Wind turbine blade damage identification using lightweight YOLO11 with multi-scale dilated attention
Year: 2025
Type: article
Abstract: In recent years, companies such as Siemens and GE have frequently experienced wind turbine shutdowns due to blade damage and fractures, resulting in annual losses of up to $4.6 billion. As a critical component of wind turbines, blades have a high failure rate, which directly affects the safety and reliability of wind turbine operations. However, accurately identifying wind turbine blade damage is a significant challenge, mainly due to the scarcity of damage samples, the small scale of blade damage which is difficult to detect, and the interference from external environmental factors. To address these issues, this paper proposes a high-accuracy and low-consumption damage identification method using SlimNeck-structured YOLO11 with Multi-Scale Dilated Attention (SNMSDA-YOLO11). First, to overcome the issue of limited damage samples from wind turbines, three data augmentation methods are used, including random noise augmentation, image sharpening augmentation, and saturation adjustment augmentation. Second, to solve the problem of adaptive feature extraction for damage at various scales, the multi-scale dilated attention mechanism is integrated into the YOLO11 algorithm. This enhances its ability to extract damage features across different forms of blade damage, thereby improving detection performance. Finally, a slim-neck structure is employed to optimize the model, ensuring accurate damage identification while significantly improving the computational speed and efficiency. Experimental results demonstrate that the proposed SNMSDA-YOLO11 effectively utilizes attention mechanisms to learn multi-scale damage features of wind turbine blades, achieving an accuracy of 94.9 %. Compared to existing methods, this method holds significant potential for large-scale applications in wind turbine blade damage identification.
Source: Applied Soft Computing
Authors: Wenjing Xu, Jiachi Yao, Yanxue Wang, Chao Liu, Xinyu Liu, Dongxiang Jiang
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,730
Funders: National Natural Science Foundation of China, Wuhan University of Technology, Beijing University of Civil Engineering and Architecture, Guangxi Key Lab of Manufacturing System and Advanced Manufacturing Technology, Beijing Association for Science and Technology, National Key Research and Development Program of China
OpenAlex ID: W4414998917