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

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