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High-dimensional data analytics for sparse recovery of guided-waves dispersion curves using b-splines

Momeni, H ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.12783/shm2023/36815
  3. Publisher: DEStech Publications , 2023
  4. Abstract:
  5. This research presents a technique to recover the dispersion curves of guided-waves by utilizing the inherent sparsity of these signals in the frequency-wavenumber domain. The proposed methodology is a data-driven approach that combines physics-based knowledge with high-dimensional analysis to obtain the dispersion curves of the medium from experimental signals. Initially, a sparse two-dimensional dispersion matrix is constructed using sparse wavenumber analysis. Then, B-splines are fitted to non-zero elements of this matrix to establish an initial estimate of the dispersion curve parameters. These parameters are further optimized using the quasi-Newton algorithm to improve the accuracy of signal prediction. The results demonstrate that this method can significantly reduce the dimensions of Lamb waves signals to approximately 0.05%, which avoids overfitting. The retrieved signals in the frequency-distance domain exhibit a correlation of approximately 50% with the original signals. In comparison with sparse wavenumber analysis, this technique requires two orders of magnitude fewer parameters to represent the medium's dispersion curves. © 2023 by DEStech Publi cations, Inc. All rights reserved
  6. Keywords:
  7. Data Analytics ; Dispersion (waves) ; Frequency domain analysis ; Guided electromagnetic wave propagation ; Interpolation ; Surface waves
  8. Source: Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring ; 2023 , Pages 796-803 ; 978-160595693-0 (ISBN)
  9. URL: https://www.dpi-proceedings.com/index.php/shm2023/article/view/36815