A More Accurate Reconstruction Method for Detecting Large-Depth Defects in Plates Using SH Guided Waves

The inspection of thickness thinning defects and corrosion defects is greatly significant for the health prediction of plate structures. The main aim of this research is to propose a novel and effective approach to achieve the accurate and rapid detection of arbitrary defects using shear horizontal (SH) guided waves, particularly for large-depth and complex defects. The proposed approach combines the quantitative detection of Fourier transform with a reference model-based strategy to improve the accuracy of large-depth defect detection. Since the shallow defect profile is theoretically constructed by inverse Fourier transform of the product of reflection coefficients and integral coefficients of reference models, the unknown large-depth defect can be initially assessed using the relevant information from a predefined reference model. By iteratively updating the integral coefficients of reference models, the accuracy of reconstruction of large-depth defects is much improved. To achieve the converged defect profile, a termination criterion, the root mean square error, is applied to guarantee the construction of defects with a high level of accuracy. Moreover, the hybrid finite element method is used to simulate the propagation of SH guided waves in plates for calculating the reflection coefficients of plates with defects. Finally, to demonstrate the capability of the developed reconstruction method for defect detection in terms of accuracy and efficiency, three types of large-depth defect profiles, i.e., a rectangular flaw, a double-rectangular flaw and a complex flaw, are examined. Results show that the discrepancy between the predicted defect profile and the real one is quite small, even in the largest-depth defect case where the defect depth is equal to 0.733 times the plate thickness, the minimal difference is observed. It is noted that the fast convergence of the proposed approach can be achieved by no more than ten updates for the worst case.