# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 2 Preliminaries and Definitions
The major advantages of using these techniques are analyzing complex nonlinear systems through learning the patterns in their output and accommodating applications where either labeled or unlabeled datasets are available, eliminating manual work. Data-driven methods develop an understanding of the studied linear or nonlinear dynamic systems and essentially model them as black boxes capable of analyzing real systems' performance. However, this imposes two major challenges: (i) large amounts of data are needed for data-driven methods to become reliable, and (ii) they lack interpretability compared with other methods. For instance, a broad range of neural networks are utilized in literature for learning the features of nonlinear systems; however, generally, we do not have meaningful and effective access to the inner workings of neurons and how they reach a specific output. Meanwhile, model-based method outputs are easily justifiable since high-fidelity models are accessible and can be examined. Nevertheless, the advantages of data-driven methods outweigh their disadvantages, and although they must be extensively addressed, data-driven techniques remain one of the most viable approaches for health monitoring. Furthermore, data-driven approaches for health monitoring of satellites and UAVs mainly focus on examining time series datasets, as is warranted by the nature of these agents' operations. An underlying assumption in data-driven approaches is that operational time series datasets exhibit similar characteristics, which is needed to justify their use in such applications [17]. Since time series datasets constitute a substantial portion of the available training data for satellites, spacecraft, and UAVs, neural networks, particularly Recurrent Neural Network (RNN), have emerged as attractive approaches for conducting FDP on such datasets. Another option for FDP in satellites and UAVs is signal processing and frequency analysis. Figure 3: Fault diagnosis strategy. They consist of the two categories of parametric and nonparametric methods. Parametric techniques operate under the assumption that the analyzed signals conform to a specific mathematical model, such as autoregression and state-space models [18]. Parametric methods are most effective when the assumed model matches closely with the signal. On the other hand, nonparametric methods are those where the analyzed signals are not associated with a known model or their attributes are not well-known. For instance, the Fourier transform, fast Fourier transform (FFT), and wavelet transform. Nonparametric methods are more robust to deviations and more flexible in application. However, these methods suffer from spectrum leakage and the picket fence effect, causing inaccuracies in frequency estimation. The drawbacks of nonparametric methods have been addressed in the literature; however, some barriers still exist in utilizing them [18]. Signal processing methods can also be combined with data-driven methods. In the literature, they provide time-frequency spectrums from the system and train Convolutional Neural Network (CNN) models with them, leveraging the advantages of data-driven methods. One of the drawbacks of signal processing methods that discourage their use in the health monitoring of satellites and UAVs is their predisposition toward making false alarms [19]. A visual representation of the discussed monitoring modes and methodologies is provided in Figure 4.  
On the other hand, if a fault is to be addressed through a network of satellites or UAVs, we must be aware of the communication between these agents in all fault diagnosis methods. By evaluating the communication topology, we can determine the required quantity of observers/filters and the designed architectures to diagnose faults across the entire network. A communication graph/topology consists of nodes interconnected by edges. Directed edges create connections between groups of nodes in a single specified direction. Conversely, undirected graphs do not restrict the direction of information flow.