Lamb wave-based damage imaging of CFRP composite structures using autoencoder and delay-and-sum

Ultrasonic guided wave is a promising technique for structural health monitoring and nondestructive testing. However, due to the anisotropy and complexity of composite materials, the imaging performance of numerous signal processing methods deteriorates with significant artifacts and unsatisfactory...

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Veröffentlicht in:Composite structures Jg. 303; S. 116263
Hauptverfasser: Yu, Yinghong, Liu, Xiao, Wang, Yihan, Wang, Yishou, Qing, Xinlin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.01.2023
Schlagworte:
Composite structures
Damage imaging
Delay-and-sum
Denoising autoencoder
Ultrasonic guided wave
Denoising autoencoder
Delay-and-sum
Composite structures
Ultrasonic guided wave
Damage imaging
ISSN:0263-8223
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Zusammenfassung:Ultrasonic guided wave is a promising technique for structural health monitoring and nondestructive testing. However, due to the anisotropy and complexity of composite materials, the imaging performance of numerous signal processing methods deteriorates with significant artifacts and unsatisfactory accuracy. To obtain a better damage imaging performance of Lamb waves in noisy and noise-free conditions, a weighted delay-and-sum (DAS) imaging method based on denoising autoencoder (DAE) learning is developed for complex composite structures. The traditional DAS formulation is modified to be more compatible with anisotropic materials. The DAE with feature learning capability is then employed to extract potentially efficient features and remove noise from ultrasonic signals. Several verification experiments conducted on flat or curved and stiffened composite structures have confirmed the ability of the DAE-DAS method to suppress artificial noise and to intensify singularities induced by the anomalies. By comparing with the unweighted DAS methods and the weighted DAS method without feature extraction, the proposed algorithm has satisfactory robustness to achieve higher localization accuracy and fewer artifacts.
ISSN:0263-8223
DOI:10.1016/j.compstruct.2022.116263