{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T15:25:59Z","timestamp":1787844359787,"version":"build-2784847793"},"reference-count":52,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T00:00:00Z","timestamp":1693267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2014NextGenerationEU","award":["CN00000023"],"award-info":[{"award-number":["CN00000023"]}]},{"name":"European Union\u2014NextGenerationEU","award":["1033\u201417\/06\/2022"],"award-info":[{"award-number":["1033\u201417\/06\/2022"]}]},{"name":"Italian Ministry of University and Research Decree","award":["CN00000023"],"award-info":[{"award-number":["CN00000023"]}]},{"name":"Italian Ministry of University and Research Decree","award":["1033\u201417\/06\/2022"],"award-info":[{"award-number":["1033\u201417\/06\/2022"]}]},{"name":"Spoke 11\u2014Innovative Materials &amp; Lightweighting","award":["CN00000023"],"award-info":[{"award-number":["CN00000023"]}]},{"name":"Spoke 11\u2014Innovative Materials &amp; Lightweighting","award":["1033\u201417\/06\/2022"],"award-info":[{"award-number":["1033\u201417\/06\/2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23177515","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T10:30:52Z","timestamp":1693391452000},"page":"7515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-Damage Detection in Composite Space Structures via 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"}],"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":[[2023,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.actaastro.2010.02.021","article-title":"A Review on Large Deployable Structures for Astrophysics Missions","volume":"67","author":"Puig","year":"2010","journal-title":"Acta Astronaut."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Belvin, K. (2004, January 28). Advances in Structures for Large Space Systems. Proceedings of the Space 2004 Conference and Exhibit, San Diego, CA, USA.","DOI":"10.2514\/6.2004-5898"},{"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","first-page":"434","DOI":"10.1016\/j.actaastro.2017.05.010","article-title":"A 1 cm Space Debris Impact onto the Sentinel-1A Solar Array","volume":"137","author":"Krag","year":"2017","journal-title":"Acta Astronaut."},{"key":"ref_5","unstructured":"(2023, July 17). Structural Health Monitoring for Future Space Vehicles\u2014Simone Mancini, Giorgio Tumino, Paolo Gaudenzi. Available online: https:\/\/journals.sagepub.com\/doi\/10.1177\/1045389X06059077."},{"key":"ref_6","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_7","unstructured":"(2023, July 17). Piezoelectric Wafer Embedded Active Sensors for Aging Aircraft Structural Health Monitoring\u2014Victor Giurgiutiu, Andrei Zagrai, Jing Jing Bao. Available online: https:\/\/journals.sagepub.com\/doi\/abs\/10.1177\/147592170200100104."},{"key":"ref_8","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_9","unstructured":"Tansel, I.N., 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, 2005. RAST 2005, Istanbul, Turkey."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ju, M., Dou, Z., Li, J.-W., Qiu, X., Shen, B., Zhang, D., Yao, F.-Z., Gong, W., and Wang, K. (2023). Piezoelectric Materials and Sensors for Structural Health Monitoring: Fundamental Aspects, Current Status, and Future Perspectives. Sensors, 23.","DOI":"10.3390\/s23010543"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qing, X., Li, W., Wang, Y., and Sun, H. (2019). Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications. Sensors, 19.","DOI":"10.3390\/s19030545"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.prostr.2020.01.046","article-title":"A Review of Structural Health Monitoring Methods for Composite Materials","volume":"22","author":"Metaxa","year":"2019","journal-title":"Procedia Struct. Integr."},{"key":"ref_13","unstructured":"Siebel, T., and Mayer, D. (2011, January 1). Damage Detection on a Truss Structure Using Transmissibility Functions. Proceedings of the 8th International Conference on Structural Dynamics, EURODYN 2011, Leuven, Belgium."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1061\/(ASCE)EM.1943-7889.0000426","article-title":"Damage Detection for Space Truss Structures Based on Strain Mode under Ambient Excitation","volume":"138","author":"Xu","year":"2012","journal-title":"J. Eng. Mech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16091","DOI":"10.1007\/s10853-022-07558-1","article-title":"Review of Advanced Techniques for Manufacturing Biocomposites: Non-Destructive Evaluation and Artificial Intelligence-Assisted Modeling","volume":"57","author":"Preethikaharshini","year":"2022","journal-title":"J. Mater. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/j.1475-1305.2000.tb01175.x","article-title":"Impact Location and Quantification on a Composite Panel Using Neural Networks and a Genetic Algorithm","volume":"36","author":"Worden","year":"2000","journal-title":"Strain"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.jsv.2006.07.019","article-title":"Impact Detection in an Aircraft Composite Panel\u2014A Neural-Network Approach","volume":"299","author":"LeClerc","year":"2007","journal-title":"J. Sound Vib."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105026","DOI":"10.1088\/0964-1726\/21\/10\/105026","article-title":"Determination of Impact Location on Composite Stiffened Panels","volume":"21","author":"Ghajari","year":"2012","journal-title":"Smart Mater. Struct."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep Learning for Time Series Classification: A Review","volume":"33","author":"Forestier","year":"2019","journal-title":"Data Min. Knowl. Disc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Azuara, G., Ruiz, M., and Barrera, E. (2021). Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks. Sensors, 21.","DOI":"10.3390\/s21175825"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.jsv.2016.10.043","article-title":"Real-Time Vibration-Based Structural Damage Detection Using One-Dimensional Convolutional Neural Networks","volume":"388","author":"Abdeljaber","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106790","DOI":"10.1016\/j.compstruc.2022.106790","article-title":"SHM under Varying Environmental Conditions: An Approach Based on Model Order Reduction and Deep Learning","volume":"266","author":"Torzoni","year":"2022","journal-title":"Comput. Struct."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shin, Y.-S., and Kim, J. (2023). Sensor Data Reconstruction for Dynamic Responses of Structures Using External Feedback of Recurrent Neural Network. Sensors, 23.","DOI":"10.3390\/s23052737"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Deng, F., Tao, X., Wei, P., and Wei, S. (2023). A Robust Deep Learning-Based Damage Identification Approach for SHM Considering Missing Data. Appl. Sci., 13.","DOI":"10.3390\/app13095421"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1109\/TASE.2020.3034401","article-title":"Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning","volume":"18","author":"Dang","year":"2021","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"De, R., Kundu, A., and Chakraborty, S. (2022). Long Short-Term Memory-Based Deep Learning Algorithm for Damage Detection of Structure, Springer.","DOI":"10.1007\/978-981-16-6490-8_27"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kong, Y.-L., Huang, Q., Wang, C., Chen, J., Chen, J., and He, D. (2018). Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10030452"},{"key":"ref_30","first-page":"e1676933","article-title":"A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies","volume":"2022","author":"Wang","year":"2022","journal-title":"Int. J. Aerosp. Eng."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Angeletti, F., Iannelli, P., Gasbarri, P., Panella, M., and Rosato, A. (2023). A Study on Structural Health Monitoring of a Large Space Antenna via Distributed Sensors and Deep Learning. Sensors, 23.","DOI":"10.3390\/s23010368"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113059","DOI":"10.1016\/j.compstruct.2020.113059","article-title":"Single and Multi-Layer Core Designs for Pseudo-Ductile Failure in Honeycomb Sandwich Structures","volume":"256","author":"Naresh","year":"2021","journal-title":"Compos. Struct."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"169","DOI":"10.30536\/j.jtd.2018.v16.a3065","article-title":"Stiffness Evaluation of LAPAN-A5\/Chibasat Deployable Solar Panel Composite Plate using Simplified Finite Element Model","volume":"16","author":"Triharjanto","year":"2019","journal-title":"J. Teknol. Dirgant."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0263-8231(99)00026-9","article-title":"The Strength Characteristics of Aluminum Honeycomb Sandwich Panels","volume":"3","author":"Paik","year":"1999","journal-title":"Thin-Walled Struct."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0094-5765(02)00277-1","article-title":"Dynamics of Multibody Systems in Space Environment; Lagrangian vs. Eulerian Approach","volume":"54","author":"Santini","year":"2004","journal-title":"Acta Astronaut."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.actaastro.2020.04.015","article-title":"Design and Performance Assessment of a Distributed Vibration Suppression System of a Large Flexible Antenna during Attitude Manoeuvres","volume":"176","author":"Angeletti","year":"2020","journal-title":"Acta Astronaut."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1121\/1.1342076","article-title":"Adaptive Structures: Dynamics and Control","volume":"109","author":"Clark","year":"2001","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Preumont, A. (2018). Vibration Control of Active Structures, Springer International Publishing. Solid Mechanics and Its Applications.","DOI":"10.1007\/978-3-319-72296-2"},{"key":"ref_41","unstructured":"(2023, August 16). Physik Instrumente (PI) P-876 DuraActTM\u2014Piezoelectric Patch Transducers Datasheet. Available online: https:\/\/www.piceramic.com\/fileadmin\/user_upload\/physik_instrumente\/files\/datasheets\/P-876-Datasheet.pdf."},{"key":"ref_42","unstructured":"Kunbo, X., Zheng, J., Gong, Z., Yan, C., Yongqiang, M., Pin-Liang, Z., and Qiang, W. (2017, January 17\u201321). Investigation on Solar Array Damage Characteristic under Millimeter Size Orbital Debris Hypervelocity Impact. Proceedings of the 7th European Conference on Space Debris, Darmstadt, Germany."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Beitia, J., Loisel, P., and Fell, C. (2017, January 28\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_44","doi-asserted-by":"crossref","unstructured":"Ren, Y., Tao, J., and Xue, Z. (2020). Design of a Large-Scale Piezoelectric Transducer Network Layer and Its Reliability Verification for Space Structures. Sensors, 20.","DOI":"10.3390\/s20154344"},{"key":"ref_45","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_46","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_47","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","unstructured":"Ziaja, M., Bosowski, P., Myller, M., Gajoch, G., Gumiela, M., Protich, J., Borda, K., Jayaraman, D., Dividino, R., and Nalepa, J. (2021). Benchmarking Deep Learning for On-Board Space Applications. Remote Sens., 13.","DOI":"10.3390\/rs13193981"},{"key":"ref_50","unstructured":"R\u016f\u017ei\u010dka, V., Mateo-Garc\u00eda, G., Bridges, C., Brunskill, C., Purcell, C., Long\u00e9p\u00e9, N., and Markham, A. (2003). Fast Model Inference and Training On-Board of Satellites. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rapuano, E., Meoni, G., Pacini, T., Dinelli, G., Furano, G., Giuffrida, G., and Fanucci, L. (2021). An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study. Remote Sens., 13.","DOI":"10.3390\/rs13081518"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/S1000-9361(08)60133-8","article-title":"Design and Experiment of PZT Network-Based Structural Health Monitoring Scanning System","volume":"22","author":"Lei","year":"2009","journal-title":"Chin. J. Aeronaut."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7515\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:42:10Z","timestamp":1760128930000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7515"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,29]]},"references-count":52,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177515"],"URL":"https:\/\/doi.org\/10.3390\/s23177515","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,29]]}}}