Damage characterization using CNN and SAE of broadband Lamb waves

The method based on Lamb wave shows great potential for structural health monitoring (SHM) and nondestructive testing (NDT). Deep learning algorithms including convolutional neural networks (CNN) and stack autoencoder (SAE) are promising to extract features from Lamb wave signals that can be linked with damage for subsequent localization and quantification. Generally, narrowband Lamb wave with purified mode and suppressed dispersion is used because of clear relationship model between damage features and recorded signals. However, model performance is limited because contained damage information of narrowband Lamb wave is inadequate. To overcome this limitation, a broadband Lamb wave deep learning algorithm is proposed for damage localization and quantification. Compared with narrowband, broadband Lamb wave generated at a large frequency range contains richer information of structural damage. In this study, different mode selections, different signal processing methods and different deep learning algorithms are applied to extract damage features from different perspectives, and fusion of all extraction results facilitates the full utilization of rich broadband information. An experiment is given to demonstrate the effectiveness and high-accuracy of proposed method.