{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T22:39:21Z","timestamp":1788907161588,"version":"build-2803163510"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Most modern Earth and Universe observation spacecraft are now equipped with large lightweight and flexible structures, such as antennas, telescopes, and extendable elements. The trend of hosting more complex and bigger appendages, essential for high-precision scientific applications, made orbiting satellites more susceptible to performance loss or degradation due to structural damages. In this scenario, Structural Health Monitoring strategies can be used to evaluate the health status of satellite substructures. However, in particular when analysing large appendages, traditional approaches may not be sufficient to identify local damages, as they will generally induce less observable changes in the system dynamics yet cause a relevant loss of payload data and information. This paper proposes a deep neural network to detect failures and investigate sensor sensitivity to damage classification for an orbiting satellite hosting a distributed network of accelerometers on a large mesh reflector antenna. The sensors-acquired time series are generated by using a fully coupled 3D simulator of the in-orbit attitude behaviour of a flexible satellite, whose appendages are modelled by using finite element techniques. The machine learning architecture is then trained and tested by using the sensors\u2019 responses gathered in a composite scenario, including not only the complete failure of a structural element (structural break) but also an intermediate level of structural damage. The proposed deep learning framework and sensors configuration proved to accurately detect failures in the most critical area or the structure while opening new investigation possibilities regarding geometrical properties and sensor distribution.<\/jats:p>","DOI":"10.3390\/s23010368","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:19:46Z","timestamp":1672370386000},"page":"368","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Study on Structural Health Monitoring of a Large Space Antenna via Distributed Sensors and Deep Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Federica","family":"Angeletti","sequence":"first","affiliation":[{"name":"School of Aerospace Engineering, Sapienza University of Rome, Via Salaria 851, 00138 Rome, Italy"},{"name":"Since February 1st 2022\u2014OHB System AG, Manfred-Fuchs-Stra\u00dfe 1, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Iannelli","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering (DIMA), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8894-6612","authenticated-orcid":false,"given":"Paolo","family":"Gasbarri","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Sapienza University of Rome, Via Salaria 851, 00138 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9876-1494","authenticated-orcid":false,"given":"Massimo","family":"Panella","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonello","family":"Rosato","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ketner, G.L. (1989). Survey of Historical Incidences with Controls-Structures Interaction and Recommended Technology Improvements Needed to Put Hardware in Space, Pacific Northwest Lab. Technical Report.","DOI":"10.2172\/6179780"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.actaastro.2013.10.015","article-title":"Modeling and analysis of a large deployable antenna structure","volume":"95","author":"Chu","year":"2014","journal-title":"Acta Astronaut."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.2514\/1.A33127","article-title":"Design and Deployment Analysis of Modular Deployable Structure for Large Antennas","volume":"52","author":"Wang","year":"2015","journal-title":"J. Spacecr. Rocket."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Angeletti, F., Gasbarri, P., Palmerini, G., and Sabatini, M. (2018, January 3\u201310). Learning-based control scheme to deploy modular space structures. Proceedings of the 2018 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2018.8396782"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kellogg, K.H., Thurman, S.W., Edelstein, W.N., Spencer, M.W., Chen, G.S., Underwood, M.L., Njoku, E.G., Goodman, S.D., and Jai, B. (2013, January 2\u20139). NASA\u2019s Soil Moisture Active Passive (SMAP) observatory. Proceedings of the 2013 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2013.6496938"},{"key":"ref_6","unstructured":"Council, N.R. (2007). Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, The National Academies Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e2021JC017610","DOI":"10.1029\/2021JC017610","article-title":"Ocean and Sea Ice Retrievals From an End-To-End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4\u201336.5 GHz Measurements","volume":"126","author":"Tenerelli","year":"2021","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.actaastro.2022.07.024","article-title":"A model predictive control for attitude stabilization and spin control of a spacecraft with a flexible rotating payload","volume":"199","author":"Iannelli","year":"2022","journal-title":"Acta Astronaut."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.actaastro.2021.08.003","article-title":"Deep learning-based Structural Health Monitoring for damage detection on a large space antenna","volume":"193","author":"Iannelli","year":"2022","journal-title":"Acta Astronaut."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1177\/0583102405052561","article-title":"Embedded non-destructive evaluation for structural health monitoring, damage detection, and failure prevention","volume":"37","author":"Giurgiutiu","year":"2005","journal-title":"Shock Vib. Dig."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"57","DOI":"10.4028\/www.scientific.net\/KEM.347.57","article-title":"Structural Analysis Methods for Structural Health Management of Future Aerospace Vehicles","volume":"347","author":"Tessler","year":"2007","journal-title":"Key Eng. Mater."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1177\/147592170200100104","article-title":"Piezoelectric Wafer Embedded Active Sensors for Aging Aircraft Structural Health Monitoring","volume":"1","author":"Giurgiutiu","year":"2002","journal-title":"Struct. Health Monit."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, J.S., Vinoy, K.J., and Varadan, V.K. (2002, January 18\u201321). Wireless health monitoring of cracks in structures with MEMS-IDT sensors. Proceedings of the SPIE\u2019s 9th Annual International Symposium on Smart Structures and Materials, San Diego, CA, USA.","DOI":"10.1117\/12.475048"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"589","DOI":"10.2514\/1.51818","article-title":"Application of System-Identification Techniquest to Health Monitoring of On-Orbit Satellite Boom Structures","volume":"48","author":"Liu","year":"2011","journal-title":"J. Spacecr. Rocket."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Haridas, A., and Speckmann, H. (2021). Structural Health Monitoring (SHM) of Space Structures, Materials Research Forum LLC.","DOI":"10.1007\/978-3-030-64594-6_39"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1002\/stc.1755","article-title":"Vision-based algorithms for damage detection and localization in structural health monitoring","volume":"23","author":"Dworakowski","year":"2016","journal-title":"Struct. Control. Health Monit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.measurement.2016.01.024","article-title":"Vision-based structural displacement measurement: System performance evaluation and influence factor analysis","volume":"88","author":"Ye","year":"2016","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e2321","DOI":"10.1002\/stc.2321","article-title":"A literature review of next-generation smart sensing technology in structural health monitoring","volume":"26","author":"Sony","year":"2019","journal-title":"Struct. Control. Health Monit."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Beitia, J., Loisel, P., and Fell, C. (2017, January 27\u201330). Miniature accelerometer for High-Dynamic, Precision Guided Systems. Proceedings of the 2017 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Kauai, HI, USA.","DOI":"10.1109\/ISISS.2017.7935652"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5130","DOI":"10.3182\/20110828-6-IT-1002.00302","article-title":"Robust Attitude Control Design for the BIOMASS Satellite (Earth Explorer Core Mission Candidate)","volume":"44","author":"Bennani","year":"2011","journal-title":"IFAC Proc. Vol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.ifacol.2022.09.328","article-title":"Robust Collocated Control of Large Flexible Space Structures","volume":"55","author":"Angeletti","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.actaastro.2022.05.058","article-title":"Active vibration control of large space structures: Modelling and experimental testing of offset piezoelectric stack actuators","volume":"198","author":"Callipari","year":"2022","journal-title":"Acta Astronaut."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1177\/1045389X06059077","article-title":"Structural Health Monitoring for Future Space Vehicles","volume":"17","author":"Mancini","year":"2006","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/978-3-030-64908-1_16","article-title":"Multiple Model Filtering for Failure Identification in Large Space Structures","volume":"128","author":"Palmerini","year":"2021","journal-title":"Lect. Notes Civ. Eng."},{"key":"ref_25","unstructured":"Tansel, I., Chen, P., Wang, X., Yenilmez, A., and Ozcelik, B. (2005, January 9\u201311). Structural health monitoring applications for space structures. Proceedings of the 2nd International Conference on Recent Advances in Space Technologies, Istanbul, Turkey."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Martins, B., and Kosmatka, J. (2019, January 7\u201311). In-Situ Health Monitoring of Aerospace Structures via Dynamic Strain Measurements. Proceedings of the AIAA Scitech 2019 Forum, San Diego, CA, USA.","DOI":"10.2514\/6.2019-1758"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106932","DOI":"10.1016\/j.ymssp.2020.106932","article-title":"An inverse damage location problem applied to AS-350 rotor blades using bat optimization algorithm and multiaxial vibration data","volume":"145","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_28","unstructured":"Siebel, T., and Majer, D. (2011, January 4\u20136). Damage Detection on a Truss Structure Using Transmissibility Functions. Proceedings of the EURODYN, Leuven, Belgium."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.compstruct.2005.11.007","article-title":"Application of eigenvalue perturbation theory for detecting small structural damage using dynamic responses","volume":"78","author":"Yu","year":"2007","journal-title":"Compos. Struct."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.4028\/www.scientific.net\/AMR.753-755.2347","article-title":"Structural Damage Diagnosis Using an Improved Eigenvalue Perturbation Method","volume":"753","author":"Li","year":"2013","journal-title":"Adv. Mater. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"061010","DOI":"10.1115\/1.4047302","article-title":"Higher-Order Stabilized Perturbation for Recursive Eigen-Decomposition Estimation","volume":"142","author":"Mucchielli","year":"2020","journal-title":"J. Vib. Acoust."},{"key":"ref_32","first-page":"147592171876048","article-title":"Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis","volume":"18","author":"Bhowmik","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_33","first-page":"392","article-title":"Singular spectrum analysis for enhancing the sensitivity in structural damage detection","volume":"333","author":"Kun","year":"2013","journal-title":"J. Sound Vib."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"402","DOI":"10.3397\/1\/376336","article-title":"Singular spectrum analysis based structural damage detection from nonlinear vibration measurements containing noise","volume":"63","author":"Liu","year":"2015","journal-title":"Noise Control. Eng. J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Roemer, M., Ge, J., Liberson, A., Tandon, G., and Kim, R. (2005, January 5\u201312). Autonomous impact damage detection and isolation prediction for aerospace structures. Proceedings of the 2005 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2005.1559664"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"266","DOI":"10.14525\/JJCE.10.1.3542","article-title":"Damage identification in truss structures using finite element model updating and imperialist competitive algorithm","volume":"10","author":"Ghaffarzadeh","year":"2016","journal-title":"Jordan J. Civ. Eng."},{"key":"ref_37","unstructured":"Ramakrishnan, S. (2016). Data-Driven Methodologies for Structural Damage Detection Based on Machine Learning Applications. Pattern Recognition, IntechOpen. Chapter 6."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cross, E.J., Gibson, S.J., Jones, M.R., Pitchforth, D.J., Zhang, S., and Rogers, T.J. (2021). Physics-Informed Machine Learning for Structural Health Monitoring. Structural Integrity, Springer International Publishing.","DOI":"10.1007\/978-3-030-81716-9_17"},{"key":"ref_39","unstructured":"Voss, S. (2019). Application of Deep Learning for Spacecraft Fault Detection and Isolation, TU Delft Aerospace Engineering."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7092","DOI":"10.1109\/TIA.2019.2916758","article-title":"A Neural Network Based Prediction System of Distributed Generation for the Management of Microgrids","volume":"55","author":"Rosato","year":"2019","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s42496-019-00028-z","article-title":"A Deep Learning Strategy For On-Orbit Servicing Via Space Robotic Manipulator","volume":"98","author":"Stolfi","year":"2019","journal-title":"Aerotec. Missili Spaz."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"211490","DOI":"10.1109\/ACCESS.2020.3039733","article-title":"Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series","volume":"8","author":"Succetti","year":"2020","journal-title":"IEEE Access"},{"key":"ref_43","first-page":"1462","article-title":"DRAW: A Recurrent Neural Network For Image Generation","volume":"Volume 37","author":"Bach","year":"2015","journal-title":"Proceedings of the 32nd International Conference on Machine Learning"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.actaastro.2012.08.018","article-title":"Effects of uncertainties and flexible dynamic contributions on the control of a spacecraft full-coupled model","volume":"94","author":"Gasbarri","year":"2014","journal-title":"Acta Astronaut."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.actaastro.2019.07.028","article-title":"Optimal design and robust analysis of a net of active devices for micro-vibration control of an on-orbit large space antenna","volume":"164","author":"Angeletti","year":"2019","journal-title":"Acta Astronaut."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mazzini, L. (2016). Flexible Spacecraft Dynamics, Control and Guidance, Springer Aerospace Technology, Springer. [1st ed.].","DOI":"10.1007\/978-3-319-25540-8"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.actaastro.2021.04.007","article-title":"End-to-end design of a robust attitude control and vibration suppression system for large space smart structures","volume":"187","author":"Angeletti","year":"2021","journal-title":"Acta Astronaut."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ast.2015.01.002","article-title":"Modal behavior of a new large reflector conceptual design","volume":"42","author":"Morterolle","year":"2015","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"360","DOI":"10.2514\/3.19988","article-title":"Quaternion feedback for spacecraft large angle maneuvers","volume":"8","author":"Wie","year":"1985","journal-title":"J. Guid. Control. Dyn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"375","DOI":"10.2514\/3.20418","article-title":"Quarternion feedback regulator for spacecraft eigenaxis rotations","volume":"12","author":"Wie","year":"1989","journal-title":"J. Guid. Control. Dyn."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ceschini, A., Rosato, A., Succetti, F., Luzio, F.D., Mitolo, M., Araneo, R., and Panella, M. (2021, January 7\u201310). Deep Neural Networks for Electric Energy Theft and Anomaly Detection in the Distribution Grid. Proceedings of the 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC \/ I&CPS Europe), Bari, Italy.","DOI":"10.1109\/EEEIC\/ICPSEurope51590.2021.9584796"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values","volume":"8","author":"Che","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Namin, A.S. (2019, January 9\u201312). The Performance of LSTM and BiLSTM in Forecasting Time Series. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"114844","DOI":"10.1016\/j.eswa.2021.114844","article-title":"Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting","volume":"175","author":"Mughees","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_55","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/368\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:55:32Z","timestamp":1760147732000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/368"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,29]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010368"],"URL":"https:\/\/doi.org\/10.3390\/s23010368","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,29]]}}}