Impedance-based Health Monitoring for Aeronautic Structures using Statistical Meta-modeling

In this work, techniques of impedance-based structural health monitoring are applied to aeronautical structures using statistical meta-modeling methods. First, a procedure is developed to find the best test conditions through factorial designs. Also, Taguchi robustness techniques are used to reduce noise influence in damage detection processes. Further, based on meta-models, a procedure is developed for damage identification and characterization, as applied to a vertical fin of an unmanned aerial vehicle (UAV). Structural changes are obtained by using localized adding masses at several determined points along the structure. Finally, by using two meta-models, namely a probabilistic neural network model and a surface response model, it is possible to identify as well as to characterize damage in the structure.