Abstract
Wavenumber imaging with Green’s function reconstruction of ultrasonic diffuse fields is used to realize fast imaging of near-surface defects in rails. Ultrasonic phased array has been widely used in industries because of its high sensitivity and strong flexibility. However, the directly measured signal is always complicated by noise caused by physical limitations of the acquisition system. To overcome this problem, the cross-correlations of the diffuse field signals captured by the probe are performed to reconstruct the Green’s function. These reconstructed signals can restore the early time information from the noise. Experiments were conducted on rails with near-surface defects. The results confirm the effectiveness of the cross-correlation method to reconstruct the Green’s function for the detection of near-surface defects. Different kinds of ultrasonic phased array probes were applied to collect experimental data on the surface of the rails. The Green’s function recovery is related to the number of phased array elements and the excitation frequency. In addition, the duration and starting time of the time-windowed diffuse signals were explored in order to achieve high-quality defect images.
Highlights
In the transportation industry, the railway is exposed to harsh environments all year
Based on its independent parallel reception channels, the ultrasonic phased array is powerful in data acquisition with full matrix capture (FMC)
The wavenumber algorithm combined with the full matrix data is summarized as the following steps: Firstly, a 3-D full matrix data is obtained, and 3-D Fourier transform is performed on this data; secondly, the 2-D data slice corresponding to each wavenumber, ku, is mapped by Stolt complex interpolation, and the transformation of all data slices from E(ω, kv |ku ) to F(kx, kz |ku ) is implemented in turn; F(kx, kz |ku ) is added into F(kx, kz )
Summary
The railway is exposed to harsh environments all year. Ultrasonic nondestructive testing (UNDT) uses controllable mechanical waves to detect internal defects in materials, which is one of the most commonly used methods [1]. Such waves can be generated by piezoceramic transducers [2,3], metamaterials-based sensors [4,5], and so on in order to detect and localize damages. The Green’s function retrieval theory and wavenumber imaging algorithm are combined to solve the imaging problem of near-surface defects, which is still under development in ultrasonic nondestructive testing.
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