## 2 Materials and Methods
### Related Works
#### 2.2.6 AI for Aircraft Maintenance  
Artificial intelligence has notably become a critical component in aviation maintenance, with its application scope expanding broadly across the sector.  
The study [56] delves into the use of convolutional neural networks combined with autonomous drones for streamlining visual inspections in aircraft maintenance. Building upon prior research, the paper presents improved techniques for detecting defects such as dents, enhancing accuracy through targeted image processing and pre-classification methods.  
A pioneering approach that marries machine learning with IoT to predict the performance of aircraft wing anti-icing systems is introduced in [57]. Tests show that this artificial neural network-based method surpasses conventional computational fluid dynamics in efficiency and speed, demonstrating its potential utility in aviation maintenance.  
The paper [58] provides an overview of statistical and machine learning methodologies applied to analyze aircraft environmental effects, including fuel consumption, emissions, and noise. It synthesizes key studies and points to opportunities for these technologies to contribute to the sustainability of aviation practices.  
In [59], deep neural networks and transfer learning are utilized to automatically detect corrosion in aircraft lap joints, with accuracy on par with skilled inspectors, potentially streamlining maintenance workflows and enhancing condition-based maintenance practices.  
The study [60] explores active vibration control in helicopters, particularly focusing on individual blade control (IBC) to mitigate hub vibrations. Using fuzzy neural networks among other models, the research underscores IBC's effectiveness in reducing vibration, offering insights for the development of helicopter vibration control systems.  
The paper [61] proposes four data-driven frameworks for estimating the baseline exhaust gas temperature of aeroengines, a critical factor for engine health monitoring and flight safety. Among the tested machine learning models, the Generalized Regression Neural Network was highlighted for its superior accuracy and efficiency, suggesting its suitability for real-world airline deployment.  
An examination of machine learning applications in lithium-ion battery research is presented in [62], with particular emphasis on aviation-specific batteries and sustainable aviation technologies. The review assesses the strengths and limitations of various machine learning approaches, paving the way for future innovations in this field.  
The paper [63] showcases the application of a multilayer perceptron neural network to model aeroengine transient performance, focusing on thermal management during these periods. Leveraging both simulation data and real-world engine measurements, this model demonstrates accurate replication of thermal transients in engines.  
A data-oriented strategy to predict base pressure in suddenly expanded aerodynamic flows is introduced in [64]. This model, trained with existing response data, precisely forecasts base pressure, offering valuable data for optimizing base drag in aerospace applications such as rockets and missiles.  
The study [65] brings forward machine learning techniques, particularly deep neural networks, and random forest classifiers, for predicting null motions in control moment gyroscopes, which are pivotal for satellite attitude management. This approach exhibits remarkable accuracy, making it a reliable tool for anticipating null motions beyond the initial training scenarios.