A Drone-Driven X-Ray Image-Based Diagnosis of Wind Turbine Blades for Reliable Operation of Wind Turbine

In this paper, we propose methods for the diagnosis of down conductor cables installed inside blades for the reliable operation of wind turbines. Utilizing the two synchronized drones equipped with X-ray devices to take X-ray images inside the blades, we propose deep learning methods for diagnosis based on X-ray images. The first method is the unsupervised deblurring method for blurry X-ray images that are generated by the drones. The key idea of the proposed method is to apply an image sharpening process to the latent space of the trained model using only blurry X-ray images. Through experiments using the X-ray images of blades, we show that the proposed method makes the outline of the down conductor cable clearer. As the second method, we propose an object detection-based method to provide fast and accurate object detection as the diagnosis. Given an X-ray image, we also propose a method to determine drone flight direction to enable drones to follow the down conductor cable. Through experiments, we show that our method has good performance in object detection (i.e., mAP of 98.21%) and classification (i.e., AUC of 0.9), and drone flight direction determination (i.e., AUC of 0.99).