Artificial intelligence (AI) has transformed many industries in recent years and has changed the way how technology is perceived. AI has also influenced several aspects of aviation, including flight planning and optimization, air traffic control, safety, and passenger experience. This article explores the applications of AI in flight planning, autonomous flight control systems, predictive maintenance, air traffic management, and passenger experience enhancement.
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Flight Planning and Optimization
There have been many innovations and technological advancements in aviation since 1903, when the Wright brothers developed the first airplane. In this modern age, thousands of people travel by air daily, with more than 37000 daily flights counted in the European total network manager area, showing the extent of how important flight planning and optimization have become. 1
Traditionally, flight planning involves lengthy calculations and experience-based decisions. AI improves this process by analyzing vast amounts of data, including weather patterns, air traffic control (ATC) restrictions, fuel efficiency, and historical flight patterns, allowing airlines to operate more effectively. For instance, AI algorithms can create dynamic flight routes that avoid turbulence, traffic congestion, and unfavorable weather conditions. 1
Autonomous Flight Control Systems
AI-powered autonomous flight control systems are already making a significant impact in aviation by assisting pilots in various ways. The first automatic landing of a commercial flight was made by British Airways in 1965. 2 Present-day AI-based autoland systems can safely land aircraft in low-visibility conditions, improving safety and reducing the risk of human error during critical maneuvers.
Recent research predicts that pilotless aerial vehicles (PAVs) will replace traditional manned aircraft and UAVs in the near future. It emphasizes historical trends showcasing the diminishing role of pilots as technology advances, with modern autopilots handling various flight tasks with greater precision.
The study underlines the increasing capabilities of AI systems, citing examples like ALPHA, an AI defeating a human pilot in flight simulations and predicts a future where technological developments reshape military aviation, minimizing the need for human pilots. 3
Risk Assessment and Predictive Maintenance for Fault Detection and Safety
AI plays a vital role in predictive maintenance by continuously analyzing data from various aircraft sensors, identifying early signs of potential equipment failure, and allowing operators to assess potential risks and hazards, which reduces the risk of in-flight breakdowns and ensures aircraft safety.
For instance, in a 2023 study, researchers focused on enhancing predictive maintenance and fault detection in aircraft by employing Artificial Intelligence (AI) algorithms by utilizing Nonlinear Autoregressive Network with Exogenous Inputs (NARX) and Long Short-Term Memory (LSTM) neural networks, specifically designed for time-series forecasting. 5
These AI-based tools were developed for Remaining Useful Life (RUL) prediction of turbojet engines subject to compressor degradation. The Matlab&Simulink software, equipped with NASA's T-MATS library, was utilized to simulate engine performance during various flights. The NARX neural network outperformed the LSTM network in RUL prediction for a fleet of 40 engines experiencing compressor degradation, which demonstrated the potential of AI algorithms in predictive maintenance strategies optimization. 5
Air Traffic Management and Control
In a 2022 study, researchers addressed the challenges faced by the current air traffic management (ATM) system due to increasing flight volumes and diverse aircraft in European airspace.
The study introduces an augmented air traffic control (AATC) system utilizing long short-term memory (LSTM) networks to enhance situational awareness for air traffic controllers. The AATC system analyzes surveillance data, detecting conflicts and providing real-time insights. 1
The researchers employed large-scale, realistic air traffic models with diverse scenarios, including weather events, cyberattacks, emergencies, and human factors. The LSTM networks exhibited high accuracy, around 99%, in predicting various events, representing their potential in monitoring and classifying air traffic patterns.
The study emphasizes the importance of AI in transforming the traditional human-centric ATM system, enhancing safety, and addressing congestion and operational challenges. 1
Passenger Experience Enhancement
Aside from the utility of AI in air traffic management and flight planning, artificial intelligence can also enhance the experience of passengers. AI can analyze a passenger's travel history and preferences to suggest personalized flight options and travel deals and manage luggage more efficiently, reducing the risk of lost or misplaced baggage. Moreover, AI-powered chatbots can provide real-time information on flight status, answer passenger queries, and assist with booking changes. 4
Future Trends and Developments
Although the integration of AI in aviation is still in its early stages, it is expected to play a crucial role in the future. While human pilots will likely remain essential for the foreseeable future, advancements in AI could lead to autonomous passenger aircraft.
Digital assistants powered by artificial intelligence are expected to take over operational aviation scenarios, such as within aircraft cockpits and air traffic control operations rooms. 6 AI-powered facial recognition and other biometric technologies could streamline security checks and boarding processes, improving passenger experience and reducing wait times.
References and Further Reading
- Ortner, P., Steinhöfler, R., Leitgeb, E., & Flühr, H. (2022). Augmented air traffic control system—artificial intelligence as digital assistance system to predict air traffic conflicts. Ai. https://doi.org/10.3390/ai3030036
- Siegel, D., & Hansman, R. J. (2011). Development of an autoland system for general aviation aircraft. http://hdl.handle.net/1721.1/66604
- Heydarian Pashakhanlou, A. (2019). AI, autonomy, and airpower: the end of pilots?. Defence studies. https://doi.org/10.1080/14702436.2019.1676156
- Sarol, S. D., Mohammad, M. F., & Rahman, N. A. A. (2022). Mobile Technology Application in Aviation: Chatbot for Airline Customer Experience. In Technology Application in Aviation, Tourism and Hospitality: Recent Developments and Emerging Issues (pp. 59-72). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6619-4_5
- De Giorgi, M. G., Menga, N., Mothakani, A., & Ficarella, A. (2023, June). A data-driven approach for health status assessment and remaining useful life prediction of aero-engine. In Journal of Physics: Conference Series. IOP Publishing. https://doi.org/10.1088/1742-6596/2526/1/012071
- Kirwan, B. (2023). The Future Impact of Digital Assistants on Aviation Safety Culture. Human Interaction and Emerging Technologies (IHIET-AI 2023): Artificial Intelligence and Future Applications. Ahram, T., and Taiar, R.(Eds). https://doi.org/10.54941/ahfe1002932
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