基于深度学习的导波特征提取及其激光超声检测

针对激光超声检测中波场的三维数据处理计算量大且损伤特征提取难的问题,提出了一种基于深度学习模型的导波 波场分析方法. 首先,以 VGG-Net 网络为框架,建立了基于 VGG11(A-LRN)的残差网络模型,用于挖掘时间-空间波场数据中的 导波特征;其次,以局部波数特征为物理机理,采用导波传播的解析式生成训练样本,解决了深度学习大数据获取的问题,获得 了波场特征提取的神经网络模型;最后,以激光超声系统在含损伤结构中的实验数据作为测试样本,验证了所提出的网络模型 能够提取表征损伤的导波特征,实现了结构的损伤成像,其损伤成像精度均在 67% 以上,损伤形貌的可视化效果好。

Guided wave feature extraction based on deep learning with its laser ultrasonic detection

To address the problems of the expensive computing cost for the wave field data processing and the difficulty for damage feature extraction in laser ultrasonic detection, a guided wave field analysis method based on deep learning is proposed. First, under the framework of VGG-Net, a residual network based on VGG11 is developed for extracting guided wave features from time-space wave field data. Then, taking the local wavenumber characteristic as the physical mechanism of the model, the problem of obtaining big data for training deep learning model can be solved by using the analytic formula of guided wave propagation. Therefore, the neural network can be obtained for extracting guided wave feature. Finally, using the experimental data in the plate structure with damage through laser ultrasonic system as test samples, the capability of guided wave feature extraction and damage identification using the proposed method is validated. The damage identification accuracy is above 67% and the shape of structural damage can be visualized.