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Experimental Damage Localization and Quantification with a Numerically Trained Convolutional Neural Network

Communication avec acte
Author
POSTORINO, Hadrien
ccMONTEIRO, Eric
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
ccRÉBILLAT, Marc
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
ccMECHBAL, Nazih

URI
http://hdl.handle.net/10985/23342
DOI
10.1007/978-3-031-07322-9_41
Date
2022-06

Abstract

Structural Health Monitoring (SHM) based on Lamb wave propagation is a promising technology to optimize maintenance costs, enlarge service life and improve safety of aircrafts. A large quantity of data is collected during all the life cycle of the structure under monitoring and must be analysed in real time. We propose here to use 1D-CNN to estimate the severity and the localisation of a damage with the signals measured on a composite structure monitored with piezoelectric transducers (PZT). Two architectures have been tested: one takes for input the difference of the time signals of two different states and the second takes for in-puts temporal damage indexes. Those simple networks with a few layers predict with high precision the position and the severity of a damage in a composite plate. The evaluations on different cases show the robustness to simulated manufacturing uncertainties and noise. An evaluation on experimental measurement shows promising results to localise a damage on a real plate with a CNN trained with numerical data.

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