The integration of Artificial Intelligence (AI) into gold processing operations has significantly transformed maintenance strategies by minimizing unplanned downtime and improving operational efficiency. Traditional maintenance approaches, such as reactive and scheduled maintenance, often lead to either unexpected failures or excessive servicing. AI-driven predictive maintenance offers a proactive, data-driven alternative that leverages real-time monitoring and advanced analytics to forecast equipment failures and optimize maintenance schedules. Gold processing mills, which operate under harsh and variable conditions, require intelligent maintenance systems to manage equipment like crushers, ball mills, and classifiers effectively. This review explores how AI technologies—such as machine learning, deep learning, reinforcement learning, and natural language processing—are applied in predictive maintenance within gold processing contexts. It synthesizes methodologies from existing literature, identifies common themes, highlights gaps, and discusses the transition from preventive to predictive maintenance. The findings aim to guide both researchers and practitioners in implementing AI-based maintenance strategies that enhance equipment reliability, reduce costs, and support data-driven decision-making in the mining industry.
Ruvimbo et al. (Wed,) studied this question.