Data-driven deep neural network for structural damage detection in composite solar arrays on flexible spacecraft

Data-driven deep neural network for structural damage detection in composite solar arrays on flexible spacecraft

Federica Angeletti, Paolo Gasbarri, Marco Sabatini

Abstract. A data-driven approach based on Deep Neural Network (DNN) techniques is here proposed for Structural Health Monitoring of large in-orbit flexible systems. Damage scenarios are generated via a Finite Element commercial code to train and test the machine learning model, by considering equivalent properties of the composite material of the solar panels. The fully coupled 3D equations for the flexible spacecraft are integrated to test typical profiles of attitude manoeuvres in case of different damages. The DNN model is trained using sensor-measured time series responses, with each response associated with the label of the corresponding damage scenario, and tested via k-folding approach. This methodology offers a promising approach to detect structural damage in solar arrays on spacecraft using machine learning techniques.

Keywords
Structural Health Monitoring, Deep Learning, Flexible Spacecraft

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: Federica Angeletti, Paolo Gasbarri, Marco Sabatini, Data-driven deep neural network for structural damage detection in composite solar arrays on flexible spacecraft, Materials Research Proceedings, Vol. 37, pp 368-372, 2023

DOI: https://doi.org/10.21741/9781644902813-81

The article was published as article 81 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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