Sparse Reconstruction and Damage Imaging Method Based on Uniform Sparse Sampling

Abstract The full wavefield detection method based on guided waves can efficiently detect and locate damages relying on the collection of large amounts of wavefield data. The acquisition process by scanning laser Doppler vibrometer (SLDV) is generally time-consuming, which is limited by Nyquist sampling theorem. To reduce the acquisition time, full wavefield data can be reconstructed from a small number of random sampling point signals combining with compressed sensing. However, the random sampling point signals need to be obtained by adding additional components to the SLDV system or offline processing. Because the random sparse sampling is difficult to achieve via the SLDV system, a new uniform sparse sampling strategy is proposed in this paper. By using the uniform sparse sampling coordinates instead of the random spatial sampling point coordinates, sparse sampling can be applied to SLDV without adding additional components or offline processing. The simulation and experimental results show that the proposed strategy can reduce the measurement locations required for accurate signal recovery to less than 90% of the Nyquist sampling grid, and the damage location error is within the minimum half wavelength. Compared with the conventional jittered sampling strategy, the proposed sampling strategy can directly reduce the sampling time of the SLDV system by more than 90% without adding additional components and achieve the same accuracy of guided wavefield reconstruction and damage location as the jittered sampling strategy. The research results can greatly improve the efficiency of damage detection technology based on wavefield analysis.