Impact characterization on RC airplane model in operation using machine learning

Abstract

Structural Health Monitoring represents a growing field of great interest for aerospace engineering. This manuscript proposes an on-working SHM method for impact detection on RC airplane by ultrasounds, that is based on Machine Learning algorithms (polynomial regression and neural networks) and is useful to establish critical and dangerous operational conditions. The proposed method can be used to detect impact events both in metallic or composite structures, it is specifically designed to be used on typical fuselage and wing panels and is based on the propagation of Lamb waves in the structure on which PZT sensors are bonded for receiving signals. Algorithms are implemented in order to evaluate the impact location by post-processing the acquired signals. Several test cases are numerically studied before being tested in laboratory and reproduced on-working conditions. A good agreement between the numerical, laboratory and in-flight results is achieved.