# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
### Model-Based Methods
Moreover, it utilizes the MMAE method to use each observer's residuals and the state error covariance matrix for obtaining the fault conditional probabilities, which are then employed to achieve fault diagnosis. The proposed methodology of [PERSON] et al. also enhances the efficiency of the MMAE method. It does not require additional sensors to monitor actuator deflections and change the flight controller. [PERSON] et al. [38] designed an \(H_{\infty}\) observer for FDI of actuators onboard a quadrotor UAV where the rotational dynamics are modeled as a reduced quasi-linear parameter-varying system. The numerical test results demonstrate that the proposed approach performs on the same accuracy level as similar methods. However, it is claimed to be more efficient because it only accounts for the system's rotational dynamics. On the other hand, it falls short in carrying out fault diagnosis while considering translational dynamics. The next progressions for this research could include validating the proposed approach with an experimental setup, developing a mixed \(H/H_{\infty}\) scheme for fault diagnosis or using it with a fault-tolerant controller. To also address surface icing, [PERSON] et al. [39] presented a model-based methodology for UAV icing and actuator fault diagnosis. Their approach is structured around combining the concepts of internal and Unknown Input Observers (UIO) and exploiting their fusion to develop a linear parameter-varying observer to circumvent the issues surrounding model uncertainties and unknown inputs. [PERSON] et al. also suggested the following additions to enhance the proposed methodology: Alleviating the methodology's conservativeness, improving its performance, and generalizing it for more comprehensive operating conditions. An innovative hierarchical approach for fault diagnosis in satellite components or subsystems is presented in [66]. This method emphasizes dividing the complex system into smaller pieces for structured diagnostic reasoning based on rules specific to each element. These modules are interconnected through a Component Dependency Model (CDM) using Bayesian networks. A leader-follower SFF is used to demonstrate the method's functionality and prove its potential to enhance satellite health monitoring. The approach will be extended to dynamic Bayesian network-based CDMs and systematically generated, providing promising directions for enhancing fault diagnosis capabilities. In [52; 55], [PERSON] and [PERSON] explored strategies for managing actuator malfunctions in a decentralized manner for SFF. This is facilitated through a cooperative framework comprising three layers. Initially, established recovery methodologies are employed at the bottom-layer fault recovery based on assessments of fault severity. Nevertheless, inaccurate fault assessments can result in violations of mission error protocols. The higher layer supervising identifies faults and initiates the formation layer of fault recovery. This high-level supervision endeavors to adjust for the satellite partially restored at the lower level. In another paper, [PERSON] and [PERSON] [47] developed a hybrid method that combined FDI stages such as [52; 55] for a team of cooperating UAVs. Their hybrid methodology incorporates both bottom-layer and top-layer FDI modules. Simulation results involving a team of five UAVs are provided, demonstrating the effectiveness of this approach. In [48], [PERSON] et al. expanded the work in [47] and used Discrete-Event Systems (DES) at the top level to diagnose faults, while traditional diagnostic methods are used at the lower level. A broader spectrum of faults can be detected and isolated using this method than has been reported in the literature. In another study [53], [PERSON] and [PERSON] explored a novel distributed KF approach designed to estimate actuator faults for SFF.