A time-frequency energy segmentation reconstruction method for multimodal ultrasonic guided waves

Abstract

Multimodal ultrasonic guided wave (UGW) signal reconstruction technology can accurately separate individual modes, providing more comprehensive and precise information for material nondestructive testing. However, the accuracy of existing reconstruction techniques heavily depends on the precision and completeness of time–frequency (TF) ridge extraction. To address this challenge, this paper proposes a TF energy segmentation reconstruction method without relying on complete TF ridge extraction, as traditionally required. This approach introduces an adaptive noise variance estimation Bayesian filter to extract the TF ridges under unknown noise distribution, particularly in regions where TF ridges intersect or overlap. By using the extracted TF ridges as references, the energy segmentation method directly separates and reconstructs UGW modes from the TF representation even when the extracted TF ridges are incomplete. This is because the proposed method can automatically retrieve the energy of each mode with a region growing algorithm from the time domain and frequency domain so that both modes with rapidly changing instantaneous frequency or group delay can be recovered, while the traditional method can only separate modes from a single domain. Numerical simulations and photoacoustic-guided wave experiments validate the effectiveness of the proposed method, achieving reconstruction accuracies of 96.9% and 92.5% for the simulated and experimental signals, respectively.