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Title: A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions

Abstract

Recent developments in the ability to automatically and efficiently extract natural frequencies, damping ratios, and full-field mode shapes from video of vibrating structures has great potential for reducing the resources and time required for performing experimental and operational modal analysis at very high spatial resolution. Furthermore, these techniques have the added advantage that they can be implemented remotely and in a non-contact fashion. Emerging full-field imaging techniques therefore have potential to allow the identification of the modal properties of structures in regimes that used to be challenging. For instance, these techniques suggest that the high spatial resolution structural identification could be performed on an aircraft during flight using a ground or aircraft-based imager. They also have the potential to identify the dynamics of microscopic systems. In order to realize this capability it will be necessary to develop techniques that can extract full-field structural dynamics in the presence of non-ideal operating conditions. In this work, we develop a framework for the deployment of emerging algorithms that allow the automatic extraction of high-resolution, full-field modal parameters in the presence of non-ideal operating conditions. One of the most notable non-ideal operating conditions is the rigid body motion of both the structure being measuredmore » as well as the imager performing the measurement. We demonstrate an instantiation of the framework by showing how it can be used to address, in-plane, translational, rigid body motion. The development of a frame-to-frame keypoint–based technique for identifying full-field structural dynamics in the presence of either rigid body motion is presented and demonstrated in the context of the framework for the deployment of full-field structural identification techniques in the presence of non-ideal operating conditions. It is expected that this framework will ultimately help enable the collection of full-field structural dynamics using measurement platforms including unmanned aerial vehicles, robotic telescopes, satellites, imagers mounted in high-vibration environments (seismic, industrial, harsh weather), characterization of microscopic structures, and human-carried imagers. If imager-based structural identification techniques mature to the point that they can be used in non-ideal field conditions, it could open up the possibility that the structural health monitoring community will be able to think beyond monitoring individual structures, to full-field structural integrity monitoring at the city scale.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [3];  [3]
  1. Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
  2. Department of Aerospace, California Institute of Technology, Pasadena, CA, USA
  3. Los Alamos National Laboratory, Los Alamos, NM, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1478265
Resource Type:
Published Article
Journal Name:
Journal of Intelligent Material Systems and Structures
Additional Journal Information:
Journal Name: Journal of Intelligent Material Systems and Structures Journal Volume: 29 Journal Issue: 17; Journal ID: ISSN 1045-389X
Publisher:
SAGE Publications
Country of Publication:
United States
Language:
English

Citation Formats

Dasari, Sudeep, Dorn, Charles, Yang, Yongchao, Larson, Amy, and Mascareñas, David. A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions. United States: N. p., 2018. Web. doi:10.1177/1045389X17754271.
Dasari, Sudeep, Dorn, Charles, Yang, Yongchao, Larson, Amy, & Mascareñas, David. A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions. United States. doi:10.1177/1045389X17754271.
Dasari, Sudeep, Dorn, Charles, Yang, Yongchao, Larson, Amy, and Mascareñas, David. Fri . "A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions". United States. doi:10.1177/1045389X17754271.
@article{osti_1478265,
title = {A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions},
author = {Dasari, Sudeep and Dorn, Charles and Yang, Yongchao and Larson, Amy and Mascareñas, David},
abstractNote = {Recent developments in the ability to automatically and efficiently extract natural frequencies, damping ratios, and full-field mode shapes from video of vibrating structures has great potential for reducing the resources and time required for performing experimental and operational modal analysis at very high spatial resolution. Furthermore, these techniques have the added advantage that they can be implemented remotely and in a non-contact fashion. Emerging full-field imaging techniques therefore have potential to allow the identification of the modal properties of structures in regimes that used to be challenging. For instance, these techniques suggest that the high spatial resolution structural identification could be performed on an aircraft during flight using a ground or aircraft-based imager. They also have the potential to identify the dynamics of microscopic systems. In order to realize this capability it will be necessary to develop techniques that can extract full-field structural dynamics in the presence of non-ideal operating conditions. In this work, we develop a framework for the deployment of emerging algorithms that allow the automatic extraction of high-resolution, full-field modal parameters in the presence of non-ideal operating conditions. One of the most notable non-ideal operating conditions is the rigid body motion of both the structure being measured as well as the imager performing the measurement. We demonstrate an instantiation of the framework by showing how it can be used to address, in-plane, translational, rigid body motion. The development of a frame-to-frame keypoint–based technique for identifying full-field structural dynamics in the presence of either rigid body motion is presented and demonstrated in the context of the framework for the deployment of full-field structural identification techniques in the presence of non-ideal operating conditions. It is expected that this framework will ultimately help enable the collection of full-field structural dynamics using measurement platforms including unmanned aerial vehicles, robotic telescopes, satellites, imagers mounted in high-vibration environments (seismic, industrial, harsh weather), characterization of microscopic structures, and human-carried imagers. If imager-based structural identification techniques mature to the point that they can be used in non-ideal field conditions, it could open up the possibility that the structural health monitoring community will be able to think beyond monitoring individual structures, to full-field structural integrity monitoring at the city scale.},
doi = {10.1177/1045389X17754271},
journal = {Journal of Intelligent Material Systems and Structures},
number = 17,
volume = 29,
place = {United States},
year = {2018},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.1177/1045389X17754271

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Cited by: 3 works
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