# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
The models are trained with the simulated datasets provided by the Basilisk simulation framework [82], a novel approach to training data-driven models for satellite fault diagnosis applications. Producing and using large amounts of synthetic data could be conducive to achieving adeptly trained models due to not having a limitation on the amount of data produced and knowing the exact details of the occurring faults beforehand as opposed to using historical data for training models [81]. Conversely, to avoid overfitting, amply-sized synthetic and historical datasets could also be created using generative models. The datasets acquired using generative AI could become a promising approach to alleviate the problems associated with the training phase of data-driven methods. [PERSON] et al. [83] developed a feature selection algorithm based on the fuzzy Bayesian risk theory along with a heuristic forward greedy algorithm for fault diagnosis of power systems of in-orbit satellites. The proposed algorithm works with an SVM for classifying faulty or nominal states. While the proposed feature selection method is observed to obtain more accurate results than other prevalent methods, it is computationally intensive, and overcoming this shortcoming could be a favorable approach to progressing this research. [PERSON] et al. [17] provided a solution to overcome the problem of faulty dataset scarcity for training data-driven satellite fault detection models. The solution incorporates redistributing the healthy and faulty datasets by oversampling the faulty samples using Dynamic Time Warping (DTW). Their proposed fault detection scheme is then completed by employing the fast DTW method for assessing correlations between samples and a KNN model for detecting malfunctions. [PERSON] et al. tested the methodology, and DTW oversampling significantly increased the fault detection accuracy. The proposed method by [PERSON] et al. could also help mitigate the training difficulties of data-driven methods for applications where several distinct fault types could be present in a system. Data augmentation is a technique that has been widely employed in the field of ML to make classification models more robust to diverse test cases. Data augmentation is especially common in the field of computer vision. To demonstrate how data augmentation works, an example is provided in Figure 7. Let us assume that we want to train a data-driven model to be able to discern dog images among other classes. If our dataset only consists of images like the original image in Figure 7, our dataset would be scarce and the data-driven model would be prone to overfitting. For example, the data-driven model through the training process might come to the conclusion that dogs are only dogs if the ear-shaped features are towards the top of the image or the paw-shaped features are toward the bottom of the image. To us humans, this conclusion appears clearly erroneous; however, this must be clarified to data-driven models by curating a comprehensive dataset or using a more robust architecture. In this case, a myriad of image modifications are imposed on the original dataset to make the dataset more diverse as shown in Figure 7. This process is referred to as data augmentation, and it is applicable to more than just images. For example, in the work of [PERSON] et al. [17], DTW oversampling was used that in some way operates as a deterministic way of finding an in-between time series between two original time series data points to make the training dataset more diverse. Therefore, data augmentation is also a viable way of enhancing data-driven methods' performance aside from feeding them synthetically generated datasets. [PERSON] et al. [84] proposed a data-generative AI model based on the Wasserstein Generative Adversarial Network (WGAN) architecture to balance scarce satellite RW datasets for use in training data-driven models. The utilized WGAN model comprises 1D convolutional layers within its generator and critic (discriminator).