# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
The proposed model consists of training several local classifiers whose outputs are used to train a meta-level classifier. The proposed mixed strategy mostly outperformed its constituent learners under noisy, noiseless, and mixed circumstances. Additionally, training using mixed noisy and noiseless datasets yielded more accurate results. However, one of the main shortcomings of mixed-learning strategies is that some mediocre learners within the scheme could negatively affect the overall mixed-learning strategy's performance [32]. The research conducted by [PERSON] et al. [29] presented a data-driven predictive method employing an Extreme Learning Machine (ELM) for detecting faults by analyzing satellite telemetry data. The proposed methodology of [PERSON] et al. first predicts the nominal operating data of the satellite. Then, it applies a static confidence interval to obtain a range containing the values corresponding to a normal operating state. ELMs are especially useful for time-sensitive applications such as satellite system fault diagnosis due to their learning speed and generalizability. They also require less expert manpower utilization [87]. The gray wolf optimization algorithm is also utilized to enhance the biases and input weights of the learner. Testing using the NASA shuttle valve benchmark dataset [88] achieved 98.5% to 99.6% accuracy in detecting deviations from nominal operating states, outperforming both unoptimized ELM and SVM. A fault detection method for satellite telemetry data is presented by [PERSON] et al. [33]. The detection method employs a graph neural network that considers the correlations between extracted features. Based on the cyclical operation schedule of satellites, malfunction thresholds are dynamically evaluated for detecting instances where the thresholds are exceeded. The proposed method demonstrated an accuracy of 98.30% for fault detection during testing on a satellite power system telemetry information dataset. Contrary to most ML methods, graph neural networks do not require the assumption of independent features as within graph-like data, nodes are interconnected, and there are dependencies between the nodes [89]. [PERSON] et al. [34] developed a ResNeXt-based slice residual attention network for the health monitoring of a CMG onboard spacecraft. The proposed data-driven method utilizes random slicing and an attention mechanism stage. As part of the data preprocessing phase of the proposed method, the short-time Fourier transform (STFT) is employed to generate spectrogram images to boost the diagnosis capability. The model identifies the CMG's fault states but falls short in accurately measuring each sub-component's fault severity. [PERSON] et al. expressed the prospects of converting the approach of distinct fault classification into a regression model for more rigorous fault diagnosis or using reinforcement learning to address more fault types. The research carried out by [PERSON] et al. [35] presented a CNN-based spacecraft and mobile robot CMG fault diagnosis scheme that comprises two attention-enhanced convolutional blocks. The input data are preprocessed into time-frequency spectrums usingthe STFT to enhance the fault diagnostic scheme. The scheme successfully classifies the overall CMG fault states; however, it fails to quantify each subcomponent's state of failure accurately. A demonstration of time-frequency transformation and the final images that get fed into the data-driven models is provided in Figure 8.  
[PERSON] et al. [90] developed two data-driven models to identify fault scenarios in a CMG onboard spacecraft. Both models employ the K-means algorithm to carry out diagnostics. However, one of the models utilizes the PCA for feature extraction, whereas the other uses the t-distribution random neighborhood embedding method based on the t-distributed Stochastic Neighbor Embedding (t-SNE) technique. Simulation results indicate the latter model provides significantly better performance. [PERSON] et al.