Martinez Bridget, Green Andre, Silva Moises Felipe, Yang Yongchao, Mascareñas David
Los Alamos National Laboratory, Los Alamos, NM 87544, USA.
Applied Electromagnetism Laboratory, Universidade Federal do Pará, R. Augusto Corrêa, Guamá 01, Belém, 66075-110 Pará, Brazil.
Sensors (Basel). 2020 Jun 22;20(12):3526. doi: 10.3390/s20123526.
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.
与传统的加速度计或应变片技术相比，其部署成本非常低。当利用像素时间序列上的算法（如主成分分析和盲源分离解决方案），通过全场、高分辨率分析技术从视频中获取结构动力学信息时，就能实现高分辨率、全场模态识别这一额外优势。振动结构视频的一个重要特性是其特别稀疏。通常，振动结构的视频具有由数千甚至数百万像素以及数百到数千帧组成的维度。然而，振动结构的运动仅用少数几个振型及其相关的时间序列就能描述。因此，诸如压缩感知等新兴的稀疏和随机采样技术应该适用于对视频进行模态识别。这项工作展示了全场、高分辨率的结构动力学识别框架如何与压缩采样相结合。当从以传统方式捕获的视频中去除70%至90%的帧时，这项工作中描述的技术被证明能够从振动结构的实验视频中恢复振型。
