Artificial Intelligence Technologies for Aircraft Maintenance A Systematic Literature Review
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Published
Dec 28, 2025
Dmitry Pavlyuk
Iyad Alomar
Abstract
Effective aircraft maintenance is crucial in ensuring safety, reliability, and cost-effectiveness in the aviation industry. Recent research and industry developments in artificial intelligence (AI) raise the potential to transform various aspects of aircraft maintenance, including predictive maintenance, fault diagnosis, and aircraft health monitoring and management. This paper presents a systematic literature review of AI technologies such as Automated Reasoning and Deep Learning in aircraft maintenance, highlighting its challenges and prospects. An extensive literature search resulted in a final dataset of 696 publications, covering the 40-years period from 1984 till 2024 and describing AI applications in airworthiness management, aircraft health monitoring, and maintenance, repair, and overhaul operations. These publications were analyzed to identify key AI technologies and related aircraft maintenance processes, identifying trends, popular research venues, and underexplored areas. The review concludes with insights into AI adoption in aircraft maintenance and its potential implications for researchers, practitioners, educators, and other stakeholders.
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Keywords
machine learning, deep learning, remaining useful life, predictive maintenance, fault diagnosis, health monitoring, health management
References
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Doğru, A., Bouarfa, S., Arizar, R., & Aydoğan, R. (2020). Using convolutional neural networks to automate aircraft maintenance visual inspection. Aerospace, 7(12), 1–22. https://doi.org/10.3390/aerospace7120171
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Jiang, F., Jia, B., Wang, J., & Zheng, G. (2024). Research on Failure Cause Analysis Method Based on Aircraft Maintenance Records. 374–388. https://doi.org/10.1007/978-981-99-8864-8_36
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Kwakye, A. D., Jennions, I. K., & Ezhilarasu, C. M. (2024). Platform health management for aircraft maintenance – a review. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 09544100231219736. https://doi.org/10.1177/09544100231219736
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Lee, J., & Mitici, M. (2023). Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics. Reliability Engineering and System Safety, 230. https://doi.org/10.1016/j.ress.2022.108908
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Li, J., King, S., & Jennions, I. (2023). Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review. Machines, 11(4). https://doi.org/10.3390/machines11040481
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Mallioris, P., Aivazidou, E., & Bechtsis, D. (2024). Predictive maintenance in Industry 4.0: A systematic multi-sector mapping. CIRP Journal of Manufacturing Science and Technology, 50, 80–103. https://doi.org/10.1016/j.cirpj.2024.02.003
Meng, X., Jing, B., Wang, S., Pan, J., Huang, Y., & Jiao, X. (2024). Fault Knowledge Graph Construction and Platform Development for Aircraft PHM. Sensors, 24(1). https://doi.org/10.3390/s24010231
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Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
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Peng, K., Jiao, R., Dong, J., & Pi, Y. (2019). A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter. Neurocomputing, 361, 19–28. https://doi.org/10.1016/j.neucom.2019.07.075
Rajagopalan, S., Purohit, A., & Singh, J. (2024). Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions. Measurement Science and Technology, 35(10). https://doi.org/10.1088/1361-6501/ad5b7d
Ranasinghe, K., Sabatini, R., Gardi, A., Bijjahalli, S., Kapoor, R., Fahey, T., & Thangavel, K. (2022). Advances in Integrated System Health Management for mission-essential and safety-critical aerospace applications. Progress in Aerospace Sciences, 128. https://doi.org/10.1016/j.paerosci.2021.100758
Raoofi, T., & Yasar, S. (2023). Analysis of frontier digital technologies in continuing airworthiness management frameworks and applications. Aircraft Engineering and Aerospace Technology, 95(10), 1669–1677. https://doi.org/10.1108/AEAT-06-2022-0166
Rodríguez, D. A., Lozano Tafur, C., Melo Daza, P. F., Villalba Vidales, J. A., & Daza Rincón, J. C. (2024). Inspection of aircrafts and airports using UAS: A review. Results in Engineering, 22, 102330. https://doi.org/10.1016/j.rineng.2024.102330
Selvarajan, S., Manoharan, H., Shankar, A., Khadidos, A. O., Khadidos, A. O., & galletta, A. (2024). PUDT: Plummeting uncertainties in digital twins for aerospace applications using deep learning algorithms. Future Generation Computer Systems, 153, 575–586. https://doi.org/10.1016/j.future.2023.11.034
Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering and System Safety, 115, 124–135.
Tamilselvan, P., Wang, Y., & Wang, P. (2012). Deep belief network based state classification for structural health diagnosis. IEEE Aerospace Conference Proceedings. https://doi.org/10.1109/AERO.2012.6187366
Tang, X., Chi, G., Cui, L., Ip, A. W. H., Yung, K. L., & Xie, X. (2023). Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis. Sensors, 23(11). https://doi.org/10.3390/s23115295
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