基于机器视觉的风力机叶片损伤检测系统
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TP391、TK83

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2020年内蒙古自治区科技计划攻关项目《风电机组叶片健康监测与维护关键技术研究》


The blade of wind turbine based on machine vision
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Research on Key Technologies of wind turbine blade health monitoring and maintenance, a key project of science and technology plan of Inner Mongolia Autonomous Region in 2020

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    摘要:

    针对风力机叶片表面出现的磨损等早期损伤特征现象,传统损伤检测方法存在高成本低效率等问题,设计了一种基于机器视觉和图像处理相结合的风力机叶片损伤检测系统。通过搭建机器视觉实验平台完成风力机损伤叶片图像采集和处理,通过使用HSV进行颜色平面提取,卷积运算、高亮显示操作滤波,选用自动阈值分割方法中最小均匀性度量法进行阈值分割处理,最后通过数学形态学去噪处理,腐蚀、膨胀、开运算等操作完成特征提取,设计了基于LabVIEW的风力机叶片智能图像识别系统,通过对图像处理后的损伤特征识别效果调试,完成性能测试。实验结果表明,基于该算法处理后的图像在设计的识别系统内准确识别率达到92.3%,并对裂纹损伤进行目标测量得到实际长度且绝对误差最大为3mm。该系统满足叶片检损的要求,实现对风力机叶片表面裂纹、轮廓磨损等损伤的图像处理和识别,并对损伤处进行标记、计数和测量,实现无损探伤,为兆瓦级风力机叶片损伤检测提供方法借鉴和图像处理、系统设计的技术支持。

    Abstract:

    Aiming at the early damage characteristics such as wear on the surface of wind turbine blades and the problems of high cost and low efficiency in traditional damage detection methods, a wind turbine blade damage detection system based on the combination of machine vision and image processing is designed. The machine vision experimental platform is built to complete the image acquisition and processing of the damaged blade of the wind turbine. The HSV is used for color plane extraction, convolution operation, highlight operation and filtering. The minimum uniformity measurement method in the automatic threshold segmentation method is selected for threshold segmentation. Finally, the mathematical morphology denoising is used to eliminate corrosion, expansion Open operation and other operations to complete feature extraction. An intelligent image recognition system of wind turbine blade based on LabVIEW is designed. Through the debugging of damage feature recognition effect after image processing, the performance test is completed. The experimental results show that the image processed based on the algorithm has an accurate recognition rate of 92.3% in the designed recognition system, and the actual length of crack damage is measured, and the maximum absolute error is 3mm. The system meets the requirements of blade damage detection, realizes the image processing and identification of wind turbine blade surface cracks, contour wear and other damage, marks, counts and measures the damage, and realizes non-destructive flaw detection, which provides method reference and technical support for image processing and system design for megawatt wind turbine blade damage detection.

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王一博,韩巧丽,张曦文,等. 基于机器视觉的风力机叶片损伤检测系统[J]. 科学技术与工程, 2022, 22(12): 4879-4886.
Wang Yibo, Han Qiaoli, Zhang Xiwen, et al. The blade of wind turbine based on machine vision[J]. Science Technology and Engineering,2022,22(12):4879-4886.

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历史
  • 收稿日期:2021-06-18
  • 最后修改日期:2022-04-02
  • 录用日期:2021-11-30
  • 在线发布日期: 2022-05-07
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