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Multi-Damage Detection in Composite Space Structures via Deep Learning

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance d...

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Detalles Bibliográficos
Autores principales: Angeletti, Federica, Gasbarri, Paolo, Panella, Massimo, Rosato, Antonello
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490817/
https://www.ncbi.nlm.nih.gov/pubmed/37687970
http://dx.doi.org/10.3390/s23177515
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author Angeletti, Federica
Gasbarri, Paolo
Panella, Massimo
Rosato, Antonello
author_facet Angeletti, Federica
Gasbarri, Paolo
Panella, Massimo
Rosato, Antonello
author_sort Angeletti, Federica
collection PubMed
description The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples.
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spelling pubmed-104908172023-09-09 Multi-Damage Detection in Composite Space Structures via Deep Learning Angeletti, Federica Gasbarri, Paolo Panella, Massimo Rosato, Antonello Sensors (Basel) Article The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples. MDPI 2023-08-29 /pmc/articles/PMC10490817/ /pubmed/37687970 http://dx.doi.org/10.3390/s23177515 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Angeletti, Federica
Gasbarri, Paolo
Panella, Massimo
Rosato, Antonello
Multi-Damage Detection in Composite Space Structures via Deep Learning
title Multi-Damage Detection in Composite Space Structures via Deep Learning
title_full Multi-Damage Detection in Composite Space Structures via Deep Learning
title_fullStr Multi-Damage Detection in Composite Space Structures via Deep Learning
title_full_unstemmed Multi-Damage Detection in Composite Space Structures via Deep Learning
title_short Multi-Damage Detection in Composite Space Structures via Deep Learning
title_sort multi-damage detection in composite space structures via deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490817/
https://www.ncbi.nlm.nih.gov/pubmed/37687970
http://dx.doi.org/10.3390/s23177515
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