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Using 2D Phase-Based Motion Estimation and Video Magnification for Binary Damage Identification on a Wind Turbine Blade

机译:在风力涡轮机叶片上使用基于2D相的运动估计和视频倍率进行二元损伤识别

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Videos (sequence of images) as three-dimensional signals may be considered as a very rich source of information for several applications in structural dynamics identification and structural health monitoring (SHM) systems. Within this paper high-speed cameras are used to record the sequence of images (video) of a baseline and damaged wind turbine blade (WTB) while vibrating due to the external loadings. Among several computer vision algorithms for motion extraction from the videos, phase-based motion estimation technique is used to extract the response of both the baseline and damaged wind turbine blade. Modal parameters (natural frequencies and operating deflection shapes) were used as damage sensitive features in order to detect the occurrence of damage in the wind turbine blade. The first four natural frequencies of the both baseline and damaged wind turbine blade are extracted by analyzing the estimated motion provided by the phase based motion estimation in the frequency domain. The motion magnification algorithm is also utilized to visualize and extract the operating deflection shapes of the wind turbine blade which may be used later as an indicator of the presence of damage. It has been shown that changes in the dynamic behavior of the wind turbine blade will result to deviations in the nominal natural frequencies and operating deflection shapes, and the damaged wind turbine blade can be differentiated from the baseline WTB using this non-contact measurement approach.
机译:作为三维信号的视频(图像序列)可以被认为是结构动态识别和结构健康监测(SHM)系统中的若干应用的非常丰富的信息来源。在本文中,高速摄像机用于记录基线和损坏的风力涡轮机叶片(WTB)的图像(视频)的序列,同时由于外部负载振动而振动。在来自视频的运动提取的几种计算机视觉算法中,基于相的运动估计技术用于提取基线和损坏的风力涡轮机叶片的响应。模态参数(自然频率和操作偏转形状)用作损坏敏感特征,以检测风力涡轮机叶片中损坏的发生。通过分析由频域中的基于相的运动估计提供的估计的运动来提取所述基线和损坏的风力涡轮机叶片的前四个固有频率。运动放大倍率算法还用于可视化和提取风力涡轮机叶片的操作偏转形状,其可以以后作为存在损坏的指示器。已经表明,风力涡轮机叶片的动态行为的变化将导致偏离标称自然频率和操作偏转形状,并且损坏的风力涡轮机叶片可以使用该非接触式测量方法与基线WTB不同。

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