# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
[PERSON] et al. [80] advanced the idea of utilizing data-driven approaches for Fault Detection and Isolation (FDI) using an ensemble of Adaboost decision tree, Adaboost Random Forest (RF), MultiLayer Perceptron (MLP), and K-Nearest Neighbors (KNN) ML algorithms applied to a four-wheel configuration of RWs. Principal Component Analysis (PCA) was also incorporated into the scheme to perform dimension reduction. Their method in [80] underperformed the required expectations for an FDI system with the highest classifier precision of 58.78%. [PERSON] et al. claimed that implementing more complicated datasets for running the algorithms and utilizing optimization methods for hyperparameter evaluation could lead to more accurate results. Expanding the scheme to address different malfunction intensities, symmetrical RW arrangements, and computation overload would be the next step in this scheme's progression. [PERSON] et al. [21] discussed data-driven fault isolation for locating multiple concurrently existing malfunctions in a four-CMG pyramid assembly. The proposed data-driven scheme consists of a feature extraction method, a feature reduction method, and a classification algorithm, which, through testing, are chosen to be Correlation Analysis, PCA, and an optimized Support Vector Machine (SVM), respectively. A sensitivity analysis was carried out to demonstrate the proposed method's robustness against added noise signals, unavailable sensors, and skipped measurements. The results of the sensitivity analysis displayed suitable error margins for practical implementations. The simulations exhibit 100% and 99% accuracies for healthy and single-malfunctioning-CMG assemblies, respectively, and 97.8%, 91.4%, and 77% accuracies which correspond to two, three, and four in-phase malfunctions coexisting in a system. The problem of diagnosing simultaneous faults is best countered using data-driven methods. As [PERSON] and [PERSON] have demonstrated in [45], where concurrent faults can occur, e.g., RW assemblies, the model-based methods may struggle with diagnosing all of the faults depending on the system and if more than two are present. [PERSON] et al. [32] explored detecting and isolating in-phase malfunctions using automated data-driven methods applied on three-orthogonal, standard four-wheel, and pyramid RW configurations. Their proposed approach preprocesses the time series input data by automatically extracting features from the temporal, spectral, and statistical domains and then passing it through a feature reduction stage. Subsequently, several classification methods were considered: Gradient Boosting, RF, Decision Tree, and MLP. It was shown that Gradient Boosting and RF performed the best among the methods. However, both operated poorly under complex fault scenarios. The FDI methodology fared relatively better for the three-orthogonal case than the standard four-wheel, and pyramid arrange ments, especially due to the symmetry in the pyramid assembly. Expanding upon the presented scheme by improving the accuracy of FDI for more complicated malfunction scenarios would be the next step in developing a more robust FDI scheme. Moreover, [PERSON] et al. mentioned that for RW configurations that have each RW manipulating torques in more than one axis (e.g., pyramid and standard four-wheel), fault complexities (including inception and duration) could have a detrimental effect on the FDI scheme's accuracy. [PERSON] et al. [81] formulated a data-driven approach to fault detection in RW assemblies employing supervised and unsupervised learning techniques, including Gaussian Mixture Model (GMM), SVM, Artificial Neural Networks, and Long Short-Term Memory (LSTM) Neural Networks. The learners receive a combination of RW attitude, temperature, control torques, and power consumption to detect the occurrence of faults. However, temperature proved to be the most sensitive variable to faults.