为更好地推动风电机组叶片运维技术智能化发展,基于机器视觉检测技术,提出一种风电机组叶片多类型损伤检测方法。首先对智能巡检无人机平台采集到的风电机组叶片图像进行图像灰度化、滤波增强、分割以及形态学处理,实现叶片损伤区域的识别;然后基于连通域分析原理来获取叶片损伤区域的几何特征和灰度特征等参数信息,并依此设计出风电机组叶片损伤类型识别分类器;最后将检测算法和分类器融合于所设计的风电机组叶片损伤可视化检测系统。试验表明,该系统对于表皮脱落、涂层破损、砂眼、油污及裂纹等典型叶片损伤的平均检测准确率为90.4%。
Abstract
A multi-type damage detection method for wind turbine blades is proposed by machine vision detection technology to promote the intellectual development of its operation and maintenance. Firstly, the blade image from the intelligent patrol UAV platform is used to identify the blade-damaged area by graying, filtering, enhancement, segmentation, and morphological processing. Then, an identification classifier of the blade damage type is designed through the geometric features and gray feature information of the blade damage area by connected domain analysis. Finally, the detection algorithm and classifier are integrated into the wind turbine blade damage visual detection system. The results show that the average detection accuracy is 90.4% for typical blode damage such as skin peeling, coating damage, sand holes, oil stains, cracks, etc.
关键词
风电机组 /
叶片 /
机器视觉 /
损伤检测 /
多类型损伤
Key words
wind turbines /
blades /
machine vision /
damage detection /
multi-type damage
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基金
江苏省输配电装备技术重点实验室自主科研课题（2022JSSPD07）
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