# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 3 Review Methodology
For example, comparing the results of fault classifiers proposed by different works is not easily and intuitively possible. Different classifiers are usually \begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref.** & **Approach** & **Strengths (+) / Limitations (–)** \\ \hline \multirow{3}{*}{[5,12]} & **D-D** & (+) Could operate even with limited historical data \\  & (+) Employs regressors for prognosis, which adds to the interpretability and quantitatively of the predictions \\ \hline \multirow{3}{*}{[69]} & **D-D** & (+) Eliminates the need for component-level measurements \\  & (+) Significantly diminishes the need for historical operational data \\ \hline \multirow{3}{*}{[70]} & **M-B** & (+) The methodology is robust to Gaussian and non-Gaussian noises \\  & (+) Does not require historical data due to being a M-B strategy \\  & (–) Implementation complexity and computational burden incurred by using CAUKF and PFs \\ \hline \multirow{3}{*}{[71]} & **M-B** & (+) Accounts for non-Gaussian noise \\  & (+) More accurate than classic PFs \\  & (+) Investigates the bearing lubrication faults in more detail using the lubrication temperature \\  & (–) Only accounts for lubrication faults as a result of temperature gradients \\ \hline \multirow{3}{*}{[72]} & **M-B** & (+) Real-time prognosis analysis of the UAV \\  & (+) Multiactuator RUL prediction \\  & (–) Nonlinear but stationary degradation models are employed for brushless Direct Current (DC) motors \\  & (–) Only addresses the hovering state of the UAV \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Papers on prognostics and their strengths and limitations. \begin{table}
\begin{tabular}{l l} \hline \hline
**Refs.** & **Strengths (+)/Limitations (–)** \\ \hline \multicolumn{3}{l}{**Fault Detection and Isolation Techniques**} \\ \hline \multirow{3}{*}{[45]} & (+) Considers simultaneous faults \\  & (–) Limited by over-actuated systems for isolating multiple faults \\  & (+) Instant fault detection via local observers \\  & (+) Reliable detection regardless of fault magnitude \\  & (+) Hybrid approach isolates low-severity faults \\  & (+) Structural perturbation minimizes observer errors \\  & (+) Identifies multiple faults in linear systems with correlated signatures \\  & (+) Operates with inaccurate information \\  & (+) Distributed FDI using local measurements \\  & (+) Decentralized control strategy satisfies communication constraints \\  & (+) Decentralized strategy for communication limitations \\  & (+) Switching method contains errors in specific parts \\ \hline \multicolumn{3}{l}{**Comm. Strategies**} \\ \hline \multirow{3}{*}{[55]} & (+) Decentralized control meets deep space communication needs \\  & (–) Healthy satellites must allocate more resources \\  & (+) Expanded framework for fault diagnosis in broader contexts \\  & (+) Nonlinear system capability in adverse conditions \\  & (+) Uses primary satellite data to diagnose faults without extra sensors \\  & (+) Develops decentralized UIO for actuator faults \\  & (+) Heterogeneous clusters reduce computational load \\  & (+) Similar advantages as previous entry \\  & (+) Robust against disturbances in varying conditions \\ \hline \multicolumn{3}{l}{**Resource Management and Performance**} \\ \hline \multirow{3}{*}{[63]} & (+) Reduces operational costs while enhancing reliability \\  & (+) MIMO structure analysis for optimality \\  & (+) CKF achieves superior estimation performance \\  & (-) Increases communication cost \\  & (+) Quaternion representation simplifies complex systems \\  & (-) Computational load managed by rule-based systems \\  & (+) Robustness through H-infinity method \\  & (+) Multiple-leader structure considers communication data noise \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Papers on multiagent fault diagnostics and their strengths and limitations.