# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 1 Introduction  
The reliability of satellite and Unmanned Aerial Vehicle (UAV) missions has received significant attention in recent years owing to the cost and time needed to launch satellites [1]. The possible severe consequences in the case of satellite and UAV failures also contribute to this cause in addition to the manufacturing and operational costs. According to [2], 32% of on-orbit spacecraft failures are attributed to the attitude and orbit control system (AOCS) and 27% to the power subsystem. Consequently, addressing the AOCS and power subsystem health monitoring could significantly boost onboard spacecraft mission reliability. Moreover, 51% of the malfunctions are due to Reaction Wheel (RW), momentum wheels, Control Moment Gyro (CMG), thrusters, and gyroscopes, and 18% are due to unknown reasons, warranting the need to explore health monitoring in the mentioned system components. Regarding the time of failure after launch, 41% of the recorded failures in [2] occurred in the first year of operation, making Fault Diagnosis and Prognosis (FDP) strategies imperative to extending on-orbit spacecraft and satellites' lifetimes. On the other hand, for UAVs, malfunctions could be caused by intentional or unintentional factors. Among the various operation-endangering abnormalities, the following are the most prevalent: cyber-attacks, such as signal jamming, spoofing, measurement sensor, keylogging viruses, control system attacks, and exogenous physical factors, such as sudden aerodynamic disturbances, and damaged actuators [3]. The satellite and UAV actuator and sensor failures could also be attributed to intrinsic hardware wear-and-tear and degradation. To increase the mission reliability of these agents, all kinds of significant faults should be fixed or circumvented regardless of their source. There are two main approaches for increasing the mission reliability of satellites and UAVs [4]: hardware redundancy and analytical redundancy. Hardware redundancy involves incorporating redundant components for switching to a healthy component once the primary unit malfunctions. However, this poses a major issue. With the rise in the number of launched satellites and the decrease in their sizes, embedding redundant components becomes impractical [5]. The limited space available in UAVs also does not allow for hardware redundancy. In addition, switching from a faulty to a healthy component could disturb the operational continuity of missions [6]. Due to hardware redundancy's evident shortcomings, there is a need for advanced analytical redundancy methods. Analytical redundancy methods comprise vastly different means to monitor the health status of agents, namely, data-driven, model-based, and expert manpower-based methods. Compared with the methods reliant on expert personnel to go through the operational data of the agents, autonomous methods are distinctly superior, especially since they are more cost-effective and time-efficient while being less prone to errors. Hence, the advantages of autonomous analytical redundancy methods have inspired researchers to explore this area further. Among the host of analytical redundancy methods, data-driven and model-based methods are the most prevalent in the literature owing to their advantages. To address the health monitoring requirements of agents more precisely, analytical redundancy methods are divided into fault diagnosis and fault prognosis fields. The former accounts for informing the system of a fault in it, whereas the latter is responsible for estimating the system's Remaining Useful Life (RUL) given the diagnosed faults. From a mission reliability perspective, the insights derived from the FDP modules are used as input to the fault-tolerant control module. To increase the readability and understandability of this work among the myriad of works on general system reliability applications, we employ and explain the terminology in our work decided upon by the International Federation of Automatic Control (IFAC) Technical Committee SAFEPROCESS and outlined in [7].