# Artificial Intelligence in Aviation: New Professionals for New Technologies
## 2 Related Works
### Artificial Intelligence in the Aviation Industry
#### 2.1.3 AI in Aviation Maintenance  
Maintenance is one of the most sought-after areas of application of artificial intelligence and, at the same time, the widest area for its applications.  
The study [18] explores the use of Convolutional Neural Networks in conjunction with autonomous drones for automating aircraft maintenance visual inspections. Building on previous work, the paper introduces techniques to improve defect detection performance, achieving higher accuracy rates in identifying issues like dents by focusing on specific image augmentations and pre-classification.  
The novel machine learning and IoT-based approach to predict the thermal performance of aircraft wing anti-icing systems is proposed in [19]. When tested, this approach using an artificial neural network proved more efficient and time-saving than traditional computational fluid dynamics methods, suggesting its potential application in aviation.  
The paper [20] surveys the use of statistical and machine learning methods to enable a more efficient, accurate, and data-driven analysis of aircraft environmental impacts like fuel burn, emissions, and noise, summarizing key research themes, representative works, and opportunities to further integrate these techniques to support the sustainability of aviation operations.  
The article [21] proposes using deep neural networks and transfer learning with images from aircraft lap joints to automatically detect corrosion with precision comparable to trained operators, which could support maintenance personnel and enable more automated condition-based maintenance.  
The study [22] delves into active vibration control in helicopters, focusing on individual blade control (IBC) to diminish vibration loads at the hub. By integrating various models and methods, including a fuzzy neural network, the research suggests that IBC can reduce hub vibration loads, offering valuable insights for designing helicopter vibration control laws.  
The four data-driven frameworks to predict the exhaust gas temperature baseline of aeroengines, essential for engine health analysis and flight safety, are proposed in [23]. Using data from the real engine, machine learning methods were trained, with the Generalized Regression Neural Network model showing the highest accuracy and efficiency, making it suitable for practical airline applications.  
The article [24] reviews the application of machine learning in lithium-ion battery research, specifically in material research, battery health estimation, and fault diagnosis, with a focus on aviation batteries and green aviation technology. The study explores the strengths and weaknesses of different machine learning applications, aiming to foster a deeper understanding and future advancements in the field.  
The paper [25] highlights the use of machine learning, specifically a Multilayer Perceptron neural network, to model the transient performance of aero engines, with a focus on heat transfer during transient operation. This model, trained on finite element simulation data and refined with measurements from aero engines, accurately replicates engine thermal transients.  
The data-driven approach for predicting base pressure in suddenly expanded flows, which affects the base drag in aerodynamic vehicles, is presented in [26]. Using machine learning models trained on data from response equations, the platform accurately predicts base pressure, which can help optimize base drag in rockets and missiles.  
The study [27] introduces machine learning techniques, specifically Deep Neural Network (DNN) and Random Forest Classifier (RFC), to predict null motions in a 4-Control Moment Gyroscope used for satellite attitude control. The RFC method shows superior accuracy over the DNN, allowing for reliable predictions of null motions even for maneuvers not in the training data.