Keywords
aviation engineering
predictive maintenance
air traffic management
integration
How to Cite
Abstract
This review paper investigates how machine learning (ML) has transformed multiple facets of aviation engineering. The work demonstrates substantial progress in flight operations and air traffic management (ATM) optimization through frameworks such as Reinforcement-Learning-Informed Prescriptive Analytics (RLIPA) and deep reinforcement learning (DRL) techniques applied to conflict resolution. The study highlights how ML contributes to operational efficiency through faster computational processes and better decision-making abilities for those who control air traffic. The paper examines how leading firms such as SpaceX and Raytheon use ML technology to enhance manufacturing processes, including predictive maintenance (PdM) and autonomous systems development. The paper discusses ML implementation obstacles, including model interpretability, and highlights further research requirements for adapting to real-world issues such as changing traffic volumes and weather variations. Overall, the study demonstrates how ML technology can transform aviation engineering through enhancements in safety standards as well as operational and process efficiency.
References
ADAM. (2020). How Machine Learning Models Can Benefit Aerospace Manufacturing. Acubed by Airbus. Retrieved December 7, 2024, from https://acubed.airbus.com/blog/adam/how-machine-learning-models-can-benefit-aerospace-manufacturing/
Agrawal, M., Puranik, T. G., Kalyanam, K., Mulholland, K., & Tan, R. (2024). Predicting Air Traffic Management Initiatives using Machine Learning. AIAA SCITECH 2024 Forum, 0535. https://doi.org/10.2514/6.2024-0535
Ahmadi, S. H. S., Suharyo, O. S., & Susilo, A. K. (2017). Selection Anti Submarine Sensor of Helicopter Using ELECTRE III Method. International Journal of Applied Engineering Research, 12(9), 1974–1981. https://www.ripublication.com/ijaer17/ijaerv12n9_30.pdf
Al-Ghzawi, M., & El-Rayes, K. (2024). Machine learning for predicting the impact of construction activities on air traffic operations during airport expansion projects. Automation in Construction, 158, 105189. https://doi.org/10.1016/j.autcon.2023.105189
Andreeva-Mori, A., & Onji, M. (2024). Traffic-dependent Airborne Delay Buffer Optimization for Efficient Air Traffic Flow Management under Uncertainties. TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES, 67(3), 145–153. https://doi.org/10.2322/tjsass.67.145
ANI. (2024). GE Aerospace using AI and machine learning to enhance quality of service. The News Mill. Retrieved December 7, 2024, from https://thenewsmill.com/2024/09/ge-aerospace-using-ai-and-machine-learning-to-enhance-quality-of-service/
Arnez, F., Espinoza, H., Radermacher, A., & Terrier, F. (2021). Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty. In Lecture notes in computer science (Vol. 12853, pp. 219–225). https://doi.org/10.1007/978-3-030-83906-2_17
Bati, F., & Withington, L. (2019). Application of Machine Learning for Aviation Safety Risk Metric. 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), 1–9. https://doi.org/10.1109/dasc43569.2019.9081657
Brown, W. L., Dabipi, I., Sharma, D., Zhang, L., Zhu-Stone, W., Mei, L., Wiggins, U. T., Cornelius, T. T., Jones, S., Wescott, R., Sharp, J. A., & Glenn, F. (2021). The Investigation of Logistic Regression Methods Applied to Engineering Education using Project Based Learning for Airport Systems Design. 2021 IEEE Frontiers in Education Conference (FIE), 1–5. https://doi.org/10.1109/fie49875.2021.9637205
Cai, Z. (2023). Overview of Research of Machine Learning in Air Traffic Management. Applied and Computational Engineering, 8, 103–112. https://doi.org/10.54254/2755-2721/8/20230091
Caricato, A., Ficarella, A., & Chiodo, L. S. (2021). Prognostic techniques for aeroengine health assessment and Remaining Useful Life estimation. E3S Web of Conferences, 312, 1–17. https://doi.org/10.1051/e3sconf/202131211017
Chen, G.-P., Chen, T.-S., & Chen, T.-C. (2024). Obstacle Avoidance for Flight Safety of Unmanned Aerial Vehicles Using Deep Reinforcement Learning. 2024 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), 1–5. https://doi.org/10.1109/apwcs61586.2024.10679316
Choi, H.-C., Deng, C., & Hwang, I. (2021). Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace. IEEE Access, 9, 151186–151197. https://doi.org/10.1109/access.2021.3126117
Chu, T., & Yin, C. (2024). Fault diagnosis for the aircraft system. International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 13071, 130710D. https://doi.org/10.1117/12.3025524
Cofer, D., Sattigeri, R., Amundson, I., Babar, J., Hasan, S., Smith, E. W., Nukala, K., Osipychev, D., Moser, M. A., Paunicka, J. L., Margineantu, D. D., Timmerman, L., & Stringfield, J. Q. (2022). Flight Test of a Collision Avoidance Neural Network with Run-Time Assurance. 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), 1–10. https://doi.org/10.1109/dasc55683.2022.9925743
Dalmau, R., Ballerini, F., Naessens, H., Belkoura, S., & Wangnick, S. (2021). An explainable machine learning approach to improve take-off time predictions. Journal of Air Transport Management, 95, Article 102090. https://doi.org/10.1016/j.jairtraman.2021.102090
Dangut, M. D., Skaf, Z., & Jennions, I. (2020). Aircraft Predictive Maintenance Modeling using a Hybrid Imbalance Learning Approach. TESConf 2020 - 9th International Conference on Through-life Engineering Services, 1–6. https://doi.org/10.2139/ssrn.3718065
Dangut, M. D., Skaf, Z., & Jennions, I. K. (2021). An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset. ISA Transactions, 113, 127–139. https://doi.org/10.1016/j.isatra.2020.05.001
De Giovanni, L., Lancia, C., & Lulli, G. (2024). Data-Driven Optimization for Air Traffic Flow Management with Trajectory Preferences. Transportation Science, 58(2), 540–556. https://doi.org/10.1287/trsc.2022.0309
Dhief, I., Alam, S., Lilith, N., & Mean, C. C. (2022). A machine learned go-around prediction model using pilot-in-the-loop simulations. Transportation Research Part C: Emerging Technologies, 140, Article 103704. https://doi.org/10.1016/j.trc.2022.103704
Do-Han, K. (2024). Why is Blue Origin investing on AI Talent. AIM Research. Retrieved December 7, 2024, from https://aimresearch.co/generative-ai/why-is-blue-origin-investing-on-ai-talent
Dube, A. (2024). Application of Deep Learning in Predictive Maintenance of Aircraft Engines. Darpan International Research Analysis, 12(3), 83–100. https://doi.org/10.36676/dira.v12.i3.58
Fremont, D. J., Chiu, J., Margineantu, D. D., Osipychev, D., & Seshia, S. A. (2020). Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI. In Lecture notes in computer science (Vol. 12224, pp. 122–134). https://doi.org/10.1007/978-3-030-53288-8_6
Gallego, C. E. V., Comendador, V. F. G., Carmona, M. a. A., Valdés, R. M. A., Nieto, F. J. S., & Martínez, M. G. (2019). A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction. Transportation Research Part C: Emerging Technologies, 107, 356–386. https://doi.org/10.1016/j.trc.2019.08.015
Garcia, A. B., Babiceanu, R. F., & Seker, R. (2021). Artificial Intelligence and Machine Learning Approaches For Aviation Cybersecurity: An Overview. 2021 Integrated Communications Navigation and Surveillance Conference (ICNS), 1–8. https://doi.org/10.1109/icns52807.2021.9441594
Gavra, V., & Van Kampen, E.-J. (2024). Evolutionary Reinforcement Learning: A Hybrid Approach for Safety-informed Intelligent Fault-tolerant Flight Control. AIAA SCITECH 2024 Forum, 0954. https://doi.org/10.2514/6.2024-0954
Gui, G., Zhou, Z., Wang, J., Liu, F., & Sun, J. (2020). Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data. IEEE Transactions on Vehicular Technology, 69(5), 4817–4826. https://doi.org/10.1109/tvt.2020.2981959
Gutierrez, P. (2024). Machine Learning Powers EGNOS Performance Prediction. Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design. Retrieved December 7, 2024, from https://insidegnss.com/machine-learning-powers-egnos-performance-prediction/
Hasan, G. M., Hasan, M., Liu, P., Rad, M., Bernier, E., & Hall, T. J. (2022). Optical Wavelength Meter with Machine Learning Enhanced Precision. arXiv (Cornell University), 1–18. https://doi.org/10.48550/arxiv.2203.07420
Haughn, K. P. T., Harvey, C., & Inman, D. J. (2023). Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing. arXiv (Cornell University), 1–16. https://doi.org/10.48550/arxiv.2304.03133
Helgo, M. (2023). Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis. Journal of Robotics Spectrum, 1, 90–99. https://doi.org/10.53759/9852/jrs202301009
Hermawan, A. P., Kim, D.-S., & Lee, J.-M. (2020). Predictive Maintenance of Aircraft Engine using Deep Learning Technique. 2020 International Conference on Information and Communication Technology Convergence (ICTC), 1296–1298. https://doi.org/10.1109/ictc49870.2020.9289466
Hu, X., Pan, X., & Wang, J. (2024). Horizontal Tail Flight Load Prediction Method for Large UAVs based on Machine Learning. Journal of Physics: Conference Series, 2762, 1–7. https://doi.org/10.1088/1742-6596/2762/1/012082
Ingole, O., Pande, A., Dongre, A., Jadhav, D., Dhamecha, D., Daspute, H., Komble, S., & Bhosale, T. (2022). Investigation of Different Regression Models For The Predictive Maintenance of Aircraft’s Engine. 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), 1–6. https://doi.org/10.1109/icerect56837.2022.10059651
Jacko, J. A. (2009). Human-Computer Interaction. Interacting in Various Application Domains. In Lecture Notes in Computer Science. Springer. https://doi.org/10.1007/978-3-642-02583-9
Jagannath, J., Jagannath, A., Furman, S., & Gwin, T. (2021). Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment. In Studies in computational intelligence (Vol. 984, pp. 25–82). https://doi.org/10.1007/978-3-030-77939-9_2
Jia, L., Ezhilarasu, C. M., & Jennions, I. K. (2023). Cross-Condition Fault Diagnosis of an Aircraft Environmental Control System (ECS) by Transfer Learning. Applied Sciences, 13(24), Article 13120. https://doi.org/10.3390/app132413120
Jia, Z., Li, Y., Wang, S., & Liu, Z. (2022). A multi-channel data-based fault diagnosis method integrating deep learning strategy for aircraft sensor system. Measurement Science and Technology, 34, Article 025115. https://doi.org/10.1088/1361-6501/aca219
Jin, W., Liu, H., & Shen, F. (2024). Artificial Intelligence in Flight Safety: Fatigue Monitoring and Risk Mitigation Technologies. Preprints, 1–11. https://doi.org/10.20944/preprints202409.2208.v1
Kabashkin, I., Misnevs, B., & Zervina, O. (2023). Artificial Intelligence in Aviation: New Professionals for New Technologies. Applied Sciences, 13(21), Article 11660. https://doi.org/10.3390/app132111660
Karaoğlu, U., Mbah, O., & Zeeshan, Q. (2023). Applications of Machine Learning in Aircraft Maintenance. Journal of Engineering Management and Systems Engineering, 2(1), 76–95. https://doi.org/10.56578/jemse020105
Katta, S. S., & Viegas, E. K. (2023). Towards a Reliable and Lightweight Onboard Fault Detection in Autonomous Unmanned Aerial Vehicles. 2023 IEEE International Conference on Robotics and Automation (ICRA), 1284–1290. https://doi.org/10.1109/icra48891.2023.10161183
Khelifi, A., Johnson, C. C., Thompson, L., Bouaynaya, N. C., Carannante, G., & Trabelsi, M. A. (2024). Advancing Cockpit Safety: Cost-Effective Flight Data Monitoring with Deep Learning. 80th Annual Vertical Flight Society Forum and Technology Display, FORUM 2024, 1–16. https://doi.org/10.4050/f-0080-2024-1239
Lee, H., Lim, H. J., & Chattopadhyay, A. (2021). Data-driven system health monitoring technique using autoencoder for the safety management of commercial aircraft. Neural Computing and Applications, 33, 3235–3250. https://doi.org/10.1007/s00521-020-05186-x
Li, L. (2021). Research on Comparison of Different Algorithms in Diagnosing Faults of Aircraft Engines. Journal of Aerospace Technology and Management, 13, 1–10. https://doi.org/10.1590/jatm.v13.1229
Liu, H., Xie, R., Qin, H., & Li, Y. (2024). Research on dangerous flight weather prediction based on machine learning. Journal of Physics: Conference Series, 2870, Article 012020. https://doi.org/10.1088/1742-6596/2870/1/012020
Liu, H., Zhao, Y., Zaporowska, A., & Skaf, Z. (2023). A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems. Neural Computing and Applications, 35, 2973–2989. https://doi.org/10.1007/s00521-021-06531-4
Liu, Y., Li, W., & Niu, W. (2022). Research on application of data driven PHM technology in aviation electromechanical system. International Conference on Mechanisms and Robotics (ICMAR 2022), 12331, 243–248. https://doi.org/10.1117/12.2652529
Lockheed Martin. (2024a). Lockheed Martin Leverages AI and Machine Learning to Revolutionize Defense and Space Technology. Retrieved December 7, 2024, from https://www.lockheedmartin.com/en-us/news/features/2024/lockheed-martin-leverages-ai-and-machine-learning-to-revolutionize-defense-and-space-technology.html
Lockheed Martin. (2024b). Lockheed Martin Awarded Contract to Develop Artificial Intelligence Tools for DARPA. Retrieved December 7, 2024, from https://news.lockheedmartin.com/2024-07-08-lockheed-martin-awarded-contract-to-develop-artificial-intelligence-tools-for-darpa
Lüdtke, A., & Möbus, C. (2005). A Case Study for Using a Cognitive Model of Learned Carelessness in Cognitive Engineering. Proceedings of the 11th International Conference of Human-Computer Interaction (HCI International´05), 1–5. https://oops.uni-oldenburg.de/1946/
Luettig, B., Akhiat, Y., & Daw, Z. (2024). ML meets aerospace: challenges of certifying airborne AI. Frontiers in Aerospace Engineering, 3, 1–26. https://doi.org/10.3389/fpace.2024.1475139
Luo, R., & Chen, X. (2023). Study on Deep Learning of Aircraft Safety Enhancement and Autonomous Flight Assistance. International Journal of Frontiers in Engineering Technology, 5(12), 88–96. https://doi.org/10.25236/ijfet.2023.051214
Lv, S., Sun, W., & Wang, H. (2024). Aircraft Fault Diagnosis and Prediction Algorithm Based on Deep Learning and Markov Model. 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS), 427–432. https://doi.org/10.1109/aiars63200.2024.00085
Madhavan, R. (2019). Artificial Intelligence Applications at Northrop Grumman – An Overview. Emerj Artificial Intelligence Research. Retrieved December 7, 2024, from https://emerj.com/artificial-intelligence-applications-at-northrop-grumman-an-overview/
Malik, I. (2024). How AI is Revolutionizing SpaceX’s Operations: Advancing Space Exploration. Mount Bonnell. Retrieved December 7, 2024, from https://www.mountbonnell.info/boca-chica-blastoff/the-role-of-ai-in-spacexs-operations
Melin, J. (n.d.). Moving Beyond the Hype of Predictive Maintenance. Honeywell Aerospace Technologies. Retrieved December 7, 2024, from https://aerospace.honeywell.com/us/en/about-us/blogs/moving-beyond-the-hype-of-predictive-maintenance
Mohammed, S., Chang, R.-S., Ramos, C., & Kim, T.-H. (2022). From the Editors of the Special Issue on Current Applications and Innovations of Artificial Intelligence and Machine Learning in Aerospace. IEEE Aerospace and Electronic Systems Magazine, 37(6), 4–5. https://doi.org/10.1109/maes.2022.3170740
Morales, C., Sanz, J., & Moral, S. (2017). Design of a software environment to support machine learning analysis of aircraft trajectories ( EIWAC 2017 ). In EIWAC 2017 5th ENRI International Workshop on ATM/CNS. https://www.semanticscholar.org/paper/Design-of-a-software-environment-to-support-machine-Morales-Sanz/eff024e05eb81cb3fb6a24c62385aacebf8eef42
Murthy, A., Buddhi, D., Averineni, A., Narayana, M. S., Sowjanya, G. D., & Sowmya, C. (2023). Machine Learning as A Predictive Technology and its Impact on Digital Markets. 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 672–676. https://doi.org/10.1109/icacite57410.2023.10182639
Noon, C. (2024). Squad Goals: GE Aerospace Brings Safety-First Mindset to AI Consortium. GE Aerospace. Retrieved December 7, 2024, from https://www.geaerospace.com/news/articles/squad-goals-ge-aerospace-brings-safety-first-mindset-ai-consortium
Northrop Grumman. (2023). Deep Learning Technologies Will Change How We Process Data. Retrieved December 7, 2024, from https://www.northropgrumman.com/life-at-northrop-grumman/deep-learning-technologies-will-change-how-we-process-data
Pahuja, R. (2024). Artificial Intelligence at Boeing – Two Use Cases. Emerj Artificial Intelligence Research. Retrieved December 7, 2024, from https://emerj.com/artificial-intelligence-at-boeing/
Park, C., Kim, G. S., Park, S., Jung, S., & Kim, J. (2023). Multi-Agent Reinforcement Learning for Cooperative Air Transportation Services in City-Wide Autonomous Urban Air Mobility. arXiv (Cornell University), 1–15. https://doi.org/10.48550/arxiv.2306.04137
Pearce, J. (2024). “Perfect marriage” between AI & humans: Rolls-Royce’s safety framework. TechInformed. Retrieved December 7, 2024, from https://techinformed.com/perfect-marriage-between-ai-humans-rolls-royce-ai-safety-framework/
Phadnis, A. (2023). Pratt & Whitney, Awiros launch AI-based aircraft engine analysis tool. Business Standard. Retrieved December 7, 2024, from https://www.business-standard.com/companies/news/pratt-whitney-awiros-launch-ai-based-aircraft-engine-analysis-tool-123062000600_1.html
Pratt & Whitney Customer Service. (2022). How Artificial Intelligence is Enhancing P&WC Engine Maintenance. Pratt & Whitney. Retrieved December 7, 2024, from https://www.prattwhitney.com/en/blogs/airtime/2022/03/10/how-ai-enhances-oil-analysis-technology
Pratt & Whitney. (2022). Artificial Intelligence Boosts Pratt & Whitney Canada’s Oil Analysis Technology to Deliver Proactive and Preventative Maintenance for Its Helicopter Customers. Retrieved December 7, 2024, from https://www.prattwhitney.com/en/newsroom/news/2022/03/08/artificial-intelligence-boosts-pwc-oil-analysis-technology-to-deliver-proactive-and-preventative
Pratt & Whitney. (2023). Pratt & Whitney and Indian Start-up Awiros launch AI-based Aircraft Engine Inspection Tool, Percept. Retrieved December 7, 2024, from https://www.prattwhitney.com/en/newsroom/news/2023/06/20/pratt-whitney-and-indian-start-up-awiros-launch-ai-based-aircraft-engine-inspec
Puranik, T. G., Rodriguez, N., & Mavris, D. N. (2020). Towards online prediction of safety-critical landing metrics in aviation using supervised machine learning. Transportation Research Part C: Emerging Technologies, 120, 102819. https://doi.org/10.1016/j.trc.2020.102819
Rahamathunnisa, U., Mohanty, A., Sudhakar, K., Jebamani, S. A., Udendhran, R., & Myilsamy, S. (2023). Machine Learning and Deep Learning for Intelligent Systems in Small Aircraft Applications. In Handbook of Research on AI and ML for Intelligent Machines and Systems (pp. 251–275). https://doi.org/10.4018/978-1-6684-9999-3.ch011
Rainey, K. (2024). Honeywell Introduces AI-Enabled Software Solution To Improve Commercial Aerospace Operations. Honeywell Aerospace Technologies. Retrieved December 7, 2024, from https://aerospace.honeywell.com/us/en/about-us/press-release/2024/04/honeywell-introduces-ai-enabled-software-solution-to-improve-commercial-aerospace-operations
Reim, G., & Norris, G. (2023). Virgin Galactic Sees Sweet Spot For Microgravity Research Flights. Aviation Week Network. Retrieved December 7, 2024, from https://aviationweek.com/space/commercial-space/virgin-galactic-sees-sweet-spot-microgravity-research-flights
RTX. (n.d.). Transformative Technologies. Retrieved December 7, 2024, from https://www.rtx.com/who-we-are/we-are-rtx/transformative-technologies
Saitata. (2023). How SpaceX Accelerates Space Exploration Using Artificial Intelligence. Medium. Retrieved December 7, 2024, from https://medium.com/teamcalai/how-spacex-accelerates-space-exploration-using-artificial-intelligence-a4f82692fbdc
Salvador, M. A. (2022). Challenges of Machine Learning for eVTOL Reliability and Safety. Journal of Mobile Multimedia, 19(01), 311–324. https://doi.org/10.13052/jmm1550-4646.19116
Sano, H. H., & Berton, L. (2022). Application of Deep Learning Models for Aircraft Maintenance. Anais Do XIX Encontro Nacional De Inteligência Artificial E Computacional, 787–797. https://doi.org/10.5753/eniac.2022.227575
Saxena, V., & Ak, S. (2023). Testing of Aircraft Failure using Machine Learning Algorithms. 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), 1900–1904. https://doi.org/10.1109/ic3i59117.2023.10398058
Shen, Y., Ma, L., & Khorasani, K. (2024). Hybrid Machine Learning-Based Methodologies for Fault Diagnosis, Prognosis and Health Management. 2024 36th Chinese Control and Decision Conference (CCDC), 4318–4325. https://doi.org/10.1109/ccdc62350.2024.10587398
Shuaia, H., Wang, J., Wang, A., Zhang, R., & Yang, X. (2023). Advances in assuring artificial intelligence and machine learning development lifecycle and their applications in aviation. 2023 5th International Academic Exchange Conference on Science and Technology Innovation (IAECST), 861–867. https://doi.org/10.1109/iaecst60924.2023.10503086
Smith, H. (2023). NASA Machine Learning Air Traffic Software Saves Fuel. NASA. Retrieved December 7, 2024, from https://www.nasa.gov/centers-and-facilities/ames/nasa-machine-learning-air-traffic-software-saves-fuel/
Sridhar, B., Chatterji, G. B., & Evans, A. D. (2020). Lessons Learned in the Application of Machine Learning Techniques to Air Traffic Management. AIAA AVIATION 2020 FORUM, 2882. https://doi.org/10.2514/6.2020-2882
Stanton, I., Munir, K., Ikram, A., & El‐Bakry, M. (2024). Data augmentation for predictive maintenance: Synthesising aircraft landing gear datasets. Engineering Reports, 1–19. https://doi.org/10.1002/eng2.12946
STP. (2021). Navy STP Technology Guide. 2021-22 Navy STP Cohort, 1–48. https://navyfst.com/wp-content/uploads/2021/10/FY-21-22-NAVY-STP-Cohort-Technology-Guide.pdf
Sui, D., Ma, C., & Dong, J. (2023a). Conflict resolution strategy based on deep reinforcement learning for air traffic management. Aviation, 27(3), 177–186. https://doi.org/10.3846/aviation.2023.19720
Sui, D., Ma, C., & Wei, C. (2023b). Tactical Conflict Solver Assisting Air Traffic Controllers Using Deep Reinforcement Learning. Aerospace, 10(2), Article 182. https://doi.org/10.3390/aerospace10020182
Taylor, C., Vargo, E., Manderfield, T., & Heitin, S. (2024). Teaching Artificial Intelligence Good Air Traffic Flow Management. Journal of Air Transportation, 32(4), 184–196. https://doi.org/10.2514/1.d0414
Timjerdine, M., Taibi, S., & Moubachir, Y. (2024). Leveraging AI and Industry 4.0 in Aircraft Maintenance: Addressing Challenges and Improving Efficiency. 2024 International Conference on Global Aeronautical Engineering and Satellite Technology (GAST), 1–6. https://doi.org/10.1109/gast60528.2024.10520779
Vaidyanathan, K., Danet, T., Tran, T. V., & Luu, Y. (2024). Improving flight planning efficiency through machine learning. Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 13051, Article 1305115. https://doi.org/10.1117/12.3015947
Wade, D. R., Wilson, A. W., & Aeromechanics Division, Aviation Engineering Directorate. (2017). Applying Machine Learning-Based Diagnostic Functions to Rotorcraft Safety. 17th Australian International Aerospace Congress: AIAC, 663–669. https://humsconference.com.au/Papers2017/Non_Peer_Reviewed/071_HUMS2017_Wade_HUMS_Best_Presentation.pdf
Wang, L., Yang, H., Lin, Y., Yin, S., & Wu, Y. (2022). Explainable and Safe Reinforcement Learning for Autonomous Air Mobility. arXiv (Cornell University), 1–13. https://doi.org/10.48550/arxiv.2211.13474
Wang, Y., Cai, W., Tu, Y., & Mao, J. (2023). Reinforcement-Learning-Informed Prescriptive Analytics for Air Traffic Flow Management. IEEE Transactions on Automation Science and Engineering, 21(3), 4188–4202. https://doi.org/10.1109/tase.2023.3292921
Wild, G., Baxter, G., Srisaeng, P., & Richardson, S. (2022). Machine Learning for Air Transport Planning and Management. AIAA AVIATION 2022 Forum, 3706. https://doi.org/10.2514/6.2022-3706
Xie, Y., Gardi, A., Sabatini, R., & Liang, M. (2022). Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations. 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), 1–9. https://doi.org/10.1109/dasc55683.2022.9925777
Xie, Y., Pongsakornsathien, N., Gardi, A., & Sabatini, R. (2021). Explanation of Machine-Learning Solutions in Air-Traffic Management. Aerospace, 8(8), Article 224. https://doi.org/10.3390/aerospace8080224
Yang, T., Li, G., Yuan, S., Qi, Y., Yu, X., & Han, Q. (2023). The LST-SATM-net: A new deep feature learning framework for aero-engine hydraulic pipeline systems intelligent faults diagnosis. Applied Acoustics, 210, 109436. https://doi.org/10.1016/j.apacoust.2023.109436
Yue, L., Yang, R., Zhang, Y., & Zuo, J. (2023). Research on reinforcement learning-based safe decision-making methodology for multiple unmanned aerial vehicles. Frontiers in Neurorobotics, 16, Article 1105480. https://doi.org/10.3389/fnbot.2022.1105480
Zang, H., Zhu, J., & Gao, Q. (2022). Deep Learning Architecture for Flight Flow Spatiotemporal Prediction in Airport Network. Electronics, 11(23), Article 4058. https://doi.org/10.3390/electronics11234058
Zhang, H., & Du, W. (2024). Research on fault diagnosis methods for certain civil aircraft based on human-machine interaction data enhancement. 2024 36th Chinese Control and Decision Conference (CCDC), 3253–3258. https://doi.org/10.1109/ccdc62350.2024.10588039
Zhou, J., Liu, Y., & Zhang, T. (2019). Fault diagnosis based on relevance vector machine for fuel regulator of aircraft engine. International Journal of Machine Learning and Cybernetics, 10, 1779–1790. https://doi.org/10.1007/s13042-018-0855-0