# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 6 Conclusions and Future Work
### Literature Analysis  
According to the review strategy outlined in Section 1, the main details of the analyzed works are compiled in Tables 5-8. Each work's significance, advantages and disadvantages, and key features have been discussed in detail in the closing tables. However, in this section, we are going to carry out an analysis of the state of the literature in this field. One of the key characteristics of each work in the literature was the methodology that was employed in them. Through compiling all these works, we arrived at a methodology/framework classification that explains the relationships between the employed strategies. This classification is illustrated by Figure 10.  
In Figure 10, the three main classes that are presented include data-driven, model-based, and other methods. In the existing literature of this field, great emphasis was placed on data-driven and model-based methods with limited attention extended to other miscellaneous methods. Therefore, the same outlook is kept in this review to represent the current state of the existing works while shedding light on other methods, as well as pronounce their potential. The two main subcategories of data-driven methods are ML algorithms and deep learning, which are comprehensively discussed in Sections 4.1 and 5.1, as well as the commonly used dimensionality reduction techniques. Statistical models such as ARIMA are a simpler set of data-driven methods that share some aspects with ML techniques. Moving on to model-based methods, deterministic and stochastic dynamic system observers or state and parameter estimators are prevalent in this field. Deterministic methods are usually used when robustness to noise is not a strict requirement. Some works within this category also employed rule-based systems such as fuzzy logic-based works, and other probabilistic analysis methods which resemble the stochastic state and parameter estimators. The intersection of data-driven and model-based methods are referred to as hybrid methods and they have been covered in Section 4.3. The research into hybrid methods is not as prolific as its parent categories. These methods mostly include NN observer-based strategies or hierarchical structures of data-driven and model-based frameworks that get activated hierarchically and work in conjunction with each other. Moreover, the vibration analysis-based methods as well as human-operated condition monitoring were classified as other methods since they were not integral to the research trends of this field but influential nonetheless. The attention that data-driven and model-based methods have been met in recent years is also a testament to the effects of the Fourth Industrial Revolution (4 IR) or Industry 4.0 that has popularized the use of AI-based methodologies, big data analytics, and Internet of Things (IoT) (in autonomous FDP techniques and interconnected sensors and other components). The intersection of other methods and data-driven methods results in strategies that combine spectrograms with computer vision capabilities enabled by data-driven methods. In the same vein, digital twinning frameworks were identified as part of the intersection between other methods and model-based methods. Strictly speaking, digital twinning could be classified as a solely model-based methodology; however, these techniques usually require more insight into the other unrelated subsystems of the studied system than the unit FDP is carried out on. For example, many of the model-based methods in this field only require the models of the power subsystems to monitor their condition, whereas a digital twinning approach might need more context into the interactions of all the other subsystems with the power subsystem. For this reason, digital twinning approaches have been recognized to be a combination of model-based and other methods. To the authors' knowledge, the combination of all three categories of data-driven, model-based, and other methods does not exist in the literature.