School of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, China; Advanced Manufacturing Center, Ningbo Institute of Technology, Beihang University, Ningbo 315100, China; Aero-engine Thermal Environment and Structure Key Laboratory of Ministry of Industry and Information Technology, Nanjing 210016, China.
Aero-engine Thermal Environment and Structure Key Laboratory of Ministry of Industry and Information Technology, Nanjing 210016, China; Science & Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, China.
Ultrasonics. 2022 Feb;119:106592. doi: 10.1016/j.ultras.2021.106592. Epub 2021 Sep 21.
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.
基于兰姆波的方法在结构健康监测（SHM）和无损检测（NDT）方面显示出巨大的潜力。深度学习算法，包括卷积神经网络（CNN）和堆栈自动编码器（SAE），有望从兰姆波信号中提取特征，这些特征可以与损伤相关联，以便后续进行定位和定量。通常，由于损伤特征与记录信号之间存在明确的关系模型，因此使用窄带兰姆波，具有纯化模式和抑制色散。然而，由于窄带兰姆波包含的损伤信息不足，因此模型性能受到限制。为了克服这一限制，提出了一种用于损伤定位和定量的宽带兰姆波深度学习算法。与窄带相比，在较大频率范围内产生的宽带兰姆波包含更多的结构损伤信息。在这项研究中，从不同的角度应用不同的模式选择、不同的信号处理方法和不同的深度学习算法来提取损伤特征，融合所有提取结果有助于充分利用丰富的宽带信息。给出了一个实验来证明所提出方法的有效性和高精度。
