# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 5 Prognostics
### Model-Based Methods  
Model-based strategies enable researchers to perform prognostics on a relatively straightforward but basic level when high-fidelity models are available compared with data-driven techniques. This is because data-driven techniques can accept numerous features that could affect the RUL of the studied agents, thus yielding more precise and descriptive estimations of agents' RUL. Incorporating the said features into models and accounting for coupling between different parameters could prove challenging and sometimes infeasible. Despite the established usage of model-driven techniques, their implementation in the fault prognosis area is scarce in the current literature. Some of the works in this area are reviewed in the following.  
[PERSON] et al. [70] suggested a model-based approach to the fault prognosis of RWs that is robust to Gaussian and non-Gaussian noises. After malfunction detection, the prognosis methodology activates by obtaining the fault-sensitive and nonmeasurable system parameters using the CAUKF scheme presented in [16] and outlined in Section 4.2. A PF is then used along with a health-decay model to estimate the damage model parameters and, through propagation in time, yields the RUL of the RW. The simulation test results demonstrate that implementing this approach would be conducive to acquiring accurate RW RUL estimations.  
[PERSON] and [PERSON] [71] designed a stochastic model-based methodology for RUL estimation of RWs onboard satellites that accounts for the bearing lubrication faults. The proposed methodology consists of three key steps: First, lubricant temperature state estimation using an adjusted PF; second, model parameter estimation via a two-step PF accepting the lubricant's injected volume, estimated temperature, and degradation model as inputs, and third, RW RUL estimation under two lubrication malfunction cases. [PERSON] and [PERSON] also presented a PF resampling method for enhancing the accuracy and efficiency of the filters. The adaptive resampling method used in conjunction with the PFs demonstrated more accurate results for higher measurement noises as compared with the adjusted PF.  
The proposed methodology's performance in estimating the RWs' RUL exhibited errors between 0.1 and 4%. Future research could involve developing hybrid model-based and data-driven methods for more confident results or enhancing the proposed methodology by monitoring more RW parameters.  
In the relatively unexplored field of UAV actuator fault prognosis, [PERSON] et al. [72] proposed a prognostic and predictive maintenance strategy to increase the mission lifetime of quadrotor UAV actuators. [PERSON] et al. first use a Two-Stage Kalman Filter (TSKF) to estimate the UAV state and actuator degradations in real time. To more easily analyze the degradation of multiple actuators, a comprehensive degradation variable was defined using the entropy weight method that assigns weights to actuators based on their degradation processes. Then, a health index is defined employing the Mahalanobis distance and three health stages are described based on the deviation of the UAV's health index from a nominal health index. Stage 1 encompasses the health conditions that are more degraded than the nominal state but do not influence the UAV performance heavily. Stage 2 is representative of health conditions that have lessened the UAV performance due to degradation and the inferred RUL may not realize the mission's requirements. Finally, the third stage describes the health states that are not able to meet mission stipulations. Then the results of these steps are used to autonomously extend the mission lifetime of the system. The proposed method of [PERSON] et al. is applicable to hovering UAVs, and future research could address extending their method to other flight states.