The present work introduces two unsupervised data-driven methodologies for processing Lamb waves (LWs) to localize structural damage, specifically employing convolutional autoencoders (CAEs) and conditional generative adversarial networks (CGANs). Both techniques are capable of processing diagnostic signals without the need for any prior feature extraction. Once all signals are processed, a damage probability map is generated. The performance of the methods was tested using two different experimental datasets. The first derives from LWs obtained from a set of piezoelectric transducers mounted on two different composite panels, made of two different layups. Pseudo-damage and real damage were considered. The second dataset derives from LWs acquired on a full-scale composite wing, where damage was introduced through impacts performed using an air-gun. The results of this study revealed that the proposed unsupervised methods are capable of localizing damage properly, with comparable accuracy.

中文翻译：

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用于无监督结构损伤定位的卷积自动编码器和 CGAN


目前的工作介绍了两种无监督数据驱动的方法来处理兰姆波（LW）以定位结构损伤，特别是采用卷积自动编码器（CAE）和条件生成对抗网络（CGAN）。这两种技术都能够处理诊断信号，而不需要任何事先的特征提取。一旦处理完所有信号，就会生成损坏概率图。使用两个不同的实验数据集测试了该方法的性能。第一个源自安装在由两种不同叠层制成的两个不同复合板上的一组压电换能器获得的 LW。考虑了伪损坏和真实损坏。第二个数据集源自在全尺寸复合材料机翼上获取的 LW，其中损坏是通过使用气枪进行的撞击造成的。这项研究的结果表明，所提出的无监督方法能够以相当的精度正确定位损坏。