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Gaussian Process NARX Model for Damage Detection in Composite Aircraft Structures

Article dans une revue avec comité de lecture
Author
DA SILVA, Samuel
225522 Universidade Estadual Paulista Júlio de Mesquita Filho = São Paulo State University [UNESP]
VILLANI, Luis G. G.
1001219 Universidade Federal do Espirito Santo [UFES]
ccRÉBILLAT, Marc
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
ccMECHBAL, Nazih
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/21977
DOI
10.1115/1.4052956
Date
2021-12
Journal
Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

Abstract

This article demonstrates the Gaussian process regression model’s applicability combined with a nonlinear autoregressive exogenous (NARX) framework using experimental data measured with PZTs’ patches bonded in a composite aeronautical structure for concerning a novel structural health monitoring (SHM) strategy. A stiffened carbon-epoxy plate regarding a healthy condition and simulated damage on the center of the bottom part of the stiffener is utilized. Comparing the performance in terms of simulation errors is made to observe if the identified models can represent and predict the waveform with confidence bounds considering the confounding effect produced by noise or possible temperature variations assuming a dataset preprocessed using principal component analysis. The results of the GP-NARX identified model have attested correct classification with a reduced number of false alarms, even with model uncertainties propagation regarding healthy and damaged conditions.

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