# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
The CNN model that is shared across the three models is responsible for feature extraction whereas the subsequent structures are responsible for further processing and diagnostics. A combination of random search with grid search is used for pinpointing the optimal weights of the ensemble method. Through testing and comparing the model to different state-of-the-art models for this application, the proposed ensemble method mostly outperformed the other models at the cost of increased computational intensity. [PERSON] et al. [75] proposed a transformer and local interpretable model-agnostic explanations (LIME) combination to perform fault diagnosis on a fixed-wing UAV's elevator. The LIME strategy was used to increase the interpretability of the transformer model by locally approximating the output of the proposed transformer and examining the effect of different features in the final diagnosis. The LIME algorithm also gives insights about how to simplify the transformer further by specifying the features that do not contribute extensively to the diagnosis and could be dropped. Furthermore, [PERSON] et al. implemented a loss function to mitigate the problem of faulty categories' imbalance. The proposed methodology displayed high accuracy in diagnosing faults and efficient convergences owing to the utilized loss function. [PERSON] and [PERSON] [93] conducted work on satellite RW fault detection by proposing a simple One-Class Linear Regression (OC-LR) algorithm. The proposed OC-LR framework works by only training on nominal RW operational datasets which is curated using a simple polynomial representation of RW motor current's relationship with its angular velocity and acceleration. This simplified approach to monitoring the RW health state enables the algorithm to perform without the need for a rigorous mathematical model. Then, three detection metrics are used as follows: if a faulty data point falls within one, two, and three standard deviations of the training dataset's mean, there is a 68%, 95%, and 99.7% chance that it would be detected as a faulty data point, respectively. Through simulated and real-life tests, it was found that depending on how obvious the emerged fault is, any of the three detection metrics could surpass the other two, with their accuracy rates ranging from 76.7% to 99.1%. [PERSON] and [PERSON]'s proposed algorithm is relatively simple, readily applicable, and not demanding, especially when dynamic models and faulty operational datasets are not available. However, this approach carries a relatively higher risk of false positives and negatives that could be catastrophic in a space system. Moreover, solely performing anomaly detection while only outputting the anomaly's degree of deviation from the nominal operation may not be enough for a fault-tolerant subsystem to save the whole satellite system, especially considering that different faults with different dynamics that require dissimilar remedying measures might exhibit the same levels of deviation. One of the possible avenues for enhancing this methodology is to implement a voting strategy between the different detection metrics. [PERSON] and [PERSON] [46] introduced a Dynamic Neural Network (DNN)-based system for FDI in RWs of satellite formation flying (SFF) missions with a decentralized architecture. DNNs are trained with extended backpropagation using input/output data from the attitude control subsystem to model the nonlinear dynamics of each spacecraft. A fine-tuned set of DNN parameters is used to minimize estimation errors and to meet performance criteria. The methodology effectively detects low-severity actuator faults by employing local and neighboring spacecraft-based fault detectors. In the study, multilayer DNNs are trained based on relative attitude measurements and used to represent SFF dynamics. Using DNNs, [PERSON] and [PERSON] [63] developed an effective FDI system for Pulsed Plasma Thruster (PPT) used in ACS for SFF missions.