Abstract

Vibratory screens are used in mining to classify mineral and send it to different pathways, normally using conveyor belts. Vibration analysis techniques are commonly used for condition monitoring and early detection of unforeseen failures to generate predictive maintenance. This paper proposes a novel solution to implement wireless sensors forming an instrumentation dedicated network combined with data-driven machine learning for monitoring vibrating screens. The system is optimized explicitly for vibratory equipment, which sets it apart from general-purpose condition monitoring systems. Embedded sensors are battery-powered and robust to withstand constant vibratory movement. The data used for training the machine learning models are gathered from a lab setup and discrete element simulations. The test bench consisted of a lab-scale vibratory screener, in which 3-axis accelerations, cumulative damage and wear are measured using sensors embedded in the rubber screens. Proposed data-driven machine learning models classify each screen condition in states according to the ISO 2372 standards for vibration severity. The system can identify random failures (based on test bench measured data) as progressive degradation failures over time (based on discrete element methods simulation results). The accuracy of the classification algorithms consistently ranges from 95% to 98%. Moreover, the system allows the early detection of unacceptable states up to 168 hours before the screen's end-of-life predation by an expert. The system is characterized for (i) avoiding unplanned downtime and consequently (ii) increase operational availability. The system is intended to notify users when an abnormal operation is detected and impending failure events in the early stage.