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

G. Scarselli; F. Dipietrangelo; F. Nicassio

doi:10.21741/9781644902813-33

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

G. Scarselli, F. Dipietrangelo, F. Nicassio

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.

Keywords
Lamb Waves, RC Airplane, Impact Detection, Machine Learning

Published online 11/1/2023, 5 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: G. Scarselli, F. Dipietrangelo, F. Nicassio, SHM implementation on a RPV airplane model based on machine learning for impact detection, Materials Research Proceedings, Vol. 37, pp 150-154, 2023

DOI: https://doi.org/10.21741/9781644902813-33

The article was published as article 33 of the book Aeronautics and Astronautics

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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