# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
The developed model's accuracy during the hovering and rising phases reached about 100% and 98%, respectively, while using the proposed sampling method and outdid the results yielded by sequential sampling. The model's accuracy rate was also indifferent to various loading conditions. [PERSON] et al. [91] proposed an adaptive radial basis functions (ARBF) approach based on the Lyapunov function theory and sliding-mode concept-based methodology to tune the weight parameters of neural network-based observers. The Neural Network (NN) observers are then utilized for fault estimation of sensors onboard quadrotor UAVs. The ARBF method's performance is compared with conventional radial basis functions (CRBF), adaptive multilayer perceptron (AMLP), conventional multilayer perceptron (CMLP), and Extended State Observer (ESO) methods and was shown to be more accurate and efficient than them. [PERSON] et al. [22] developed a fault detection scheme based on unsupervised learning for diagnosing cyber-attacks and physical damages in the control systems of UAVs. This approach was incentivized by the shortcomings of supervised learning-based methods in requiring labeled data and failing to detect unexpected faults. The proposed model consists of a stacked autoencoder and uses thresholds for detecting faults. The input data features comprise UAV coordinates, attitude, IMU and sensors' data, and control inputs to the actuators. The next steps in this research could be adding to the features or incorporating sequential flight features, sequential autoencoders, or RNNs for enhanced detection performance. The presented model performed better in detecting Global Positioning System (GPS) spoofing attacks, denial-of-service (DoS) attacks, and rudder failure than in detecting elevator, aileron, and engine failures. [PERSON] et al. [23] developed a novel Siamese hybrid neural network (SHNN) scheme based on Few-Shot Learning (FSL) for fault diagnosis of fixed-wing UAVs. FSL is an ML approach specializing in learning from a limited dataset for mainly supervised learning-based applications and exhibits high generalizability [92]. The FSL strategies' advantages have been leveraged to compensate for the lack of sufficient faulty state data for training the data-driven models, considering that the available faulty data for fixed-wing UAVs are scarce. [PERSON] et al. carried out comprehensive experimental testing to validate the proposed scheme under the following conditions: different training data sizes, different flights, and unknown faults. The tests also demonstrate that the SHNN framework performs better than SVM and 1D CNN models for fewer training sample sizes. The future scope of this research includes utilizing attention mechanisms and alleviating the computational burden of the algorithm. The work by [PERSON] et al. [31] proposes an ensemble data-driven model for actuator fault diagnosis of UAVs, specifically the ailerons. The proposed ensemble method uses a weighted combination of three different hybrid models. The difference between ensemble and hybrid methods in this context is that ensemble methods take the final diagnosis output of different models and make a weighted decision based on each individual model's output and are more akin to a parallelized structure between the individual models. Whereas a hybrid data-driven model combines different architectures within one holistic model and their combination only outputs one diagnosis. Weighted ensemble architectures could be used to alleviate the dominance within lopsided models and increase the generalizability of the model. On the other hand, in a hybrid model, each segment is usually responsible for one duty, e.g., feature extraction. [PERSON] et al. used an overarching ensemble model of three hybrid models as follows: (1) A CNN-bidirectional LSTM (CNN-BiLSTM), (2) A CNN-bidirectional Gated Recurrent Unit (CNN-BiGRU), and (3) A CNN-bidirectional Gated Recurrent Unit-LSTM (CNN-BiGRU-LSTM).