A Hybrid AI Framework for Integrated Predictive Maintenance and Mineral Quality Assessment in Mining

In the mining industry, operational efficiency, equipment reliability, and mineral quality assessment are paramount for cost-effective and sustainable production. Traditional approaches often address equipment maintenance and quality control as separate challenges, leading to suboptimal operational synergy. This paper proposes a novel artificial intelligence (AI) framework that integrates predictive maintenance with real-time mineral quality assessment through advanced sensor fusion and deep learning. Our model leverages a hybrid architecture, combining Convolutional Neural Networks (CNNs) for analyzing visual and spectral data of iron ore with Long Short-Term Memory (LSTM) networks for processing temporal sensor data (vibration, thermal, acoustic) from critical equipment like crushers and conveyors. A dedicated fusion layer synthesizes these spatial and temporal features to simultaneously predict equipment failure probability and classify mineral quality. Validated on a real-world dataset from active iron ore mines, the system demonstrates a significant 20–30% reduction in projected maintenance downtime and a 15% improvement in mineral classification accuracy compared to baseline models while achieving real-time inference speeds of less than 10 milliseconds. This work underscores the transformative potential of unified AI-driven systems in enhancing the intelligence, resilience, and productivity of modern mining operations.