AI-Based Predictive Maintenance for General Aviation Aircraft

Authors

  • Sam Suseelan

Keywords:

Artificial Intelligence, Predictive Maintenance, General Aviation, Machine Learning, Data Analytics

Abstract

Advances in artificial intelligence have brought many new possibilities into predictive maintenace. this happens especialy on general aviation. Predictive maintenance, which uses artificial intelligence to predict when machinery will break down, is revolutionizing how work needs to be done. It allows us to focus less on reparing things we've already broken and focus more on keeping things up and running smoothly. This paper analyzes how artificial intelligence is integrated into predictive maintenance systems with the goal of doing away with orderly current aircraft, analyzing methodologies that use data analytics and also predictive machine learning to predict the failure of components and schedule maintenance accordingly. This article talks about the large amount of benifits AI has on the aviation inditrly. Such as better sefety, less expence, and it makes everything run smoother. The Essay coves the issue on what challanges thease companies are facening they are facening challanges on trying to get their systems work. One thing that the AI Advanced PdM System does is it introduces future possibilities for technological advancements in PdM including, but not limited to, edge computing, real time data prediction, and autonomous maintanance. This paper delves deep into what the future holds for maintenance in the state of GA aviation with the use of AI.

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References

U. Saetti, J. F. Horn, and T. Berger, ‘‘On the effects of rotor induced vibrational stability on helicopter flight dynamics,’’ CEAS Aeronaut. J., vol. 15, no. 2, pp. 439–458, Feb. 2024.

L.-K. Young, P. Jeremiah, Y. Minori, K. Hussain, and I. Oh, ‘‘Predictive maintenance of aircraft systems using AI models developed with rotor blade vibration data,’’ in Proc. Aerosp. Symp. Awarded Best Paper, Jul. 2024.

Salierno, G.; Morvillo, S.; Leonardi, L.; Cabri, G. An architecture for predictive maintenance of railway points based on big data analytics. In Proceedings of the International Conference on Advanced Information Systems Engineering, Grenoble, France, 8–12 June 2020; pp. 29–40.

Stanton, I.; Munir, K.; Ikram, A.; El-Bakry, M. Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities. Syst. Eng. 2022, 26, 216–237.

Xiong, M.; Wang, H.; Fu, Q.; Xu, Y. Digital twin–driven aero-engine intelligent predictive maintenance. Int. J. Adv. Manuf. Technol. 2021, 114, 3751–3761.

Lima Romero, M.; Suyama, R. Agentic AI for Intent-Based Industrial Automation. arXiv 2025, arXiv:2506.04980.

Yang, H.; LaBella, A.; Desell, T. Predictive maintenance for general aviation using convolutional transformers. Proc. AAAI Conf. Artif. Intell. 2022, 36, 12636–12642.

Kulida, E.; Lebedev, V. About the Use of Artificial Intelligence Methods in Aviation. In Proceedings of the 13th International Conference on Management of Large-Scale System Development (MLSD), Moscow, Russia, 1–3 July 2020; pp. 1–5.

Degas, A.; Islam, M.R.; Hurter, C.; Barua, S.; Rahman, H.; Poudel, M.; Ruscio, D.; Ahmed, M.U.; Begum, S.; Rahman, M.A.; et al. A Survey on Artificial Intelligence (AI) and Explainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory. Appl. Sci. 2022, 12, 1295

Xie, Y.; Pongsakornsathien, N.; Gardi, A.; Sabatini, R. Explanation of Machine-Learning Solutions in Air-Traffic Management. Aerospace 2021, 8, 224.

Hurter, C.; Degas, A.; Guibert, A.; Durand, N.; Ferreira, A.; Cavagnetto, N.; Islam, M.R.; Barua, S.; Uddin Ahmed, M.; Begum, S.; et al. Usage of More Transparent and Explainable Conflict Resolution Algorithm: Air Traffic Controller Feedback. Transp. Res. Proc. 2022, 66, 270–278.

Hashemi, S.M.; Hashemi, S.A.; Botez, R.M.; Ghazi, G. Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl. Appl. Sci. 2023, 13, 9503.

Madeira, T.; Melício, R.; Valério, D.; Santos, L. Machine Learning and Natural Language Processing for Prediction of Human Factors in Aviation Incident Reports. Aerospace 2021, 8, 47.

Lee, H.; Madar, S.; Sairam, S.; Puranik, T.G.; Payan, A.P.; Kirby, M.; Pinon, O.J.; Mavris, D.N. Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning. Aerospace 2020, 7, 73.

Tikayat Ray, A.; Bhat, A.P.; White, R.T.; Nguyen, V.M.; Pinon Fischer, O.J.; Mavris, D.N. Examining the Potential of Generative Language Models for Aviation Safety Analysis: Case Study and Insights Using the Aviation Safety Reporting System (ASRS). Aerospace 2023, 10, 770.

Doğru, A.; Bouarfa, S.; Arizar, R.; Aydoğan, R. Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection. Aerospace 2020, 7, 171.

Brandoli, B.; de Geus, A.R.; Souza, J.R.; Spadon, G.; Soares, A.; Rodrigues, J.F., Jr.; Komorowski, J.; Matwin, S. Aircraft Fuselage Corrosion Detection Using Artificial Intelligence. Sensors 2021, 21, 4026.

Papakonstantinou, C.; Daramouskas, I.; Lappas, V.; Moulianitis, V.C.; Kostopoulos, V. A Machine Learning Approach for Global Steering Control Moment Gyroscope Clusters. Aerospace 2022, 9, 164.

PapakonstanKabashkin, I.; Misnevs, B.; Puptsau, A. Transformation of the University in the Age of Artificial Intelligence: Models and Competences. Transp. Telecommun. J. 2023, 24, 3923–3926.tinou, C.; Daramouskas, I.; Lappas, V.; Moulianitis, V.C.; Kostopoulos, V. A Machine Learning Approach for Global Steering Control Moment Gyroscope Clusters. Aerospace 2022, 9, 164.

Ke, L.; Zhang, G.; He, J.; Li, Y.; Li, Y.; Liu, X.; Fang, P. Pilot Selection in the Era of Virtual Reality: Algorithms for Accurate and Interpretable Machine Learning Models. Aerospace 2023, 10, 394.

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Published

28.02.2026

How to Cite

Sam Suseelan. (2026). AI-Based Predictive Maintenance for General Aviation Aircraft. International Journal of Intelligent Systems and Applications in Engineering, 14(1s), 77–88. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8123

Issue

Vol. 14 No. 1s (2026)

Section

Research Article

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