# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics  
The domain of diagnostics plays a crucial role in the health assessment of satellites, spacecraft, and UAVs. Detecting, localizing, and identifying faults are essential to rescuing a malfunctioning agent's mission. Furthermore, successful prognosis relies on the awareness of a system's fault and the availability of relevant information. As a result, fault diagnosis for these agents has garnered significant interest, spurring extensive research in this field. Due to the increasing attention that the field of fault diagnosis has received in monitoring a broad range of agents, scientists have expanded its implementation to include satellite, spacecraft, and UAV fault diagnosis. The predominant strategies employed in the current body of literature for performing fault diagnosis within Aerospace applications, with a specific focus on satellites and UAVs, encompass data-driven and model-based techniques. While there have been occasional references to their fusion and alternative methods in published works, these approaches have not been subjected to the same rigorous scrutiny in the literature as the previously mentioned strategies. This section comprehensively lists and reviews the applications of data-driven, model-driven, hybrid, and other methods on diagnostics of a single and a group of satellites, spacecraft, and UAVs in the current literature. To provide a detailed and yet concise overview of each paper's strong suits and shortcomings, Table 2 presents the advantages and disadvantages of each study on single-agent fault diagnostics. Table 3 offers a similar analysis for a team of aerial and space agents. In order to provide a broad yet comprehensive overview of the innovations featured in the studies reviewed in this research, each study has been scrutinized and an extensive list of their characteristics is presented in Tables 5 and 7 for single-agent and multiagent fault diagnosis, respectively. Table 5 is structured in a way to highlight the exact architectures, health monitoring modes, considered fault types and variables, and applications of each study on single-agent systems. Consequently, facilitating the process of deriving insights from each paper. In addition, Table 5 serves as a comparative standpoint over the methodologies utilized in this field. Furthermore, Table 7 offers an overview of the attributes in research related to fault diagnosis in multisatellite/UAV systems. area. Their ability to accommodate applications where autonomous diagnosis is required or when manually defined features are not available has made data-driven techniques a popular method to perform diagnostics. This section reviews how the existing literature has employed data-driven techniques for fault diagnostics. [PERSON] et al. [27] proposed a robust data-driven methodology for fault diagnosis of actuators and sensors onboard a broad range of nonlinear systems. Their methodology incorporates two recurrent neural network-based observers for Fault Detection, Isolation, and Identification (FDII) and operates based on the studied nonlinear system's state-space model. More specifically, FDII is achieved simultaneously as the diagnosis scheme gets alerted. This is performed by indicating where the fault has formed using fault-sensitive thresholds and obtaining its intensity. The proposed methodology is also strengthened against unmodeled dynamics, unknown faults, noises, and uncertainties. The methodology was tested using a case study on magnetorquer actuators and magnetometer sensors onboard the Attitude Control System (ACS) subsystem of Low Earth Orbit (LEO) satellites, and the test results verified the methodology's capability to be used in real-life situations. While actuator fault diagnosis is of paramount significance to increase the reliability of control systems, sensor FDII is also influential in affecting an agent's lifetime. Sensor faults could emerge due to external disturbances, aging, and power fluctuations [6]. The work of [PERSON] et al. addressed the reliability requirements for actuators and sensors.