SHM implementation on a RPV airplane model based on machine learning for impact detection

Abstract

Abstract. In this work an on-working Structural Health Monitoring system for impact detection on RC airplane is proposed. The method is based on the propagation of Lamb waves in a metallic structure on which PZT sensors are bonded for receiving the corresponding signals. After the detection, Machine Learning algorithms (polynomial regression and neural networks) are applied to the data obtained by the processing of the acquired ultrasounds in order to characterize the impacts. Furthermore, this work presents the development of a mini-equipment for acquisition and data processing based on a Raspberry Pi micro-computer.