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Smartphone based parameter estimates of a dynamic oscillator using high-speed video imaging and incremental discriminating colour learning

机译:使用高速视频成像和增量辨别颜色学习动态振荡器的智能手机参数估计

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Image-based systems are increasingly being used for Structural Health Monitoring (SHM) applications. Video-based motion tracking algorithms can be used to analyse dynamic responses characterised by low frequencies, large deflections and low damping ratios. The advantages of image processing over other methods include the ability to track multiple points on a structure, its scalability, and its ease of use. Standard video acquisition devices are limited in their ability to assess dynamic responses and identify natural frequencies or damping ratios of structures due to the relatively low sampling rate, or frame rate. As such, there becomes a need to use video cameras that possess the ability to record at high frame rates - a feature that is becoming increasingly common on modern smartphones. This paper demonstrates how such video cameras can be used to estimate natural frequencies and viscous damping ratios of structures by considering a Single Degree of Freedom (SDOF) linear system undergoing free vibrations. The slow-motion feature on a Nexus 6P Smartphone was used to capture the dynamic response of the vibrating system. The video was assessed by an Incremental Discriminative Colour Tracking (IDCT) algorithm which tracked the position of points on the system, from which the natural frequency and damping ratio could then be extracted. The results were compared to a reference accelerometer and theoretical estimates. This paper acts as an evidence base for the evolving capabilities of smartphone based monitoring, and ultimately, shows that smartphones have value as a tool for the cost-effective assessment of structures.
机译:基于图像的系统越来越多地用于结构健康监测(SHM)应用。基于视频的运动跟踪算法可用于分析特征在于低频,大偏转和低阻尼比的动态响应。其他方法的图像处理的优点包括跟踪结构上的多个点的能力,其可伸缩性和易用性。标准视频采集设备的能力受到评估动态响应和识别由于采样率相对较低或帧速率的结构的自然频率或阻尼比。因此,需要使用具有在高帧速率下记录的视频摄像机的视频摄像机 - 在现代智能手机上变得越来越常见的功能。本文演示了通过考虑在进行自由振动的单一自由度(SDOF)线性系统,如何使用这种视频摄像头来估计结构的自然频率和粘性阻尼比。 Nexus 6p智能手机上的慢动作功能用于捕获振动系统的动态响应。通过增量鉴别色跟踪(IDCT)算法评估视频,该算法跟踪系统上的点的位置,然后可以提取自然频率和阻尼比的位置。将结果与参考加速度计和理论估计进行比较。本文充当了基于智能手机的监测的不断发展能力的证据基础,并最终表明智能手机具有作为对结构成本效益评估的工具的价值。

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