Training Feedforward Neural Networks for Structural Health Monitoring of an Aircraft Structure

Abstract

In the past decades, structural health monitoring (SHM) has become an emerging research area for globally monitoring expensive aircraft and bridge structures. This paper presents the application of stochastic fractal search (SFS) algorithm and its chaotic-enhanced variants to train feedforward neural networks (FNNs) for monitoring an aircraft structure based on vibration data. An experimental spectral testing was carried out to obtain the normal and damaged condition data of a laboratory stiffened panel structure which imitated wingbox of an aircraft. Added mass as pseudo-fault was employed to simulate damage condition with three different damage levels at three different locations. Vibration signature features were generated based on measured frequency response functions (FRFs) and principle component analysis (PCA). Then, metaheuristic-based FNNs approach were applied to localize and predict the severity of damage on the structure. The results reveal that the proposed approach produces high classification and localization accuracy as parameters of the FNNs were optimized systematically using metaheuristic algorithms. In conclusion, the Sine chaos-enhanced SFS algorithm highlights better convergence performance and results accuracy compared to other contested metaheuristic approaches.