# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
### Hybrid Methods  
Hybrid methods are characterized by their ability to integrate data-driven and model-based methods. Using hybrid methodologies, the idea is to cover both main approaches' shortcomings using each approach's specializations. The term \"Hybrid\" implies the fusion of data-driven and model-driven methods; however, it does not restrict how the two approaches are combined. In other words, the word hybrid mostly serves as an umbrella term. This is also because hybrid methods in satellite, spacecraft, and UAV health monitoring have not been thoroughly explored in the literature, leaving hybrid methods a promising solution to further boost the existing FDII methods. This section reviews the few existing hybrid methodologies applicable to fault diagnosis applications. [PERSON] et al. [100] presented an enhanced FDI methodology for two RWs of a four-RW assembly, including both model-based and data-driven approaches used in residual generation and evaluation, respectively. The residual generation is conducted using the nonlinear geometric approach, and evaluation is carried out using a blended learning strategy with two separate hierarchically activated detection and isolation modules. The strategy incorporates robustness against external disturbances and model parametric uncertainties through the residual generation and evaluation modules. Consequently, the advantages of both the model-based and data-driven techniques are leveraged for the extra robustness of the strategy. The learning algorithm used in [PERSON] et al. 's methodology employs a two-step classification approach by training several classifiers and using their test outputs to train the second-level learner. They adjusted their strategy to perform diagnostics using parallel and series formations. However, the series formation mostly surpassed the parallel formation. The simulations verify the hierarchical residual evaluation module's capability to provide satisfactory predictions using different validation datasets under real-life circumstances. An NN-based observer is presented by [PERSON] et al. in [40] to minimize the impact of external interference on malfunction isolation and identification of single gimbal CMGs onboard spacecraft in periodic orbits. In addition, [PERSON] et al. employed multiple local fault adaptive estimators for fault isolation and basic identification for spacecraft with extra actuators. Then, their results are used to fuse gimbal and attitude data via neural networks. Finally, these NNs are used to isolate and identify malfunctions in the system. [PERSON] et al. [30] proposed a hybrid method for fault detection, isolation, and recovery of satellite IMU sensor faults. The proposed scheme works in two steps that get activated in succession: (1) a preliminary diagnosis and (2) a more precise diagnosis. The first step uses an adaptive unscented KF and the QUaternion ESTmiator (QUEST) algorithm in conjunction with each other to be able to examine different sensors and make the fault isolation problem deterministic. The second step consists of two Radial Basis Function (RBF) neural networks and an adaptive complementary filter. During normal operation, the two RBF neural networks get trained on the quaternion outputs of the AUKF and QUEST, and their training is halted when the first step is begun. After stopping their training, they predict the system quaternion outputs and contribute to achieving a higher level of accuracy in fault diagnosis and recovery of IMU faults. Furthermore, [PERSON] et al. incorporated hypothesis testing, Parity equation, and Chi-Square approaches into their residual generators to address the problem of the residual thresholding being sensitive to factors beyond only faults and false alarms. Their proposed scheme was tested using an air-bearing table and demonstrated to have consistently better accuracies than the AUKF and QUEST algorithms used individually. Another implementation of hybrid methods is conducted by [PERSON] et al.