AI AND IoT FOR SMART TERMINALS: PREDICTIVE MAINTENANCE IN THE ERA OF DIGITALISED LOGISTICS CORRIDORS

Alessandro BERUTTI

Abstract


The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is transforming predictive maintenance in container terminals by enabling improved equipment reliability, safety, and sustainability. This paper presents a pilot-scale engineering validation conducted at Trans Misr Terminal (TMT), Alexandria, aimed at assessing the technical feasibility and methodological soundness of an AI- and IoT-based predictive maintenance framework under real operational conditions. The study was intentionally conducted on a single container-handling crane as a temporary pilot prior to full-scale deployment. Real-time IoT sensor data were integrated with vibration monitoring, periodic thermographic inspections, and oil-analysis diagnostics within a cloud-based analytics platform. Ensemble machine-learning techniques, including Random Forest and Quantile Random Forest models, were applied to support early degradation detection and uncertainty-aware maintenance planning. The pilot results indicate improved maintenance responsiveness and positive trends in reliability-related performance indicators such as Mean Moves Between Failures and Mean Time to Repair. While the findings are not statistically generalizable due to the limited scope, they confirm the engineering feasibility of AI-assisted predictive maintenance as a foundation for future terminal-wide implementation and smart, sustainable port operations. 

Keywords


Predictive maintenance, Artificial Intelligence, Machine Learning, IoT, Smart terminals, Reliability, Digital logistics corridors.

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DOI: https://dx.doi.org/10.21622/MARLOG.2026.15.1.65

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The International Maritime Transport and Logistics Journal (MARLOG)

E-ISSN: 2974-3141
P-ISSN: 2974-3133

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt