Torsional guided waves have been widely utilized to inspect surface corrosion in pipelines due to their simple displacement behavior and the ability of long-range transmission. Especially, the torsional mode T(0,1), which is the first order of torsional guided waves, plays the irreplaceable position and role, mainly because of its non-dispersion characteristic property. However, one of the most pressing challenges faced in modern quality inspection is to detect surface defects in pipelines with a high level of accuracy. Taking into account this situation, a quantitative reconstruction method using the torsional guided wave T(0,1) is proposed in this paper. The methodology for defect reconstruction consists of three steps. Firstly, reflection coefficients of the guided wave T(0,1) scattered by different sizes of axisymmetric defects are calculated using the developed hybrid finite element method (HFEM). Then, applying the boundary integral equation and Born approximation, Fourier transform of the surface defect profile can be analytically derived as the correlative product of reflection coefficients of torsional guided wave T(0,1) and the fundamental solution of the intact pipeline in frequency domain. Finally, reconstruction of defects is precisely performed by inverse Fourier transform of the product in the frequency domain. Numerical experiments show that the proposed approach is suitable for the detection of surface defects with arbitrary shapes.Meanwhile, effects of the depth and width of surface defects on the accuracy of defect reconstruction have been investigated. It is noted that the reconstructive error is less than 10%, providing the defect depth is no more than half of the pipe thickness.

The ultrasonic guided wave technology plays a significant role in the field of non-destructive testing as it employs acoustic waves with the advantages of high propagation efficiency and low energy consumption during the inspect process. However, the theoretical solutions to guided wave scattering problems with assumptions such as the Born approximation have led to the poor quality of the reconstructed results. Besides, the scattering signals collected from industry sectors are often noised and nonstationary. To address these issues, a novel physics-informed framework (PIF) for the quantitative reconstruction of defects by means of the integration of the data-driven method with the guided wave scattering analysis is proposed in this paper. Based on the geometrical information of defects and initial results obtained by the PIF-based analysis of defect reconstructions, a deep-learning neural network model is built to reveal the physical relationship between the defects and the noisy detection signals. This learning model is then adopted to assess and characterize the defect profiles in structures, improve the accuracy of the analytical model, and eliminate the impact of the noise pollution in the process of inspection. To demonstrate the advantages of the developed PIF for the complex defect reconstructions with the capability of denoising, several numerical examples are carried out. The results show that the PIF has greater accuracy for the reconstruction of defects in the structures than the analytical method, and provides a valuable insight into the development of artificial intelligence (AI)-assisted inspection systems with high accuracy and efficiency in the fields of structural integrity and condition monitoring.

The classification of weld defects is very important for the safety assessment of welded structures and feature extraction of ultrasonic defect signals is vital for defect classification. A novel approach based on wavelet packet energy entropy (WPEE) and kernel principal component analysis (KPCA) feature extraction and an artificial bee colony optimisation support vector machine (ABC-SVM) classifier is proposed in this paper. Firstly, the WPEE method is adopted to extract ultrasonic signal features of weld defects and KPCA is used for feature selection. Secondly, an ABC-SVM classifier is employed to perform defect classification. Finally, experiments involving defect feature extraction, selection and classification are carried out using four types of weld defect. The results demonstrate that the performance of the proposed feature extraction method based on WPEE is superior to that of wavelet packet energy (WPE). In addition, the WPEE-KPCA method achieved a higher accuracy rate of defect classification than WPEE.