Department of Electronic Engineering, Fudan University, 200433 Shanghai, China.
Department of Electronic Engineering, Fudan University, 200433 Shanghai, China.
Ultrasonics. 2019 Nov;99:105948. doi: 10.1016/j.ultras.2019.105948. Epub 2019 Jun 20.
Multimodal and dispersive characteristics of ultrasonic guided waves (GWs) cause the wave-packet overlapping in time domain and frequency domain, which challenges the signal interpretation. In this study, we propose an automatic method for individual mode extraction. The inversible synchrosqueezed wavelet transform (SWT) is employed to obtain the high-resolution time-frequency representation (TFR) of the GW signal. Then, two image processing steps, i.e., watershed transform and region growing, are used to process the TFR distributions and extract the TFR trajectory of each individual component. After the TFR segmentation, the individual modes are reconstructed by using the inverse SWT. The algorithm performance is investigated by synthesized multimodal signals. The results show that the reconstructed individual modes are consistent with the original ones. The experimental results measured in a bovine tibia plate and a steel plate are further employed to testify the proposed algorithm. Results suggest that the presented study provides a robust tool for processing multimodal ultrasonic GW signals.
超声导波（GWs）的多模态和弥散特性导致在时域和频域上的波包重叠，这给信号解释带来了挑战。在本研究中，我们提出了一种用于提取各个模态的自动方法。采用可逆同步挤压小波变换（SWT）获得 GW 信号的高分辨率时频表示（TFR）。然后，使用两个图像处理步骤，即分水岭变换和区域生长，处理 TFR 分布并提取每个单独分量的 TFR 轨迹。在 TFR 分割后，通过使用逆 SWT 来重建各个模态。通过合成多模态信号来研究算法性能。结果表明，重建的各个模态与原始模态一致。进一步采用牛胫骨板和钢板上测量的实验结果来验证所提出的算法。结果表明，所提出的研究为处理多模态超声 GW 信号提供了一种强大的工具。
