Predictive Maintenance of Port Equipment (Published)
Contemporary port activities and operations have taken the form of sophisticated, mechanized systems such as cranes, conveyors, dry docks, and transport vehicles to maintain the efficiency of global trade. Nevertheless, the failure of any piece of equipment might lead to extensive downtime, logistical, and financial losses. Predictive maintenance (PdM), a process that uses data analysis tools and techniques to detect anomalies and predict equipment failures, is driven by Artificial Intelligence (AI) and advanced sensor analytics, and is transforming the way ports operate their critical assets. These AI models can predict when a component will fail and suggest prompt maintenance measures by continuously analyzing sensor data, including vibration, temperature, load, and hydraulic pressure. By adopting this proactive approach, unwanted downtime is reduced, equipment life is increased, and maintenance spending is optimized. The Internet of Things (IoT), which refers to the network of interconnected devices that communicate sensor data, and machine learning, combined with big data analytics, enable real-time updates on the condition of cranes, conveyors, and other port equipment. In this article, the author discusses the principles, architecture, and implementation of predictive maintenance for port equipment, the advantages of AI-based diagnostics, and the strategic roles of digital twins, virtual replicas of physical assets, and edge computing, which processes data near the source of generation. By presenting case studies and future viewpoints, the study reveals how predictive maintenance aligns with the goals of sustainable port operations, resource efficiency, and Industry 4.0. The results indicate predictive maintenance represents not just a technological enhancement but a fundamental shift toward data-driven decision-making and operational robustness across maritime logistics globally.
Keywords: Artificial Intelligence, IoT sensors, Operational Efficiency, port equipment, predictive maintenance