[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123422-en":3,"doc-seo-123422-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123422,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine Learning Approaches for Data-Driven Self-Diagnosis and Fault Detection in Spacecraft Systems - Academic Article","Ensuring the reliability and robustness of spacecraft systems remains a key challenge, especially when continuous real-time monitoring is limited during on-orbit operations. For Fault Detection, Isolation, and Recovery (FDIR), no single universal strategy has been established. The study investigates data-driven self-diagnosis and fault detection for spacecraft Guidance, Navigation, and Control (GNC) subsystems. A functional engineering simulator generates realistic onboard-like datasets, supervised learning models are trained and benchmarked against threshold detection, and comparisons across failure conditions assess strengths, limits, and complementary use with classical methods.","Article  \nMachine Learning Approaches for Data-Driven Self-Diagnosis and Fault Detection in Spacecraft Systems  \nEnrico Crotti ∗ and Andrea Colagrossi ∗  \nAcademic Editors: Amelia Zafra and Bruno Miguel Veloso  \nReceived: 10 June 2025  \nRevised: 1 July 2025  \nAccepted: 8 July 2025  \nPublished: 10 July 2025  \nCitation: Crotti, E.; Colagrossi, A. Machine Learning Approaches for Data-Driven Self-Diagnosis and Fault Detection in Spacecraft Systems. Appl. Sci. 2025, 15, 7761. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/app15147761](10.3390/app15147761)  \nCopyright: © 2025 by the authors. 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/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \nDepartment of Aerospace Science and Technology, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy * Correspondence: [enrico.crotti@mail.polimi.it](enrico.crotti@mail.polimi.it) (E.C.); [andrea.colagrossi@polimi.it](andrea.colagrossi@polimi.it) (A.C.)  \nAbstract  \nEnsuring the reliability and robustness of spacecraft systems remains a key challenge, particularly given the limited feasibility of continuous real-time monitoring during on-orbit operations. In the domain of Fault Detection, Isolation, and Recovery (FDIR), no universal strategy has yet emerged. Traditional approaches often rely on precise, model-based methods executed onboard. This study explores data-driven alternatives for self-diagnosis and fault detection using Machine Learning techniques, focusing on spacecraft Guidance, Navigation, and Control (GNC) subsystems. A high-fidelity functional engineering simulator is employed to generate realistic datasets from typical onboard signals, including sensor and actuator outputs. Fault scenarios are defined based on potential failures in these elements, guiding the data-driven feature extraction and labeling process. Supervised learning algorithms, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are implemented and benchmarked against a simple threshold-based detection method. Comparative analysis across multiple failure conditions highlights the strengths and limitations of the proposed strategies. Results indicate that Machine Learning techniques are best applied not as replacements for classical methods, but as complementary tools that enhance robustness through higher-level self-diagnostic capabilities. This synergy enables more autonomous and reliable fault management in spacecraft systems.  \nKeywords: fault detection, isolation, and recovery (FDIR); spacecraft autonomy; self-diagnosis; artificial intelligence; data-driven methods; guidance, navigation, and control  \n1. Introduction  \nThe management of onboard systems and health status monitoring of a spacecraft is a pivotal but extremely wide aspect of satellite design that is being reshaped as satellites evolve and autonomy increases. Onboard Fault Detection, Isolation, and Recovery (FDIR) provides supervision and control on the satellite behavior under unexpected situations and malfunctions. The main design philosophies for carrying the task of anomaly identification onboard spacecrafts, at the current time, are model-based and data-driven. Model-based techniques exploit an “analytical redundancy” of the onboard subsystems: by running anonboard model in parallel with real-time operations as they are performed, the spacecraft relies on a duplicated virtual version of part of itself. By checking simultaneously the outputs of real elements and their simulated version, residuals are periodically calculated, and it is possible to establish if any discrepancy from the expected scenario has occurred. Analytical redundancy adds a layer of cross-checks without the addition of hardware components to the system: the result of these che","cbCaimDFyFwH1tUy","https://ap.wps.com/l/cbCaimDFyFwH1tUy","pdf",2268129,1,25,"English","en",105,"# Introduction\n# Fault Detection, Isolation, and Recovery (FDIR) and Approaches\n# Data-Driven Machine Learning Framework for Spacecraft GNC\n## Dataset generation using a functional engineering simulator\n## Feature extraction and fault scenario labeling\n# Supervised Learning Methods and Benchmarking\n## SVM and ANN implementations\n## Comparison with threshold-based detection\n# Results and Comparative Analysis\n## Strengths, limitations, and complementary role in onboard robustness","[{\"question\":\"What problem does the study target in spacecraft systems?\",\"answer\":\"The study targets reliable and robust Fault Detection, Isolation, and Recovery (FDIR) when continuous real-time monitoring on orbit is not always feasible.\"},{\"question\":\"How are training and evaluation datasets created?\",\"answer\":\"A high-fidelity functional engineering simulator generates realistic datasets from typical onboard signals, including sensor and actuator outputs.\"},{\"question\":\"Which fault detection methods are compared in the paper?\",\"answer\":\"The paper benchmarks supervised machine learning approaches such as Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) against a simple threshold-based detection method.\"}]","Machine Learning Approaches for Data-Driven Self-Diagnosis and Fault Detection in Spacecraft Systems - 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