In this paper, the hybrid Delay-And-Sum (DAS) with Sparse Reconstruction (SR) method was further developed for damage location in composite plates. In composite materials, anisotropy leads to some challenges in using conventional damage location methods, which are developed for isotropic materials. In the hybrid DAS-SR method, the DAS and SR methods were combined as a complement of each other. To investigate the DAS-SR method for composite structures, the group velocity of the travelling wave for different directions was first measured experimentally via PZTs. The DAS and SR formulations were then modified to be compatible with the direction-dependent group velocities. The results show that this modification increases the accuracy of the DAS and SR methods for damage location in a composite plate. However, using group-velocity instead of phase-velocity in the standard SR method, causes some model mismatch and errors in the damage localization and this problem was solved by using the modified hybrid DAS-SR method. The experimental results demonstrated that the performance of the modified hybrid method for detection and location of barely visible impact damages as well as for multi-location artificial damages is better than that of the DAS and SR methods when separately used.@en

In this paper, the performance of the sparse reconstruction (SR) and the delay-and-sun (DAS) methods for damage localization, were evaluated for various environmental and operational conditions, both numerically and experimentally. To assess these damage localization methods, a methodology based on the Taguchi method was used to make the experimental design, and a modified performance-index was defined to represent the quality of reconstructed images. Then, the robustness and the accuracy of each method, in a well-defined performance region relevant to in-service aerospace structures, were investigated using the Taguchi and analysis of variance methods. It was concluded that for the defined conditions, the robustness of the delay and sum method is better than the sparse reconstruction method for uncontrolled factors. However, the sparse reconstruction method is more robust to poor baseline subtraction than the delay and sum method, while the delay and sum method was more robust to factors that lead to a model mismatch. These results provide additional insight into the design of reliable accurate structural health monitoring systems. The outcomes of this work can be used in future reaserch into SHM imaging techniques.@en

To perform active structural health monitoring, guided Lamb waves for damage detection have recently gained extensive attention. Many algorithms are used for damage detection with guided waves and among them, the delay-and-sum method is the most commonly used algorithm because of its robustness and simplicity. However, delay-and-sum images tend to have poor accuracy with a large spot size and a high noise floor, especially in the presence of multiple damages. To overcome these problems, another method that is based on sparse reconstruction can be used. Although the images produced by the sparse reconstruction method are superior to the conventional delay-and-sum method, it has the challenges of the time and cost of computations in comparison with the delay-and-sum method. Also, in some cases in multi-damage detection, the sparse reconstruction method totally fails. In this article, using prior support information of the structure achieved by the delay-and-sum method, a hybrid method based on sparse reconstruction method is proposed to improve the computational performance and robustness of sparse reconstruction method in the case of multi-damage presence. The effectiveness of the proposed method in detecting damages is demonstrated experimentally and numerically on a simple aluminum plate. The technique is also shown to accurately identify and localize multi-site damages as well as single damage with low sampled signals.@en