Developing and evaluating predictive conveyor belt wear models

Conveyor belt wear is an important consideration in the bulk materials handling industry. We define four belt wear
rate metrics and develop a model to predict wear rates of new conveyor configurations using an industry dataset that
includes ultrasonic thickness measurements, conveyor attributes, and conveyor throughput. All variables are
expected to contribute in some way to explaining wear rate and are included in modeling. One specific metric, the
maximum throughput-based wear rate, is selected as the prediction target, and cross-validation is used to evaluate the
out-of-sample performance of random forest and linear regression algorithms. The random forest approach achieves a
lower error of 0.152 mm/megatons (standard deviation [SD] = 0.0648). Permutation importance and partial dependence
plots are computed to provide insights into the relationship between conveyor parameters and wear rate. This
work demonstrates how belt wear rate can be quantified from imprecise thickness testing methods and provides a
transparent modeling framework applicable to other supervised learning problems in risk and reliability.