Due to the stringent requirements imposed by state-of-the-art technologies, most of modern spacecrafts are now equipped with very large substructures such as antennas, deployable booms and solar arrays. However, while the size of these elements increases, their mass is limited by the rocket maximum take-off weight and, therefore, they result to be lightweight and very flexible. A natural concern derived from this trend is that these structures are now more susceptible to structural damages during launch phase or operational life (impacts, transient thermal states and fatigue). Since the degradation of some structural elements would naturally result in a modification of the system dynamical behaviour, damage detection processes are usually performed by comparing the dynamical responses or the structural model matrices (i.e. stiffness, mass and damping) of the undamaged model with those of the damaged structure. However, for large structures, the presence of local damage does not generally induce substantial change in the global dynamic which makes the local failures difficult to detect. A methodology for structural damage detection based on data-driven techniques with Deep Neural Network (DNN) is hereby proposed for the study of a large in-orbit flexible system. The deep architecture evaluated in this work is composed by stacking several neural network layers with different functions. In order to generate data for training and testing the machine learning model, different damage scenarios are generated via Finite Element commercial code and, based on the extracted modal parameters, the fully coupled 3D equations for the flexible spacecraft are integrated to test typical profiles of attitude manoeuvres. The DNN model is trained using the sensor-measured time series responses, where each of the former has been associated with the label of the corresponding damage scenario.

Deep learning-based structural health monitoring for damage detection on a large space antenna / Iannelli, P.; Angeletti, F.; Gasbarri, P.; Panella, M.; Rosato, A.. - In: ACTA ASTRONAUTICA. - ISSN 0094-5765. - 193:(2022), pp. 635-643. [10.1016/j.actaastro.2021.08.003]

Deep learning-based structural health monitoring for damage detection on a large space antenna

Iannelli P.
;
Angeletti F.;Gasbarri P.;Panella M.;Rosato A.
2022

Abstract

Due to the stringent requirements imposed by state-of-the-art technologies, most of modern spacecrafts are now equipped with very large substructures such as antennas, deployable booms and solar arrays. However, while the size of these elements increases, their mass is limited by the rocket maximum take-off weight and, therefore, they result to be lightweight and very flexible. A natural concern derived from this trend is that these structures are now more susceptible to structural damages during launch phase or operational life (impacts, transient thermal states and fatigue). Since the degradation of some structural elements would naturally result in a modification of the system dynamical behaviour, damage detection processes are usually performed by comparing the dynamical responses or the structural model matrices (i.e. stiffness, mass and damping) of the undamaged model with those of the damaged structure. However, for large structures, the presence of local damage does not generally induce substantial change in the global dynamic which makes the local failures difficult to detect. A methodology for structural damage detection based on data-driven techniques with Deep Neural Network (DNN) is hereby proposed for the study of a large in-orbit flexible system. The deep architecture evaluated in this work is composed by stacking several neural network layers with different functions. In order to generate data for training and testing the machine learning model, different damage scenarios are generated via Finite Element commercial code and, based on the extracted modal parameters, the fully coupled 3D equations for the flexible spacecraft are integrated to test typical profiles of attitude manoeuvres. The DNN model is trained using the sensor-measured time series responses, where each of the former has been associated with the label of the corresponding damage scenario.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1574833
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