# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 6 Conclusions and Future Work
### Future Directions  
To provide promising avenues to further the works presented in this review, below, we outline the potential areas for future exploration and improvements based on the gaps that have been identified:  
Data-Driven FDP: Due to the scarcity of available historical operational datasets, generating artificial datasets that resemble real-life scenarios using AI-based methods is a promising approach for more rigorous exploration. Artificial datasets can address the limitations of mathematical models and provide a more comprehensive basis for FDP. Increasing user trust in data-driven methods: Due to the low interpretability of many data-driven methods, user trust in these methods could suffer in spite of their effectiveness given how sensitive satellites, spacecraft, and UAVs could be. Methods such as LIME, which is utilized in [75], could alleviate this issue, although with an added trade-off with computational burden. Dynamical Variables: Many works in the literature have considered different dynamical variables to achieve reliable fault diagnosis in single-agent systems according to their intended use. Experimenting with all possible dynamical variables in a system to train data-driven methods could yield intriguing results. Incorporating new features into data-driven methods allows for flexibility, albeit with additional computational demands. Hybrid Methods: Leveraging a hybrid approach combining model-based and data-driven methods could help overcome the shortcomings of each individual method, leading to superior analytical techniques in the field of FDP. Prognostics: While diagnostics for single-agent systems have been explored, there is room for further research in prognostics, particularly for satellites and UAVs. Comprehensive research on fault prognosis for multisatellite/UAV systems is currently lacking and warrants further exploration. Markovian Jump Systems: The integration of Markovian Jump Systems (MJS) and multiple models into fault diagnosis methods such as [13; 74; 102] for both single and multiagent systems presents an attractive opportunity, as they can handle abrupt system changes efficiently. Association Rule Learning: As demonstrated by [PERSON] et al. [85], association rule learning could serve as a not widely explored but viable means of analyzing the relationships  
Figure 12: Breakdown of the methodologies used in the multiagent FDP literature. between a bank of sensors or actuators. This approach could benefit the mission reliability of an agent even more if hardware redundancy is also available. Graph Neural Networks: These networks differ from most traditional ML methods in the sense that they do not require feature independence [89]. Therefore, they have the capability to analyze a broader range of variables within the space and aerial agents which could render examining certain fault-sensitive parameters possible. These networks have also been utilized in the pertinent literature [33]. Multiagent Systems: There is a significant gap in research concerning fault diagnosis methods for multisatellite/UAV systems that prioritize consensus, coverage, or multiagent system control logic. Addressing this gap is a valuable direction for future research. Two of the few examples in this field include [60,62]. Heterogeneous Dynamics: Existing research mainly focuses on multiagent systems with homogeneous dynamics, while real-world systems exhibit heterogeneous dynamics influenced by environmental factors and mission objectives. Investigating FDP strategies that consider dynamic variability is essential for future research on fault diagnosis in multiagent systems. Examples of such work include [60, 61, 62]. Event-Triggered Communication: Exploring event-triggered communication can optimize information flow and reduce network workload, aligning research with practical applications and enhancing system efficiency. A summary is provided in Table 8.  
Fractional-Order Systems: Future research should address fault diagnosis for fractional-order or reduced-order multisatellite systems.