Wind turbine blade surface inspection based on deep learning and UAV-taken images

Abstract

As a key component of wind turbines (WTs), the blade conditions are related to the WT normal operation and the WT blade inspection is a significant task. Most studies of WT blade inspection focus attention on acquired sensor signal processing; however, there exist problems of stability, sensor installation, and data storage and processing. Onsite visual surface inspection is still the most common inspection method, but it is inefficient and requires a long downtime. Aimed at solving the above issues, a novel blade inspection method based on deep learning and unmanned aerial vehicles is proposed. Since common defect types are visible, the inspection problem is regarded as an image recognition problem. Three convolutional neural networks are trained by using the constructed dataset for image recognition, and the F1-score is applied to evaluate the models. The VGG-11 model is chosen for the final model due to its best performance. Then, the alternating direction method of multipliers algorithm is employed to compress the model to reduce the requirements on hardware devices. The blind area of the WT can be reduced, the efficiency of subsequent maintenance can be improved, maintenance costs can be reduced, and the economic performance can be increased. Finally, a comparison experiment of different inspection methods is carried out to demonstrate the proposed advantages.