Development of a Surrogate Model for Structural Health Monitoring of a UAV Wing Spar

A critical part to implementing a structural health monitoring system is being able to understand the structural response under different operational and environmental conditions. In this work, a detailed finite element model of an unmanned aerial vehicle’s wings’ spar was developed to serve as a synthetic data generator. A probabilistic understanding of the aerodynamic loads and debonding damages at different locations and with different sizes were implemented to simulate observations of the spar’s performance in service. The target measurements are uniaxial strain, measured in several paths throughout the spar. Given measured strain, the damage assessment problem is probabilistically formulated by defining local buckling from debonding as the observable damage, which is fundamentally characterized by load-dependent buckling eigenvalues. This FE physical model is highly computationally intensive, so a Gaussian process regressor and a multilayer artificial neural network (MANN) were designed to serve as a “run time” surrogate model to learn the relationships between inputs (loads and damage conditions) and outputs (strain measurements and buckling eigenvalues). The results illustrate that the surrogate models presented are a reliable replacement to the computationally expensive inverse finite element model in damage identification.