Liner Wear Prediction Using Bayesian Regression Models and Clustering

Abstract

Chutes, bins, and hoppers are critical assets in bulk commodity handling. Sacrificial wear liners are employed to protect these assets from abrasive wear. An essential maintenance challenge is optimising the timing of liner replacements. Traditionally, episodic human inspections have been in place, but now, real-time wireless IoT sensing systems that measure liner thickness are being used. We propose a novel approach to estimate the remaining useful chute liner life. Instead of linear extrapolation based on individual sensor wear rates (commonly used in industry), we leverage a Clustered Bayesian Hierarchical Modeling (BHM). Two models are developed: Model 1 (Cluster Exemplar) uses parameters from the closest cluster exemplar, while Model 2 (Spatial and Temporal BHM) incorporates data from the active sensor, with prior distribution informed by Model 1. Data are drawn from a single hopper with 88 sensors, 20 of which reached their end-of-life threshold. Both Model 1 and Model 2 outperform the industry regression approach, significantly reducing over-prediction. Notably, Model 2 predicts remaining useful life within 95% credible intervals and identifies anomalous sensor performance. This innovative use of historical and adjacent sensor data enhances wear degradation prediction, contributing valuable insights to the literature.