# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
also indicated three ways of approaching the fault diagnosis of CMGs, namely, from the perspective of the system, assembly, and component. Their investigation into CMGs was conducted from a component-centric standpoint, considering both the physical and digital variables associated with CMGs for diagnostic purposes. [PERSON] and [PERSON] [25] proposed a data-driven framework utilizing an multiple-model adaptive estimation (MMAE) comprised of multiple autoencoders to perform fault diagnosis on spacecraft. Each autoencoder is trained on an operational mode of the spacecraft and a Bayesian probability framework is used to gauge the probabilities of each model correctly representing the real system. The proposed methodology was numerically and experimentally tested and demonstrated to be able to timely diagnose faults resembling the training dataset. However, there lies an issue with this approach. The issue is that due to each autoencoder being trained on a singular operational mode, the in-between modes and relatively unknown failures could cause uncertainties in the model performance even though it is capable of detection, isolation, and identification to some extent. Their model could be bolstered by training more autoencoders to account for new failure modes. [PERSON] and [PERSON] [37] introduced a data-driven approach for FDI in satellite thrusters. They transformed the input data from the thrusters into image representations and established distinct fault categories based on these images. Subsequently, a CNN was employed for binary image classification. Their testing results revealed accuracy rates ranging from 98.30% to 98.71%, varying according to the thrusters' axis directions. Figure 8: An example of generating spectrograms from RW time series signals for use in image-based data-driven models. [PERSON] et al. [3] developed a data-driven approach to detect and identify malfunctions in UAVs using sensor data from the Inertial Measurement Unit (IMU). Their proposed model combines a convolutional and bidirectional LSTM autoencoder for fault detection and a fault identification system based on CNN and LSTM. The identification component is activated only upon detecting a malfunction. During testing with empirical data, the detection module achieved an accuracy rate exceeding 90%, while the identifier module reached 85%. [PERSON] et al. suggested that future work should involve applying their techniques to diverse UAV fleets and evaluating their method's performance in such scenarios. An LSTM-based multivariate regressor using residual filtering is proposed by [PERSON] et al. [73] for fault detection and recovery of UAVs. The proposed data-driven model automatically derives spatial and temporal correlations as features and utilizes a filter to cancel out the effect of random noise. Fault detection is accomplished by applying thresholds to the estimated parameters, and recovery is achieved by using the model's regressive nature to reconstruct the detected faulty data. The model's accuracy rate in detecting faults was determined by tests to be 99% and 93% for the gyroscope sensor's bias and drift faults, respectively, which outperforms the least-squares SVM and the LSTM model without residual filtering. [PERSON] et al. also indicated that the possible future steps in developing this model are considering different fault magnitudes and flight modes' interference and addressing the online monitoring requirements by applying the model to an embedded monitoring unit. [PERSON] et al. [26] proposed a data-driven approach to the mechanical fault diagnosis of damaged and cracked UAV rotors. The proposed methodology employs a 1D CNN to estimate faults using rotors' surface vibration acceleration signal as input during hovering and rising. [PERSON] et al. found that the preprocessing of input data significantly affects the model's performance in identifying small faults. Consequently, they used interval sampling instead of sequential sampling to reconstruct the vibration signals to obtain better results in diagnosing minor faults.