The evolution of AI-based maintenance in gold processing mills

AI-driven predictive maintenance has transformed gold processing operations by moving maintenance strategies from reactive schedules to data-driven prognostics. Advanced algorithms and platforms such as machine learning, deep learning, computer vision, IoT, and digital twins now analyze real-time sensor data to detect emerging faults days or weeks before failures occur. These technologies have been deployed globally, from Australia's Newcrest mining IoT platform to Chinese gold company Shandong Mining's smart conveyors to South African ball mill monitoring, yielding significant benefits. For instance, AI interventions at a gold mill in South Africa averted a motor failure that traditional vibration monitoring missed, while a U.S.-IoT-enabled “soft sensor” at an Australian gold operation cut unplanned downtime with a payback under three months. Across projects, maintenance costs have fallen by double-digit percentages and downtime by over 50%. This paper reviews the global evolution and trends of AI-based maintenance in gold mills, surveys key AI technologies (AI/ML, computer vision, expert systems, IoT, and digital twins), presents case studies from Australia, South Africa, Canada, and China, and discusses challenges, return-on-investment (ROI), and future directions such as expanded edge AI and digital twin integration.