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

Abstract

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.

Authors:
; ; ORCiD logo; ;
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1634255
Alternate Identifier(s):
OSTI ID: 1648088
Report Number(s):
LA-UR-19-30846
Journal ID: ISSN 1424-8220; SENSC9; PII: s20123526
Grant/Contract Number:  
20170694PRD4; 89233218CNA000001
Resource Type:
Published Article
Journal Name:
Sensors
Additional Journal Information:
Journal Name: Sensors Journal Volume: 20 Journal Issue: 12; Journal ID: ISSN 1424-8220
Publisher:
MDPI AG
Country of Publication:
Switzerland
Language:
English
Subject:
47 OTHER INSTRUMENTATION; compressive sensing; blind source separation; random projection; sparse reconstruction; phototoxicity; nonlinear filtering; cryptography; privacy-preserving structural health monitoring (SHM); 5G network

Citation Formats

Martinez, Bridget, Green, Andre, Silva, Moises Felipe, Yang, Yongchao, and Mascareñas, David. Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video. Switzerland: N. p., 2020. Web. doi:10.3390/s20123526.
Martinez, Bridget, Green, Andre, Silva, Moises Felipe, Yang, Yongchao, & Mascareñas, David. Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video. Switzerland. doi:https://doi.org/10.3390/s20123526
Martinez, Bridget, Green, Andre, Silva, Moises Felipe, Yang, Yongchao, and Mascareñas, David. Mon . "Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video". Switzerland. doi:https://doi.org/10.3390/s20123526.
@article{osti_1634255,
title = {Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video},
author = {Martinez, Bridget and Green, Andre and Silva, Moises Felipe and Yang, Yongchao and Mascareñas, David},
abstractNote = {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.},
doi = {10.3390/s20123526},
journal = {Sensors},
number = 12,
volume = 20,
place = {Switzerland},
year = {2020},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: https://doi.org/10.3390/s20123526

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