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Convolutional autoencoders and CGANs for unsupervised structural damage localization
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DOI：10.1016/j.ymssp.2024.111645.png)
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Abstract
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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.
Keywords:
Unsupervised deep learning
Convolutional autoencoder
Generative adversarial network
Damage localization
Lamb waves
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