Sparse sampled visualization of ultrasonic guided waves for defect identification in plate structures

Ultrasonic wave visualization is an effective tool to identify defects along with their location and topology in guided wave structural health monitoring applications. However, conventional imaging techniques require relatively longer data acquisition time and large storage space. Reconstructing waves from sparse sampled images can potentially overcome such difficulties. In this regard, sparse sampling techniques based on random sampling and uniform sampling are explored in this paper. Specifically random sparse sampling using Poisson disk with biharmonic interpolation (PDS-BI) technique is shown to achieve defect identification with an image sparsity of 95% and a sensor density of [Formula presented]. On the other hand, a novel technique based on uniform sparse sampling with carrier multiplication (US-CM) is demonstrated to reconstruct the image with a sparsity of greater than 98% and a sensor density less than [Formula presented]. The performance of the proposed techniques has been quantified using Structural Similarity (SSIM) metric and validated through experiments. The reconstructed images are found to be in good agreement with those obtained by a dense array with much larger number of receivers. We also experimentally demonstrate the visualization of ultrasonic wave propagation in a metallic plate using a surface-bonded fiber Bragg grating sensor as an alternative to the conventional sensor.