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

Martinez, Bridget; Green, Andre; Silva, Moises Felipe; Yang, Yongchao; Mascareñas, David

doi:10.3390/s20123526

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

Journal Article · Mon Jun 22 00:00:00 EDT 2020 · Sensors

DOI:https://doi.org/10.3390/s20123526· OSTI ID:1634255

Martinez, Bridget; Green, Andre; ; Yang, Yongchao; Mascareñas, David

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1634255

Alternate ID(s):: OSTI ID: 1648088

Report Number(s):: LA-UR--19-30846; PII: s20123526

Journal Information:: Sensors, Journal Name: Sensors Journal Issue: 12 Vol. 20; ISSN SENSC9; ISSN 1424-8220

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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