K-means assisted local wavenumber estimation method
Source abstract
Abstract
Laser ultrasonic guided wave technology is widely used in nondestructive evaluation due to its high resolution, non-contact operation, and rapid scanning capabilities. Among various imaging techniques, the local wavenumber estimation (LWE) method is effective for assessing structural damage. However, its practical application is often limited by low computational efficiency, primarily due to the large number of possible central wavenumber ( kc ) that must be searched, ranging from those of the healthy area to the damaged region. To address this, an efficient K-means Assisted Local Wavenumber Estimation (KALWE) method is proposed, which employs a modified spectral intensity-weighted K-means clustering algorithm to identify optimal discretized central wavenumbers, thereby eliminating the need to process each individual kc . The feasibility of the proposed method is verified through numerical simulations and laser-based ultrasonic experiments. In addition, key factors, including cluster number, wavenumber separability, and spectral intensity related to the damage being evaluated, are systematically analyzed. Results demonstrate that the KALWE method significantly improves computational efficiency, increasing speed by 27–110 times with wavenumber estimation errors below 5%, offering a practical approach for rapid damage imaging in nondestructive evaluation.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Seismic Imaging and Inversion Techniques · Seismic Waves and Analysis · Seismology and Earthquake Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. Characterization of internal defects in cylindrical components using laser ultrasonic method with a modified SAFT algorithm10.1016/j.ndteint.2024.103243 ↗
2. A systematic review of ultrasonic techniques for defects detection in construction and building materials10.1016/j.measurement.2024.114181 ↗
3. Baseline-free nonlinear laser ultrasonic imaging of fatigue cracks with micron-scale width using multivariate phase space reconstruction and chaotic features10.1016/j.measurement.2024.116529 ↗
4. Deep learning enables nonlinear lamb waves for precise location of fatigue crack10.1177/14759217231167076 ↗
5. An enhanced ultrasonic method for monitoring and predicting stress loss in multi-layer structures via vibro-acoustic modulation10.1016/j.ymssp.2023.110984 ↗
6. Local wavenumber imaging with low-sampled wavefield for delamination characterization in composite plate10.1016/j.ndteint.2023.102867 ↗
7. Characterization of guided wave in thermal protection system and debonding detection using laser-generated guided wave10.1016/j.ndteint.2025.103383 ↗
8. Delamination detection in composite materials using laser-generated lamb waves with thickness-adaptive wavenumber filtering and broadband scale-adaptive 2D wavelet transform10.1016/j.ymssp.2025.112997 ↗
9. Noncontact evaluation of steel mechanical properties using nonlinear laser ultrasonics10.1016/j.ndteint.2025.103445 ↗
10. Laser ultrasonic frequency-domain imaging and phase weighted optimization based on full matrix capture10.1016/j.ultras.2024.107321 ↗
11. Fatigue damage characterization for composite laminates using deep learning and laser ultrasonic10.1016/j.compositesb.2021.108816 ↗
12. Guided wave imaging for detection and evaluation of impact-induced delamination in composites10.1088/0964-1726/24/10/105019 ↗
13. Evaluation of disbonds in adhesively bonded multilayer plates through local wavenumber estimation10.1016/j.jsv.2021.116624 ↗
14. Quantitative detection of surface defect using laser-generated Rayleigh wave with broadband local wavenumber estimation10.1016/j.ultras.2023.106983 ↗
15. Enhanced damage detection by dominant wavenumber filtering in steady-state ultrasonic wavefield imaging10.1016/j.ymssp.2024.111814 ↗
16. Multi-frequency localized wave energy for delamination identification using laser ultrasonic guided wave10.1016/j.ultras.2021.106486 ↗
17. Characterization of impact damage in composite laminates using guided wavefield imaging and local wavenumber domain analysis10.1016/j.ultras.2012.12.015 ↗
18. Structural imaging through local wavenumber estimation of guided waves10.1016/j.ndteint.2013.04.003 ↗
19. Crack imaging and quantification in aluminum plates with guided wave wavenumber analysis methods10.1016/j.ultras.2015.05.019 ↗
20. Instantaneous and local wavenumber estimations for damage quantification in composites10.1177/1475921714560073 ↗
21. Damage quantification in an aluminium-CFRP composite structure using guided wave wavenumber mapping: comparison of instantaneous and local wavenumber analyses10.1016/j.ndteint.2021.102472 ↗
22. Local wavenumber imaging in anisotropic structures10.1016/j.ymssp.2024.112102 ↗
23. Dispersion curve regression–assisted wideband local wavenumber analysis for characterizing three-dimensional (3D) profile of hidden corrosion damage10.1016/j.ymssp.2020.107347 ↗
24. Improving efficiency of local wavenumber estimation for damage detection in thin-walled structures10.1016/j.ymssp.2023.110470 ↗
25. Laser-generated guided waves for damage detection in metal-lined composite-overwrapped pressure vessels10.3390/polym14183823 ↗
26. Local wavenumber estimation for small damages based on artificial neural network10.1016/j.ndteint.2023.102866 ↗
27. Multi-view K-means clustering algorithm based on redundant and sparse feature learning10.1016/j.physa.2023.129405 ↗
28. An effective and efficient algorithm for K-means clustering with new formulation10.1109/TKDE.2022.3155450 ↗
29. Max-min K-means clustering algorithm and application in response signal feature extraction10.3233/JAE-2012-1534 ↗
30. K-means aided broadband ultrasonic guided wavefield processing for damage detection in composite panels10.1016/j.ymssp.2025.112696 ↗
31. A novel method for damage identification in cantilever beams using generalized energy index and deep learning model10.1016/j.measurement.2025.117591 ↗
32. Evaluating the thickness of thin-walled structures from ultrasound signals based on CNN-BILSTM model
33. Sensitivity kernels of guided wave velocities in composites10.1016/j.euromechsol.2022.104767 ↗
34. Local wavenumber method for delamination characterization in composites with sparse representation of Lamb waves10.1109/TUFFC.2020.3022880 ↗
35. Damage visualization using laser-generated residual guided waves with optimization of laser scanning path10.1016/j.ymssp.2021.108463 ↗