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Communication Dans Un Congrès Année : 2022

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

Résumé

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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Dates et versions

hal-03990259 , version 1 (15-02-2023)

Identifiants

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Hadrien Postorino, Eric Monteiro, Marc Rebillat, Nazih Mechbal. Experimental Damage Localization and Quantification with a Numerically Trained Convolutional Neural Network. EWSHM2022 ( European Workshop on Structural Health Monitoring) 4-7 July 2022, Jul 2022, Palerme, Italy. pp.401-407, ⟨10.1007/978-3-031-07322-9_41⟩. ⟨hal-03990259⟩
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