AI-Driven Predictive Maintenance in Mining Equipment

Sangwoo Jeon, Jueying Li, Saravanan Murugesan, Min Dugki

Abstract


AI-driven predictive maintenance is transforming the management of mining equipment by enhancing reliability and reducing downtime. This paper examines the methodologies and technologies employed in predictive maintenance systems, focusing on their applications in monitoring equipment health, predicting failures, and optimizing maintenance schedules. By presenting case studies, the paper highlights the benefits of implementing AI-driven predictive maintenance, including reduced operational costs, improved equipment lifespan, and increased overall productivity. Additionally, the challenges of integrating these systems into existing mining operations are discussed, along with future prospects for their advancement.

Keywords


AI, predictive maintenance, mining equipment, operational efficiency.

References


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