# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 1 Introduction  
As the demand for renewable energy sources grows, wind power has emerged as an efficient and safe solution. This sector has experienced significant expansion in recent years, driven by governmental incentives, lower production costs, and advancements in turbine technology [1]. Within the United States, wind power accounted for 22% of new electricity capacity installed in 2022, reaching a total capacity of 90,000 turbines [2]. Globally, the trend is equally robust. Europe increased wind generation capacity by 18.3 GW in 2023 as part of their continued commitment to green energy and 2030 energy targets [3]. This rapid growth in wind energy production presents new challenges in maintenance and inspection, necessitating innovative solutions to sustain efficiency and reliability. Inspection and early preventive maintenance of damages become vital to ensure the safe production of wind energy. Throughout the lifetime of the turbine, the stresses and harsh operating environment lead to damages that, if not addressed, can result in total loss. In addition, blade repairs range from 3000 to 16,000 USD, depending on the severity and size of the damage, while total blade replacement requires significantly more capital [4]. Common damage to WTBs includes delamination, structural cracks, and erosion at the leading edge. These are commonly a result of external factors such as strong wind, rain, snow, salt, and temperature fluctuations [5]. Delamination and surface cracks are the result of adhesive joint failures, core debonding, or excess loads, while leading edge erosion is often the result of cutting through particulate matter over the lifetime of the turbine. Current WTB fault detection methods include sensor-based monitoring, Supervisory Control and Data Acquisition (SCADA) system analysis, and visual inspection [6]. Visual-based inspection is used to identify and monitor blade defects. Methods of visual inspection have included RGB imaging, thermal, and a fusion of these modes to allow auxiliary information to be present during inference [7]. In this area, deep learning has proved to be a pivotal technology in advancing this field [6]. Additionally, the integration ofdeep learning and drones allows for an autonomous solution to the inspection problem, which is proving to be a promising direction as turbines continue to scale [8]. Existing work in the detection of WTB faults typically addresses architectural changes to a single deep learning network to improve performance for a given task. While an important contribution, the comparison between distinct architectural designs, single and two-stage detectors, could be improved. This study investigates the performance of these architectural design patterns through YOLO and Mask R-CNN to determine which method achieves the best performance on turbine blade fault localization. The dataset used in this study was introduced in our previous classification research [9]. Building upon this, the dataset is annotated for object detection architectures, allowing for the model comparison presented here. Additionally, methods such as the Adaptive Kriging Damage Assessment (AK-DA) improve inspection efficiency and reliability, further aiding in maintenance and extending equipment lifespans [10]. The focus of this study is on the halted wind turbine case scenario, which allows for the inspection of the WTBs without rotation. The wind speed and direction for action wind turbines can cause drone destabilization, turbine yaw directional change, and spinning of wind turbine blades, as expected. This leads to blurry/unfocused aerial images, which makes it more challenging for the blade fault inspection problem. Although this is not the direction of our work here, there are recent works that explore CNN-BiLSTM networks for a predictive analysis of wind speeds [11] that can help better stabilize the drone while collecting images and pre-processing techniques to de-blur the aerial images [12]. These could be incorporated to allow inspection in the active turbine case scenario.