Study on the Generalization of a Data-Driven Methodology for Damage Detection in an Aircraft Wing Using Reduced FE Models

This work investigates a data-driven approach for detecting structural damage in the wing of a Cessna 172 aircraft using reduced-order finite element (FE) models. This study focuses on the ability of machine learning methods to generalize across different structural conditions, aiming to support reliable Structural Health Monitoring (SHM) in aeronautical applications. The wing was first modeled in detail using the FiniteElement Method, followed by the development of a simplified FE model to reduce computational cost while maintaining accuracy. The similarity between the two models was evaluated through modal analysis and the Modal Assurance Criterion (MAC). Dynamic excitation representing turbulence effects was applied to simulate healthy and damaged conditions, producing acceleration data used to train one-dimensional and two-dimensional neural network classifiers. The 1D models processed raw vibration signals, while the 2D models used image representations of the same data. Both architectures were tested against results from the detailed FE model to assess their generalization capability. The 2D networks achieved higher classification accuracy, demonstrating improved robustness in identifying both minor and severe damage. The findings highlight the potential of combining reduced FE models with data-driven methods for efficient and accurate aircraft wing damage detection.