conducted experiments on 1223 real images collected from China Longyuan Power’s wind farms. The experimental results show that the proposed HybridAug and TransDetect can improve the WTB fault detection accuracy by 2.4% and 18.6%, respectively. The IADA-Net achieves a detection accuracy of 96.7%, comparable to YOLO-based models, while reducing the training data requirement by 60%, which demonstrates our method offers a reliable solution for intelligent blade operation and maintenance systems.