# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 1 Introduction
Here, we briefly go over the most prevalent definitions presented in [7]: \"Fault Detection\" is defined as the determination of an existing system fault with an emphasis on the time of detection. Following this stage, \"Fault Isolation\" is described as the stage responsible for determining the location, kind, and time of detection. The subsequent step is labeled \"Fault Identification\", which involves determining the size details of the isolated fault and the time-variant behavior that it exhibits. Alternatively, \"Fault Diagnosis\" is defined as a step that follows \"Fault Detection\", contains \"Fault Isolation and Identification\" in itself, and entails measuring the size, kind, location of the fault, and the time of its detection. Finally, \"Monitoring\" means continuous online or real-time physical system condition evaluation through storing operational information and discerning and announcing the emerging anomalies in its operation. In the current study, we review the different FDP methods for satellites and UAVs that work individually or as members of a multisatellite/UAV network using analytical redundancy methods. The inclusion of both satellites and UAVs is due to the similarities in how health-monitoring approaches have been implemented for these agents. Investigating the methods employed in one agent could help provide valuable takeaways for utilizing them in the other agent. As a result of the prominence of data-driven and model-based methods in this area, this study has been divided into sections covering each category while also reviewing other methods that are fusions of the two mentioned categories (i.e., hybrid methods) or completely different methodologies. The diagnostic and prognostic methods used for increasing the mission reliability of satellites, spacecraft, and UAVs in this work can be applied to more than one class of each of these agents. Many of the techniques addressed in this work are applicable to various actuators, sensors, and subsystems onboard satellites and UAVs. Satellites are usually categorized based on their mass, which can lie in ranges of 1 g to upwards of 1000 kg [8]. Some of the most prominent satellite classes include nanosatellites and small-, medium-, and large-sized satellites. However, the methods analyzed in this review are presentedwith the mindset of applying them to more than their original classes. Conversely, when a method is applicable to only one or a few scenarios, it is declared. Furthermore, different classes of UAVs are also considered. The main categories of UAVs include the two groups of Vertical Takeoff and Landing (VTOL) and Conventional Takeoff and Landing (CTOL) UAVs. Some examples of VTOLs covered in this work are Hex-rotor, quadrotor, and tilt-rotor UAVs and fixed-wing UAVs for the CTOL class. The same analysis logic described for the FDP of different satellite classes also holds for different UAV classes. It is notable to mention that in this review, relevant quantitative validation results of the discussed studies are presented when possible. However, the test results of different studies are usually acquired under different circumstances unless compared directly. Therefore, deep analysis solely based on the quantitative results might prove erroneous, but they are provided for reference in this review and expanded upon when possible. The rest of the article is structured as follows. Section 2 provides initial details, encompassing the problem's definitions and setting the groundwork for subsequent discussions. Section 3 goes over the review methodology that was adopted in this review and important considerations and assumptions for the readers of this review. Sections 4 and 5 explore and review FDP approaches, respectively. Finally, Section 6 summarizes this paper, presents a classification of the prominent methodologies in the field of satellite and UAV FDP, and proposes potential future research directions based on the identified gaps in the field.