# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 3 Review Methodology
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref.** & **Approach** & **Application** & **Fault Type** & **Measurement Parameters** \\ \hline
[5] & **D-D** LSTM & Single RW & Multiplicative & Motor current, RW rates \\ \hline
[12] & **D-D** ARIMA, LSTM & Single RW & Multiplicative & RW rates \\ \hline
[69] & **D-D** Chebyshev NN & Pyramid CMG & Multiplicative & Satellite rates \\ \hline
[70] & **M-B** (**Stoch.**) CAUKF and PF & Single RW & Multiplicative & Motor current, RW rates \\ \hline
[71] & **M-B** (**Stoch.**) PF & Single RW & Lubrication loss over time &
\begin{tabular}{l} Lubrication temperature, \\ Lubrication degradation \\ model, Lubricant’s injected \\ amount \\ \end{tabular} \\ \hline
[72] & **M-B** (**Stoch.**) TSKF, Entropy & & & \\ weight method, Laguerre function-based model predictive control & Quadrotor UAV Actuators & Degradation & — \\ \hline \hline \end{tabular}
\end{table} 
Table 6: An overview of papers on single-agent fault prognosis. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref.** & **Monitor Type** & **Approach** & **Application** & **Fault Type** & **Measurement Parameters** \\ \hline
[3,15,73] & FD, FId & \begin{tabular}{l} **D-D** Various \\ Neural Networks (CNN, LSTM) \\ \end{tabular} & UAV Sensors & \
\begin{tabular}{l} Operational, Bias, \\ Drift, Stuck Faults \\ \end{tabular} &
\begin{tabular}{l} Sensor Data (IMU, \\ Gyroscope, GPS) \\ \end{tabular} \\ \hline
[6,10,16,45,74] & FD, FId & \
\begin{tabular}{l} **M-B** (**Det.**) \\ Observer-Based \\ Methods \\ \end{tabular} & Satellite, UAV, and Other Systems & Multiplicative, Additive &
\begin{tabular}{l} Attitude Rates, \\ Angular Velocities, \\ Position Data \\ \end{tabular} \\ \hline  & \multicolumn{4}{c}{**Hybrid Approaches**} \\ \hline
[14,21,24,36,37] & FD, FIs, FId & \begin{tabular}{l} Combination of \\ Various \\ Algorithms (e.g., \\ SVM, Ensemble \\ Learning) \\ \end{tabular} & \
\begin{tabular}{l} Multiple \\ Applications \\ Including UAVs \\ and Power \\ Systems \\ \end{tabular} & Anomalies, &
\begin{tabular}{l} Multiple Sensor \\ Outputs Including \\ Voltage, Current, \\ and Attitude Data \\ \end{tabular} \\ \hline  & \multicolumn{4}{c}{**Fault Types**} \\ \hline
**1. Operational and Physical Attacks** & & & & & \\ \hline
[3,15,73] & FD, FId & \
\begin{tabular}{l} **Various** \\ **Data-Driven** \\ Methods** \\ \end{tabular} & UAV Sensors &
\begin{tabular}{l} Operational Faults, \\ Physical Attacks \\ \end{tabular} & Sensor Data \\ \hline
**2. Sensor and Actuator Faults** & & & & \\
[27,38] & FD, FIs, FId & \begin{tabular}{l} **Model-Based and** \\ Hybrid \\ \end{tabular} & Various UAV & \
\begin{tabular}{l} Stuck, Bias, and \\ Intermittent Faults \\ \end{tabular} &
\begin{tabular}{l} Sensor and \\ Actuator Data \\ \end{tabular} \\ \hline
**3. Systematic Anomalies and Failures** & & & & \\ \hline
[29,33] & FD &
\begin{tabular}{l} **Data-Driven** \\ Approaches** \\ \end{tabular} & Telemetry Data & Anomalies & Telemetry Sensor \\ \hline
**4. Comprehensive Fault Detection and Isolation** & & & & \\ \hline
[23,41,75] & FD, FId & \
\begin{tabular}{l} **Model-Based and** \\ **Data-Driven** \\ \end{tabular} & Satellite and UAV & Various Fault &
\begin{tabular}{l} Multiple \\ Measurement \\ Parameters \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: An overview of papers on single-agent fault diagnosis.