超声波传感器
鉴定(生物学)
涡轮机
涡轮叶片
超声波检测
声学
工程类
海洋工程
机械工程
计算机科学
材料科学
物理
植物
生物
作者
X Zhu,Zhigang Guo,Qing Zhou,Chunxiang Zhu,Tao Liu,Binrui Wang
标识
DOI:10.1080/10589759.2024.2324373
摘要
Ultrasonic testing (UT) is an effective method for detecting internal damage in wind turbine blades. However, manual interpretation is time-consuming and subjective. To automate UT in the wind turbine industry, an image detection model based on deep learning called UCD-YOLO (Ultrasonic C-scan image Detection You Only Look Once) is proposed to detect internal damage such as stratified and lack of glue in wind turbine blades. In order to enhance the feature extraction ability under complex damage forms, the use of deformable convolutional networks and context augmentation module are proposed. Designing a lightweight cross stage partial spatial pyramid pool fast (LCSP_SPPF) module, which allows the model to learn more information and improve detection accuracy. The introduction of wise intersection over union (WIoU) improves the convergence speed while reducing the regression error. Using UT devices to collect damage images of wind turbine blades and conducting comprehensive evaluation experiments on the detection performance of the proposed method. Results show that compared to the YOLOv5, UCD-YOLO is not only lighter but also achieves a 7.5% increase in mean average precision (mAP50). And compared with other detection methods, further verifying that the proposed method can quickly and effectively detect wind turbine blade internal damage, improving power generation efficiency.
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