The defect imaging based on guided wave provides an intuitive way for defect localization. Recently, sparse representation methods based on the damage sparsity assumption have been developed for defect imaging, where few sensors are used in these methods. However, these sparse imaging methods need repeatedly tuning the regularization parameter to obtain a good imaging performance. In this paper, an adaptive method based on complex Bayesian group Lasso is developed for localizing the damage. A group Lasso model is constructed to represent the defect imaging problem, and formulated by a sparse Bayesian learning (SBL) framework, where a hierarchical model of a Laplace prior is built to represent the group Lasso regularization. Estimations of the model variables are derived by using variational inference. In the proposed method, the model parameters are automatically updated without needing priori information. The effectiveness of the proposed method is verified by analyzing an experimental data.
Surface-breaking cracks are typical defects in tubular structures. Compared with other types of defects such as internal voids, surface cracks often impose more serious threats to structural integrity. This study presents an approach to detect and characterize surface-breaking cracks on tubular samples through the use of ultrasonic phased array technology with surface acoustic waves (SAWs). A Rayleigh type surface wave is selected in our work as it is nondispersive and highly sensitive to surface and subsurface defects. Finite element (FE) analysis is used to simulate the interaction between Rayleigh waves and surface-breaking cracks with varying depths, inclined angles and profiles. The reflection coefficient of Rayleigh waves is calculated based on simulation results and fitted to a proper model. Crack depths and inclined angles can be evaluated from the fitted curves. Furthermore, a full matrix capture (FMC) data acquisition strategy is simulated in FE models with phased array to collect pulse-echo signals of Rayleigh waves. An array imaging algorithm is applied to FMC data and adapted to curved surface. The profile and location of surface cracks are reconstructed from imaging results. The configuration of phased array is optimized to increase the resolution of the method. The proposed approach is validated numerically and provides an efficient way to measure the length, depth and inclined angle of surface-breaking cracks on tubular component.
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