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Use of delay and sum for sparse reconstruction improvement for structural health monitoring

Nokhbatolfoghahai, A ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1177/1045389X19873415
  3. Publisher: SAGE Publications Ltd , 2019
  4. Abstract:
  5. 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. © The Author(s) 2019
  6. Keywords:
  7. Delay-and-sum ; Lamb waves ; Damage detection ; Guided electromagnetic wave propagation ; Surface waves ; Ultrasonic waves ; Computational performance ; Delay and sums ; Guided lamb waves ; High noise ; Hybrid method ; Multiple damages ; Sparse reconstruction ; Spot sizes ; Structural health monitoring
  8. Source: Journal of Intelligent Material Systems and Structures ; Volume 30, Issue 18-19 , 2019 , Pages 2919-2931 ; 1045389X (ISSN)
  9. URL: https://journals.sagepub.com/doi/10.1177/1045389X19873415