A novel IoT sensor and evolution model for grinding mill liner wear monitoring

Tracking mill liner wear is essential for improved plant reliability and grinding performance. This study developed a novel IoT wear sensor and a Discrete Element Modelling coupled methodology to predict and continuously evolve shell liner's wear pattern. The IoT sensor was purposely developed to track and report the live thickness of a shell liner. Global wear pattern was then obtained by coupling the qualitative wear intensity obtained in DEM and sensor results, from which a topological evolution model was established to generate the shell liner's progressive wear profiles. Predictions of the wear evolution model were compared with 3D laser scan measurements collected during operation. Results indicated that the wear evolution model showed less than 8% error with laser scan measurements in quantitative wear rate comparison.