Condition based maintenance for mineral processing: Comparison of the remaining useful life predictions for pumps using adaptive auto-regressive and gamma process models, A.V. Cherkaev, S.M. Bradshaw, T.M. Louw, L. Auret, and D.V.J.W. Groenewald

Unexpected equipment failures lead to financial losses due to production disruption. Time-based maintenance, based on checks over regular time periods, has been employed to minimise disruption. However, the improvements it can offer are limited as it may lead to unnecessary service of healthy equipment or missed failures occurring between service intervals. Condition-based maintenance (CBM) provides a further improvement to equipment reliability by detecting faults and predicting remaining useful life (RUL) based on equipment health monitoring. Its application in the mineral processing industry is hindered due to the high cost of health monitoring equipment. In this study, two stochastic process models, the adaptive auto-regressive model and the gamma process, are compared in their ability to forecast the degradation state and to provide RUL prediction. The gamma process model was found to be effective in both tasks. Due to its simplicity and minimum hyperparameters requirements, it is proposed to use the gamma process as a baseline for comparison with other models. Keywords: Condition-based maintenance, remaining useful life, slurry pump, adaptive autoregressive mode, gamma process