Smart Structures and Systems

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Modal parameter identification with compressed samples by sparse decomposition using the free vibration function as dictionary

  • Kang, Jie (School of Civil and Environmental Engineering, Harbin Institute of Technology at Shenzhen, University Town) ;
  • Duan, Zhongdong (School of Civil and Environmental Engineering, Harbin Institute of Technology at Shenzhen, University Town)
  • Received : 2019.01.24
  • Accepted : 2019.12.20
  • Published : 2020.02.25
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Abstract

Compressive sensing (CS) is a newly developed data acquisition and processing technique that takes advantage of the sparse structure in signals. Normally signals in their primitive space or format are reconstructed from their compressed measurements for further treatments, such as modal analysis for vibration data. This approach causes problems such as leakage, loss of fidelity, etc., and the computation of reconstruction itself is costly as well. Therefore, it is appealing to directly work on the compressed data without prior reconstruction of the original data. In this paper, a direct approach for modal analysis of damped systems is proposed by decomposing the compressed measurements with an appropriate dictionary. The damped free vibration function is adopted to form atoms in the dictionary for the following sparse decomposition. Compared with the normally used Fourier bases, the damped free vibration function spans a space with both the frequency and damping as the control variables. In order to efficiently search the enormous two-dimension dictionary with frequency and damping as variables, a two-step strategy is implemented combined with the Orthogonal Matching Pursuit (OMP) to determine the optimal atom in the dictionary, which greatly reduces the computation of the sparse decomposition. The performance of the proposed method is demonstrated by a numerical and an experimental example, and advantages of the method are revealed by comparison with another such kind method using POD technique.

Keywords

Acknowledgement

Supported by : Natural Science Foundation of China

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