Improved Scattering Models for Guided Wave Tomography

Water collecting at pipe supports will accelerate corrosion, reducing the pipe life at a very difficult-to-inspect location. A traditional approach would require the pipe to be emptied, lifted and an ultrasonic thickness gauge be scanned across the surface. Guided wave tomography could potentially address these problems: by sending waves from an array on one side of the support through to a set of receivers on the other side, an image of the thickness can be produced, which is typically achieved by exploiting the dispersive nature of guided waves causing their speed to change depending on the thickness. However, this assumption results in a resolution limit of around 2 wavelengths (roughly 8 times wall thickness at a typical operating point), which is often too low for tomography to be a practical solution. To go beyond this, better models of guided wave scattering are needed. This talk will discuss approaches to address the limitations through more realistic scattering assumptions

Guided wave tomography with an improved scattering model

Producing accurate thickness maps of corrosion damage is of great importance for assessing life in the petrochemical industry. Guided wave tomography provides a solution for this, by sending guided waves through the region of interest, then using tomographic imaging techniques to reconstruct the thickness map, importantly eliminating the need to take measurements at all points across the surface. However, to achieve accurate maps, the imaging algorithm must account for the way in which the guided waves interact with corrosion defects, and the complex scattering which occurs. Traditional approaches have exploited the dispersive nature of guided waves: a velocity map is produced from a dataset, then converted to thickness using the dispersion relationship. However, these relationships are derived for plates of constant thickness, which is not the case in the majority of defects, causing significant inaccuracies to exist in the images. This paper develops a more sophisticated inversion solution which accounts for the full-guided wave scattering, enabling more accurate images with resolution better than a wavelength, compared with two wavelengths previously. This is demonstrated with simulated and experimental data. The speed and stability of the algorithm in the presence of random noise and systematic errors is also demonstrated

The application of the Factorization Method to the subsurface imaging of surfacebreaking cracks

A common location for cracks to appear is at the surface of a component; at the near surface, many nondestructive evaluation techniques are available to inspect for these, but at the far surface this is much more challenging. Ultrasonic imaging is proposed to enable far surface defect detection, location, and characterization. One specific challenge here is the presence of a strong reflection from the backwall, which can often mask the relatively small response from a defect. In this paper, the factorization method (FM) is explored for the application of subsurface imaging of the surface-breaking cracks. In this application, the component has two parallel surfaces, the crack is initiated from the far side and the phased array is attached on the near side. Ideally, the pure scattered field from a defect is needed for the correct estimation of the scatterer through the FM algorithm. However, the presence of the backwall will introduce a strong specular reflection into the measured data which should be removed before applying the FM algorithm. A novel subtraction method was developed to remove the backwall reflection. The performance of the FM algorithm and this subtraction method were tested with the simulated and experimental data. The experimental results showed a good consistency with the simulated results. It is shown that the FM algorithm can generate high-quality images to provide a good detection of the crack and an accurate sizing of the crack length. The subtraction method was able to provide a good backwall reflection removal in the case of small cracks (1-3 wavelengths)

Limited View X-Ray CT for Turbine Blade Characterization

Turbine blades routinely contain complex and irregular internal structures making them difficult to inspect with most NDE techniques. X-ray CT provides a powerful inspection tool, enabling 2/3D images of the turbine blade to be produced. However, standard X-ray CT methods require thousands of projections, each regularly distributed evenly through 360° to produce an accurate image. The large number of projections and the regularity of sampling can result in lengthy data acquisition times and can lead to bottlenecks in manufacturing throughput. To alleviate these bottlenecks in throughput companies may be forced to purchase additional X-ray CT capability at great cost. In recent years there has been a drive by the medical industry to reduce patient X-ray exposure by limiting the number of projections whilst maintaining image quality in CT applications. Spurred by the ever increasing power of computers and the advents of graphics card processors a variety of limited view tomographic techniques capable of generating high quality images with less data have been developed. Central to these new algorithms is the principle of compressed sensing whereby an understanding of the signal sparsity is exploited to produce accurate reconstructions of the signal of interest with fewer samples than those required by the Shannon-Nyqist theorem. We present a survey of limited view algorithms for x-ray CT of a turbine blade with the aim of producing accurate internal structure estimates using minimal data

Fast binary CT using Fourier null space regularization (FNSR)

X-ray CT is increasingly being adopted in manufacturing as a non destructive inspection tool. Traditionally, industrial workflows follow a two step procedure of reconstruction followed by segmentation. Such workflows suffer from two main problems: (1) the reconstruction typically requires thousands of projections leading to increased data acquisition times. (2) The application of the segmentation process a posteriori is dependent on the quality of the original reconstruction and often does not preserve data fidelity. We present a fast iterative x-ray CT method which simultaneously reconstructs and segments an image from a limited number of projections called Fourier null space regularization (FNSR). The novelty of the approach is in the explicit updating of the image null space with values derived from a regularized image from the previous iteration, thus compensating for any missing projections and effectively regularizing the reconstruction. The speed of the method is achieved by directly applying the Fourier Slice Theorem where the non-uniform fast Fourier transform (NUFFT) is used to compute the frequency spectrum of the projections at their positions in the image k-space. At each iteration a segmented image is computed which is used to populate the null values of the image k-space effectively steering the reconstruction towards a binary solution. The effectiveness of the method to generate accurate reconstructions is demonstrated and benchmarked against other iterative reconstruction techniques using a series of numerical examples. Finally, FNSR is validated using industrial x-ray CT data where accurate reconstructions were achieved with 18 or more projections, a significant reduction from the 5000 needed by filtered back projection