Sparse-TFM Imaging of Lamb Waves for the Near-Distance Defects in Plate-Like Structures

On Dispersion Compensation for GAW-Based Structural Health Monitoring

Guided acoustic waves (GAW) have proven to be a useful tool for structural health monitoring (SHM). However, the dispersive nature of commonly used Lamb waves compromises the spatial resolution making it difficult to detect small or weakly reflective defects. Here we demonstrate an approach that can compensate for the dispersive effects, allowing advanced algorithms to be used with significantly higher signal-to-noise ratio and spatial resolution. In this paper, the sign coherence factor (SCF) extension of the total focusing method (TFM) algorithm is used. The effectiveness is examined by numerical simulation and experimentally demonstrated by detecting weakly reflective layers with a highly dispersive A0 mode on an aluminum plate, which are not detectable without compensating for the dispersion effects.

A Convolutional Neural Network for Ultrasound Plane Wave Image Segmentation With a Small Amount of Phase Array Channel Data

Single-angle plane wave has a huge potential in ultrasound high frame rate imaging, which, however, has a number of difficulties, such as low imaging quality and poor segmentation results. To overcome these difficulties, an end-to-end convolutional neural network (CNN) structure from single-angle channel data was proposed to segment images in this article. The network removed the traditional beamforming process and used raw radio frequency (RF) data as input to directly obtain segmented image. The signal features at each depth were extracted and concatenated to obtain the feature map by a special depth signal extraction module, and the feature map was then put into the residual encoder and decoder to obtain the output. A simulated hypoechoic cysts dataset of 2000 and an actual industrial defect dataset of 900 were used for training separately. Good results have been achieved in both simulated medical cysts segmentation and actual industrial defects segmentation. Experiments were conducted on both datasets with phase array sparse element data as input, and segmentation results were obtained for both. On the whole, this work achieved better quality segmented images with shorter processing time from single-angle plane wave channel data using CNNs; compared with other methods, our network has been greatly improved in intersection over union (IOU), F1 score, and processing time. Also, it indicated that the feasibility of applying deep learning in image segmentation can be improved using phase array sparse element data as input.

Subsampling Approaches for Compressed Sensing with Ultrasound Arrays in Non-Destructive Testing

Full Matrix Capture is a multi-channel data acquisition method which enables flexible, high resolution imaging using ultrasound arrays. However, the measurement time and data volume are increased considerably. Both of these costs can be circumvented via compressed sensing, which exploits prior knowledge of the underlying model and its sparsity to reduce the amount of data needed to produce a high resolution image. In order to design compression matrices that are physically realizable without sophisticated hardware constraints, structured subsampling patterns are designed and evaluated in this work. The design is based on the analysis of the Cramér–Rao Bound of a single scatterer in a homogeneous, isotropic medium. A numerical comparison of the point spread functions obtained with different compression matrices and the Fast Iterative Shrinkage/Thresholding Algorithm shows that the best performance is achieved when each transmit event can use a different subset of receiving elements and each receiving element uses a different section of the echo signal spectrum. Such a design has the advantage of outperforming other structured patterns to the extent that suboptimal selection matrices provide a good performance and can be efficiently computed with greedy approaches.

Instantaneous Phase Coherence Imaging for Near-Field Defects by Ultrasonic Phased Array Inspection

This paper describes an imaging method for near-field defect detection in aluminum plates based on Green's function recovery and application of instantaneous phase coherence weighting factors. The directly acquired acoustic information of near-field defects is usually obscured by the nonlinear effects due to the physical limitation of the acquisition system. Using the diffuse field to recover the Green's function can effectively retrieve the early time information. However, averaging operations of finite number in this process produces an imperfect imaging result. In order to improve the image quality, two kinds of instantaneous phased coherence weighting factors are used to weight the Green's function to reduce the background noise and improve the signal-to-noise ratio: the instantaneous phase coherence factor (IPCF), and the instantaneous phase weighting factor (IPWF). Experiments are conducted on two aluminum plates with two and four near-field defects, respectively. As a result, the background noise of amplitude images weighted by IPCF and IPWF is less than that of the conventional total focusing method (TFM). In addition, the IPCF image achieves a better signal-to-noise ratio (SNR) than that of IPWF, and the phase discontinuity in an IPWF image is suppressed through the IPCF.

Wavenumber Imaging of Near-Surface Defects in Rails using Green's Function Reconstruction of Ultrasonic Diffuse Fields

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.

The Auto-Correlation of Ultrasonic Lamb Wave Phased Array Data for Damage Detection

Ultrasonic phased array is widely used for damage detection recently because of its high sensitivity and rapid scanning without sensor movements. However, the measured signal is always influenced by the remnants of the initial excitation and the nonlinear signals from the instrumentation, which limits its application in thin-plate structures. To address this issue, an approach called auto-correlation subtraction is proposed to extract the scattering information of defects in this paper. In order to testify the feasibility of this method for damage detection, the experiments were carried out on three thin aluminum plates combined with the total focusing method (TFM) for imaging. By auto-correlating the full matrix data received by sensors and then subtracting the average auto-correlation of noise recorded by all receivers, the coherent scattered signal containing defect information is recovered. The experimental results indicate that the coherent travel time is in agreement with the theoretical value and the signal-to-noise ratio are improved. Additionally, compared with the cross-correlation technique, the time synchronization between different receivers is not necessary with the auto-correlation method. Results indicate that the presented method can improve the imaging resolution and has a great potential in the field of non-destructive testing.