Remaining useful life prediction of grinding mill liners using an artificial neural network

• Liner wear influences the grinding performance in the context of metal recovery.
• We estimate remaining height and life of the liners with artificial neural network.
• Proposed model reduce downtime so is cost effective decision making on maintenance.
• With applying PCA the numbers of influencing factors for wear reduce from 9 to 5.
• Performance of the neural network model is consistent for both seen and unseen data.

Knowing the remaining useful life of grinding mill liners would greatly facilitate maintenance decisions. Now, a mill must be stopped periodically so that the maintenance engineer can enter, measure the liners’ wear, and make the appropriate maintenance decision. As mill stoppage leads to heavy production losses, the main aim of this study is to develop a method which predicts the remaining useful life of the liners, without needing to stop the mill. Because of the proven ability of artificial neural networks (ANNs) to recognize complex relationships between input and output variables, as well as its adaptive and parallel information-processing structure, an ANN has been designed based on the various process parameters which influence wear of the liners. The process parameters were considered as inputs while remaining height and remaining life of the liners were outputs. The results show remarkably high degree of correlation between the input and output variables. The performance of the neural network model is very consistent for data used for training (seen) and testing (unseen).