Gaussian Process NARX Model for Damage Detection in Composite Aircraft Structures

• Autor(a) principal: Da Silva, Samuel [UNESP]
• Data de Publicação: 2022
• Outros Autores: Villani, Luis G.G., Rébillat, Marc, Mechbal, Nazih
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1115/1.4052956; http://hdl.handle.net/11449/230645
• topic: composite structures; damage classification; diagnostic decision support; diagnostic feature extraction; Gaussian process; guided wave propagation; NARX model; nonlinear damage; prognosis; propagation of uncertainties; stiffener debonding; structural engineering; testing methodologies; wave propagation modeling

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.