# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
First, to train the WGAN model, multiple datasets for each fault type are curated. Each dataset contains very few data points as compared with the nominal RW dataset to reflect a real-life scenario. Then, identical instances of their proposed WGAN model are trained on each RW faulty class dataset. In this way, the overall training dataset becomes balanced. The final datasets augmented by the proposed framework of [PERSON] et al. were tested quantitatively using an LSTM architecture and qualitatively as well. The LSTM model was trained separately on the WGAN-augmented, naturally balanced, imbalanced, artificially duplicated (inflated), and DTW-oversampled datasets and obtained accuracy rates of 84.17%, 93.25%, 48.35%, 97.51%, 89.32% as tested on a natural test dataset. The reason for WGAN-augmented dataset's underperformance as compared with other augmentation methods was attributed to WGAN adding too much diversity to faulty datasets and some resultant classes resembling each other. However, qualitatively it was observed that the WGAN added immense diversity and variability to the base dataset while retaining its most defining patterns whereas the other data augmentation methods added little to no diversity to the base dataset. [PERSON] et al. [85] used a novel data-driven method to detect faults in satellite power systems. Their presented method consists of forming correlations between time series datasets of several sensor measurements employing association rules between sensor datasets and incorporating them into a Kernel Principal Component Analysis (KPCA) model for detection. Association rule learning is especially effective since the sensor relationships change when an anomaly occurs. The novelty of the study of [PERSON] et al. warrants the need for it to be examined further, but scant work has been carried out on this approach and application. [PERSON] et al. [28] presented a method employing a One Dimensional (1D)-CNN to address the fault detection problem in the power systems of satellites. The input data are preprocessed using the Stockwell transform before being fed into the CNN. When tested using the Advanced Diagnostics and Prognostics Testbed [86], the univariate detection methodology achieved an accuracy of 96.7%. Furthermore, [PERSON] et al. claimed that  
Figure 7: Data augmentation for a sample image of a dog (the original image is available [[https://unsplash.com/photos/short-coated-brown-and-white-puppy-eoqnr8](https://unsplash.com/photos/short-coated-brown-and-white-puppy-eoqnr8) ikwFE]([https://unsplash.com/photos/short-coated-brown-and-white-puppy-eoqnr8](https://unsplash.com/photos/short-coated-brown-and-white-puppy-eoqnr8) ikwFE) here) (accessed on 10 July 2024). the methodology could be extended to multivariate detecting, locating, and classifying malfunctions in the power systems. [PERSON] and [PERSON] [10] presented a data-driven methodology for FDI in CMGs. Their methodology relies on the following elements for fault diagnosis: a predictive data-driven model reducing the dependency of the method on _a priori_ operational data by 93.75%, a Chebyshev neural network for detecting malfunctions in CMGs, and an optimization-based scheme using a Genetic algorithm for locating faults. The input data are limited to attitude rate measurements to accommodate satellite measurement limitations, but more comprehensive input data could be incorporated into the method. Simulated results showed a 93.25% accuracy rate in isolating different types of faults. [PERSON] and [PERSON]'s proposed method is suitable for applications where sensor redundancy poses problems or when historical flight records are unavailable. [PERSON] et al. [24] proposed a mixed-learning approach to FDI of a tetrahedral RW assembly and performed a comparative study of the mixed and individual models. [PERSON] et al. incorporated the RF, SVM, partial least square, and Naive Bayes (NB) algorithms into their mixed-learning strategy.