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Light Field Imaging of Three-Dimensional Structural Dynamics

  • Benjamin Chesebrough
  • Sudeep Dasari
  • Andre Green
  • Yongchao YangEmail author
  • Charles R. Farrar
  • David Mascareñas
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Real world structures, such as bridges and skyscrapers, are often subjected to dynamic loading and changing environments. It seems prudent to measure high resolution vibration data, in order to perform accurate damage detection and to validate and update the models and knowledge about the operating structure (aka finite element models). Many existing vibration measurement methods could be either low resolution (e.g., accelerometers or strain gauges), and time and labor consuming to deploy in field (e.g., laser interferometry). Previous work by Yang et al. has shown that low-cost regular digital video cameras enhanced by advanced computer vision and machine learning algorithms can extract very high resolution dynamic information about the structure and perform damage detection at novel scales in an relatively efficient and unsupervised manner. More interestingly this work used a machine learning pipeline that made minimal assumptions about lighting conditions or the nature of the structure in order to perform modal decomposition. The technique is currently limited to two dimensions if only one digital video camera is used. This paper uses light field imagers - a new camera system that captures the direction light entered the camera - to make depth measurements of scenes and extend the modal analysis technique proposed in Yang et al. to three dimensions. The new method is verified experimentally on vibrating cantilever beams with out of plane vibration, whose full-field modal parameters are extracted from the light field measurements. The experimental results are discussed and some limitations are pointed out for future work.

Keywords

3D Structural Dynamics Modal Analysis Photogrammetry Blind Source Separation Light field imaging 

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Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  • Benjamin Chesebrough
    • 1
  • Sudeep Dasari
    • 2
  • Andre Green
    • 3
  • Yongchao Yang
    • 4
    Email author
  • Charles R. Farrar
    • 4
  • David Mascareñas
    • 4
  1. 1.Mechanical EngineeringNew Mexico Institute of Mining and TechnologySocorroUSA
  2. 2.Electrical Engineering and Computer ScienceUniversity of California: BerkeleyBerkeleyUSA
  3. 3.Computer ScienceUniversity of New MexicoAlbuquerqueUSA
  4. 4.Los Alamos National Lab – Engineering InstituteLos AlamosUSA

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