摘要翻译：
基于振动的结构健康监测(SHM)技术是最常用的结构损伤识别方法之一。结构损伤的存在可以通过监测结构在外载荷作用下的动态行为变化来识别，通常通过实验模态分析(EMA)或操作模态分析(OMA)来进行。这些用于SHM的工具通常需要有限数量的物理连接传感器（例如加速度计），以便记录结构的响应，以便进一步分析。信号调理器、电线、无线接收器和数据采集系统(DAQ)也是用于基于振动的SHM的传统传感系统的典型组件。然而，用加速度计等接触传感器对轻质结构进行测量可能会引起质量加载效应，对于大型结构来说，测量是劳动密集型和耗时的。使用传统接触式传感器对大型结构进行高空间测量并不总是可行的，而且固定接触式传感器在超过主结构的寿命时也可能缺乏可靠性。在最先进的非接触测量技术中，数字摄像机能够快速地从结构中远程采集高密度的空间信息。本文采用基于相位的运动估计(PME)方法从记录的视频（即图像序列）中提取细微运动信息，并利用提取的信息对2.3米长的Skystream风力机叶片(WTB)进行损伤识别。PME和基于相控运动放大的方法从捕获的图像序列中估计风力机叶片的基线和损坏测试情况下的结构运动。
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英文标题：
《Vibration-Based Damage Detection in Wind Turbine Blades using
  Phase-Based Motion Estimation and Motion Magnification》
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作者：
Aral Sarrafi, Zhu Mao, Christopher Niezrecki, Peyman Poozesh
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  Vibration-based Structural Health Monitoring (SHM) techniques are among the most common approaches for structural damage identification. The presence of damage in structures may be identified by monitoring the changes in dynamic behavior subject to external loading, and is typically performed by using experimental modal analysis (EMA) or operational modal analysis (OMA). These tools for SHM normally require a limited number of physically attached transducers (e.g. accelerometers) in order to record the response of the structure for further analysis. Signal conditioners, wires, wireless receivers and a data acquisition system (DAQ) are also typical components of traditional sensing systems used in vibration-based SHM. However, instrumentation of lightweight structures with contact sensors such as accelerometers may induce mass-loading effects, and for large-scale structures, the instrumentation is labor intensive and time consuming. Achieving high spatial measurement resolution for a large-scale structure is not always feasible while working with traditional contact sensors, and there is also the potential for a lack of reliability associated with fixed contact sensors in outliving the life-span of the host structure. Among the state-of-the-art non-contact measurements, digital video cameras are able to rapidly collect high-density spatial information from structures remotely. In this paper, the subtle motions from recorded video (i.e. a sequence of images) are extracted by means of Phase-based Motion Estimation (PME) and the extracted information is used to conduct damage identification on a 2.3-meter long Skystream wind turbine blade (WTB). The PME and phased-based motion magnification approach estimates the structural motion from the captured sequence of images for both a baseline and damaged test cases on a wind turbine blade.
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PDF链接：
https://arxiv.org/pdf/1804.00558