# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 6 Conclusions and Discussion on Limitations  
With this rise of installed turbines comes a mounting need for improved maintenance and inspection techniques. In this research, two popular object detection architectures were deployed and compared for performance on the localization task of WTB faults. The YOLO family of architectures was compared with versions 5, 8, and 9 along with three varying parameter sizes to allow a comprehensive performance comparison. This study showed that the most recent version of YOLO, YOLOv9 with size c, provided the highest mAP50 and mAP50 with 0.849 and 0.539, respectively. In addition to the YOLO architectures, Mask R-CNN was investigated along with altered feature extraction layers for improved performance. Here, it was shown that the base ResNet50-FPN network provides adequate performance but can be improved through a reduction in layers and parameters with a ResNet18-FPN backbone. This alteration provided a higher mAP50 of 0.8415 and instance segmentation mAP50 of 0.7933 while contributing a computation decrease through a 32-layer backbone reduction. While the scores obtained by the modified mask R-CNN did not surpass the latest YOLOv9 architecture results, they did provide a greater level of detail with instance segmentation masks. The comparative study on WTB fault localization obtained promising results. This enhanced precision can be leveraged to improve inspection accuracy and throughput as increased demand drives turbine installation numbers. While this study provides promising results on the identification and localization of wind turbine blade faults, there remain challenges to be addressed. The dataset utilized in this study was created in a controlled environment simulating the halted wind turbine case scenario, which is the current trend of inspection. However, the loss of power production is a constraint to this method. If automated methods can address the collection of images while the wind turbine is active, it can eliminate this loss of power. Here, the rotation of the blades, along with the yaw of the turbine due to the environment wind direction and speed, can cause blurry aerial images taken with the drone, resulting in poor image quality. The drone itself can experience some turbulence causing unfocused imaging. To tackle these issues, more wind-resistant drones with better camera payloads can be a solution at an additional cost. As an alternative, applying de-blurring methods such as Lucy-Richardson or Wiener filter methods can be used as a pre-processing stage to mitigate the blurs of the captured images before feeding the images to the deep learning stage for blade fault identification and localization [12]. Another solution includes mounting an anemometer such as TriSonica Mini Ultrasonic Anemometer to obtain the real-time wind speed and direction data to better control the drone for further stabilization as it takes aerial images from the blades. This approach would allow the drone to collect more focused images of halted turbine blades in the harsh environments presented in onshore and offshore wind farms. As an alternative, CNN-based wind forecasting approaches can also be used for drone stabilization and path-planning [11]. Finally, to allow for a more generic solution for inference on commercial WTBs, more publicly available datasets are required. While the presented dataset is limited in scope and size, the goal was to increase the availability of wind turbine fault datasets through open access. Furthermore, the dataset leveraged in this study is limited to three major fault types, i.e., erosion, holes, and cracks. While these are some of the most common faults, it is not a comprehensive list seen in operational wind turbines. Further improving the scope of faults will allow even greater levels of generalization for fault detection. Conceptualization, M.S. ; methodology, M.S., M.D. and E.N.D. ; software, M.D. and E.N.D. ; validation, M.S., M.D., E.N.D. and M.M. ; formal analysis, M.S., M.D., E.N.D., J.Z. and M.M. ; investigation, M.S., M.D., E.N.D., J.Z.