An Agentic AI-based Architecture for Digital Twins Specialized in Predictive Maintenance: Application to Ball Mills

Mining operations are increasingly confronted with a multitude of challenges, including price volatility, declining ore grades, and escalating energy costs. These challenges are exacerbated by variations in mineral hardness, which contribute to accelerated wear on critical equipment. Among this machinery, ball mills are particularly susceptible to wear and component failures, leading to unplanned maintenance, costly downtime, and disruptions in production. This research seeks to enhance the capabilities of predictive maintenance (PdM), with a concentrated emphasis on Anomalous Behavior Detection (ABD) and Digital Twin (DT) technologies, specifically tailored for ball mill applications within a multi-agent AI system (MAS) framework. We present a novel architectural design that synergizes DT and ABD through a semi-autonomous multi-agent AI system comprising two primary agents: the PdM agent and the Quality Assurance Agent. The primary function of the PdM agent is to identify anomalous behavior, while the Quality Assurance Agent is tasked with assessing the implications of parameter modifications on mill efficiency. Furthermore, we described the principal challenges related to data quality, system integration, and real-time responsiveness that must be systematically addressed to facilitate successful implementations in the future.