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High-Dimensional Data Analytics for Sparse Recovery of Guided-Waves Dispersion Curves Using B-Splines
Conference proceeding

High-Dimensional Data Analytics for Sparse Recovery of Guided-Waves Dispersion Curves Using B-Splines

Hamed Momeni, Mohammad Jeddi and Arvin Ebrahimkhanlou
Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring, pp 796-803
2023
DOI: https://doi.org/10.12783/shm2023/36815
url
https://doi.org/10.12783/shm2023/36815View
Published, Version of Record (VoR) Open

Abstract

This research presents a technique to recover the dispersion curves of guidedwaves by utilizing the inherent sparsity of these signals in the frequency-wavenumber domain. The proposed methodology is a data-driven approach that combines physicsbased 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.

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