Automated Predictive Maintenance and Adaptive Grinding Parameter Optimization in High-Throughput Rock Grinding Circuits Using Bayesian Neural Networks and Real-Time Vibration Signature Analysis

**Abstract:** This paper introduces a novel approach to enhancing efficiency and minimizing downtime in high-throughput rock grinding circuits via automated predictive maintenance and adaptive grinding parameter optimization. By leveraging Bayesian Neural Networks (BNNs) trained on real-time vibration signature analysis, the system accurately forecasts grinding mill component failures and dynamically adjusts operational parameters (grind size, slurry density, impeller speed) preemptively, optimizing throughput and reducing energy consumption. This system significantly surpasses traditional rule-based control methods and achieves demonstrably improved performance, adding substantial economic value to mining operations.