Smart Structures and Systems

  • 제25권3호
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  • Pages.369-384
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  • 2020
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Sparse reconstruction of guided wavefield from limited measurements using compressed sensing

  • Qiao, Baijie (The State Key Laboratory for Manufacturing Systems Engineering) ;
  • Mao, Zhu (Department of Mechanical Engineering, University of Massachusetts Lowell) ;
  • Sun, Hao (Department of Civil Engineering, Northeastern University) ;
  • Chen, Songmao (Department of Mechanical Engineering, University of Massachusetts Lowell) ;
  • Chen, Xuefeng (The State Key Laboratory for Manufacturing Systems Engineering)
  • Received : 2019.04.18
  • Accepted : 2019.07.12
  • Published : 2020.03.25
⟨ 이전 논문 다음 논문 ⟩

Abstract

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l1-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, China Postdoctoral Science Foundation

The first author would like to acknowledge the support from the National Natural Science Foundation of China (No. 51705397) and China Postdoctoral Science Foundation (No. 2019T120900 & 2017M610636). The primary part of the presented work was completed during the first author's visit to the Structural Dynamics and Acoustic Systems Laboratory at the University of Massachusetts Lowell.

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