Evaluation of the sparse reconstruction and the delay-and-sum damage imaging methods for structural health monitoring under different environmental and operational conditions

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.

Automated summary

In this paper, the performance of sparse reconstruction and delay-and-sum methods for damage localization was evaluated under various conditions using numerical and experimental methods. The study employed the Taguchi method for experimental design and defined a modified performance index to represent image quality. Results showed that the delay-and-sum method exhibited better robustness under uncontrolled factors, while sparse reconstruction was more reliable for poor baseline subtraction. These findings offer valuable insights for designing reliable structural health monitoring systems.