基于机器视觉的风力机叶片故障检测技术研究  

RESEARCH ON WIND TURBINE BLADE FAULT DETECTION TECHNOLOGY BASED ON MACHINE VISION

作　　者：朱恩龙[1,2] 冯聪聪 申振腾 史天宇 亓豪 孙波文 Zhu Enlong;Feng Congcong;Sheng Zhenteng;Shi Tianyu;Qi Hao;Sun Bowen(College of Mechinical Engineering,Tianjin University of Science and Technology,Tianjin 300222,China;Tianjin Key Laboratory of Integrated Design and Online Monitoring of Light Industry and Food Engineering Machinery and Equipment,Tianjin 300222,China;Tianjin Zhongke Intelligent Identification Co.,Ltd.,Tianjin 300457,China)

机构地区：[1]天津科技大学机械工程学院,天津300222 [2]天津市轻工与食品工程机械装备集成设计与在线监控重点实验室,天津300222 [3]天津中科智能识别有限公司,天津300457

出　　处：《太阳能学报》2023年第4期209-215,共7页Acta Energiae Solaris Sinica

摘　　要：针对运行中的风力机叶片,提出一种基于机器视觉特征分类的故障诊断方法。通过对叶片叶尖进行圆形标记,利用工业相机周期性获取叶片尖端的图像,并在Halcon软件上对图像进行预处理,对大雾天气下采集的图像利用暗通道除雾算法进行清晰化处理。利用叶尖标记检测算法提取标记、计算区域圆度和区域中心等区域特征。对相邻叶片上标记计算位移差,并与系统预警阈值比较,判断叶片在扭转或偏摆方向的变形程度和故障趋势,从而实现风力机叶片变工况运行状态在线检测和自适应预警。A fault diagnosis method based on machine vision feature classification is proposed for wind turbine blades in operation.By circularly marking the blade tip,images of the blade tip are acquired periodically using an industrial camera and pre-processed on Halcon software,and images acquired in foggy weather are clarified using a dark channel defogging algorithm.A leaf tip marker detection algorithm is used to extract markers,calculation area features such as area roundness and area centre.The markers on the adjacent blades are then compared with the system's warning threshold to determine the degree of blade deformation and fault trend in the direction of torsion or deflection,thus enabling online detection and adaptive warning of the variable operating conditions of wind turbine blades.

关 键 词：风力机叶片 故障检测 图像处理 机器视觉 特征分类 

分 类 号：TP873[自动化与计算机技术—检测技术与自动化装置]