## 6 Final Remarks  
In this research, the challenge of a reliable data-driven fault detection and diagnosis strategy for space systems is explored. The focus has been brought in particular to Support Vector Machines and Neural Networks, trained and validated by large datasets produced in a high-fidelity functional engineering simulator, and compared with a benchmark variance-based method. The kinds of possible faults happening on a space platform can be reduced to a small pool of basic instances, and the feature extraction phase has been showcased in detail as one of the main elements responsible for the quality and accuracy of classifiers in use. After a description of the hypotheses employed and details of the satellite replicated in the simulation, the testing phase was finally set up.  
The benchmark classic threshold-based detection algorithm has shown its strength points and weaknesses in terms of the accuracy reached even with a meticulous tuning of parameters. The completely different approach and higher flexibility given by AI-aided classifiers apparently hints to a progressive substitution of classic methods, or at least to a use in parallel of both methods for a fail-safe redundancy. However, performances of classifiers are not robust enough to confidently replace classic methods in their totality: what is recommended instead is a work in synergy, to outperform the capabilities of both the individual architectures. Classic detection is optimal for simple checks, like the ones that signal an out-of-range sensor or an actuator that is stuck on a constant value, but it runs into trouble when tasked with identifying useful information on a signal with a never-before-seen variance trend with respect to nominality. It would be beneficial, at that point, to let a more refined classifier enter the analysis and come in to support these simple monitoring functions, by filling their capability gaps. Classifiers like SVMs and Neural Networks are able to process signals features with an extraordinary sophistication and ANNs, in particular, with an extremely high computational quickness: apart from the training time needed in the project phase, real-time performances are superior in most instances. By coupling the use of variance-based methods adopting simple thresholds, with the accuracy and quickness of trained learning agents for a deeper diagnosis, much more information can be extracted after a fault has happened, and recovery decisions taken autonomously onboard, if shaped on this enriched awareness of all subsystem statuses, will reach autonomy levels never approached until now. Thanks to Machine Learning and artificial intelligence, spacecraft can reach a high level of robust self-diagnostics, not only for GNC and sensor management but by following this novel paradigm for the entire space system, achieving an increased lifetime and an ever-improving self-healing capability.  
Conceptualization, A.C. and E.C.; methodology, A.C. and E.C.; formal analysis, E.C.; data curation, E.C.; writing--original draft preparation, E.C.; writing--review and editing, A.C. and E.C.; visualization, E.C.; supervision, A.C.; project administration, A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries are welcomed and can be directed to the corresponding authors.  
The authors declare no conflicts of interest.