Deconvolution processing for improved acoustic wavefield imaging

Abstract

Sparse sensor networks for Lamb wave-based structural health monitoring (SHM) can detect defects in plate-like structures. However, the limited number of sensor positions provides little information to characterize the unknown scatterer. This can be achieved by full wavefield analysis e.g. using Laser Doppler vibrometry measurements.

This paper proposes deconvolution processing that enhances the acoustic wavefield interpretation by increasing the temporal resolution of the underlying ultrasound signals. Applying this preprocessor to the whole wavefield allows improved non-destructive assessment of the defect. This approach is verified experimentally through a case study on an isotropic aluminum plate with four cracks.