Predictive Maintenance of Airport Critical Systems Using Artificial Intelligence and IoT: A Framework for Saudi Vision 2030

References

[1] General Authority of Civil Aviation (GACA). (2026). Saudi Aviation Strategy. Riyadh, Saudi Arabia.

[2] General Authority of Civil Aviation (GACA). (2026). Saudi 2030 Elevates Aviation: Innovation for Sustainable Aviation. Riyadh, Saudi Arabia.

[3] Costa, J., Farinha, J. T., Raposo, H., Marques Cardoso, A. J., Carmo, A., Gonçalves, P., & Farto, J. (2026). A systematic literature review on AI-driven predictive maintenance and fault detection in aircraft systems. Applied Sciences, 16(7), 3381. https://doi.org/10.3390/app16073381

[4] Mehdipour, N., et al. (2026). Integrating digital twins, data management and artificial intelligence for predictive maintenance in aviation: A comprehensive review. Digital Twins and Applications. https://doi.org/10.1049/dgt2.70029

[5] Agustian, E. S., & Pratama, Z. A. (2024). Artificial intelligence application on aircraft maintenance: A systematic literature review. EAI Endorsed Transactions on Internet of Things, 10(1). https://doi.org/10.4108/eetiot.6938

[6] Migliavacca, M., et al. (2023). Digital twins for aircraft maintenance and operation: A systematic literature review and an IoT-enabled modular architecture. Internet of Things, 24, 100991. https://doi.org/10.1016/j.iot.2023.100991

[7] Akinosho, T. D., et al. (2023). Improve predictive maintenance through the application of artificial intelligence: A systematic review. Results in Engineering, 20, 101645. https://doi.org/10.1016/j.rineng.2023.101645

[8] Alnoman, H., Bahroun, Z., & Hassan, N. (2025). A review of emerging airport technologies from a passenger-centric perspective. Proceedings of Engineering and Technology Innovation. https://doi.org/10.46604/peti.2025.14586

[9] International Organization for Standardization. (2014). ISO 55000: Asset management—Overview, principles and terminology. Geneva: ISO.

[10] International Organization for Standardization. (2018). ISO 31000: Risk management—Guidelines. Geneva: ISO.

[11] International Organization for Standardization. (2022). ISO/IEC 27001: Information security management systems—Requirements. Geneva: ISO.

[12] International Electrotechnical Commission. (2016). IEC 62443 series: Security for industrial automation and control systems. Geneva: IEC.

[13] International Civil Aviation Organization. (2018). Safety Management Manual (Doc 9859), 4th ed. Montreal: ICAO.

[14] International Civil Aviation Organization. (2022). Global Aviation Safety Plan 2023–2025. Montreal: ICAO.

[15] Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.

[16] Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.

[17] Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834.

[18] Carvalho, T. P., et al. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.

[19] Zonta, T., et al. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889.

[20] Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241–265.

[21] Susto, G. A., et al. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11(3), 812–820.

[22] Ran, Y., Zhou, X., Lin, P., Wen, Y., & Deng, R. (2019). A survey of predictive maintenance: Systems, purposes and approaches. arXiv:1912.07383.

[23] Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971.

[24] Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52.

[25] NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). Gaithersburg, MD: National Institute of Standards and Technology.

[26] NIST. (2024). Cybersecurity Framework 2.0. Gaithersburg, MD: National Institute of Standards and Technology.

[27] European Union Aviation Safety Agency. (2023). Artificial Intelligence Roadmap 2.0: A human-centric approach to AI in aviation. Cologne: EASA.

[28] Mobley, R. K. (2002). An Introduction to Predictive Maintenance (2nd ed.). Butterworth-Heinemann.

[29] Selcuk, S. (2017). Predictive maintenance, its implementation and latest trends. Proceedings of the Institution of Mechanical Engineers, Part B, 231(9), 1670–1679.

[30] Khoshkenar, A., et al. (2026). A holistic framework for airport spatial data maintenance: Bridging theory and practice. Digital Twins and Applications. https://doi.org/10.1049/dgt2.70021

@article{1722478,
      author = {Taimoor Shahid Malik},
      title = {Predictive Maintenance of Airport Critical Systems Using Artificial Intelligence and IoT: A Framework for Saudi Vision 2030},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {2922-2934},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722478.pdf},
      abstract = {Saudi Arabia’s aviation transformation under Vision 2030 requires airport infrastructure to support rapid growth while preserving safety, security, service continuity, and lifecycle value. Airport critical systems—including baggage handling systems, passenger boarding bridges, security screening equipment, closed-circuit television, access control, fire detection, power supplies, building management systems, communications networks, and airfield support assets—are tightly coupled. Failure in one subsystem can create cascading delays, congestion, security exposure, and reputational or financial loss. Traditional corrective and time-based preventive maintenance remain necessary but are insufficient for complex, sensor-rich environments in which degradation can be detected before functional failure. This review examines how artificial intelligence (AI), the Internet of Things (IoT), edge computing, digital twins, and enterprise asset-management platforms can enable predictive maintenance in Saudi airports. It synthesizes recent research on condition monitoring, anomaly detection, fault diagnosis, remaining useful life estimation, and maintenance decision support, while interpreting these capabilities within the operational and strategic context of the Saudi Aviation Strategy. The paper proposes a layered framework that links criticality analysis, secure IoT sensing, data governance, AI analytics, human validation, computerized maintenance management systems, and performance assurance. It also presents a phased implementation roadmap and a set of technical, operational, safety, cybersecurity, and economic indicators. The central argument is that predictive maintenance should not be implemented as an isolated algorithmic project. It should be treated as a safety-conscious, cyber-secure, human-governed asset-management transformation that supports airport reliability, capacity, workforce localization, and sustainable infrastructure under Vision 2030.},
      keywords = {predictive maintenance; artificial intelligence; Internet of Things; smart airports; airport critical systems; digital twins; condition monitoring; Saudi Vision 2030; aviation infrastructure; asset management},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722478}
  }