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Sparse and Random Sampling Techniques for High-Resolution Full-Field BSS-Based Structural Dynamics Identification from Video

机译：基于视频的高分辨率全场基于BSS的结构和动力学的稀疏和随机采样技术

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摘要

Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed.
机译：用于识别结构动力学的基于视频的技术的优点是，与传统的加速度计或应变仪技术相比，它们的部署成本非常低廉。当使用全场，高分辨率分析技术从视频中获得视频的结构动力学时，利用像素时间序列上的算法，例如主成分分析和盲源分离解决方案，即可获得高分辨率的全场模态识别优势。振动结构视频的一个重要特性是它特别稀疏。通常，振动结构的视频具有包含数千个甚至数百万个像素和数百至数千个帧的尺寸。但是，仅使用一些模式形状及其关联的时间序列就可以描述振动结构的运动。结果，用于稀疏和随机采样的新兴技术（例如压缩感测）应适用于对视频执行模式识别。这项工作展示了如何将全场，高分辨率，结构动力学识别框架与压缩采样结合使用。证明了这项工作中描述的技术，当从以常规方式捕获的视频中删除70％到90％的帧时，能够从振动结构的实验视频中恢复模式形状。

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