Predicting Abrasive Wear in Excavator Bucket Teeth using Cutting-Edge Vector Machines and Artificial Neural Networks

Abstract

The mining industry faces a growing need to accurately predict abrasive wear of excavator bucket teeth in order to establish effective maintenance policies. Hence, developing robust predictive models that can effectively track the deterioration of ground cutting tools in harsh operational environments is a logical strategy to address this challenge. This research compared the effectiveness of three vector machine models for predicting abrasive wear of excavator bucket teeth: the least squares support vector machine (LS-SVM), the relevant vector machine (RVM), and the support vector machine (SVM). To determine the most effective technique, the prediction results of these methods were assessed using metrics such as correlation coefficient (R), mean absolute error (MAE), Nash-Sutcliffe Efficiency (NSE), and mean square error (MSE). According to the evaluation results, the LS-SVM model outperformed both the RVM and SVM methods. The research included a comparison of the LS-SVM model's predictive performance to that of three reference ANN techniques: radial basis function network, backpropagation neural network, and generalised regression neural network. With MSE and RMSE values of 0.025726 and 0.160394, and R, R², and NSE values of 0.999900, 0.999800, and 0.999794, respectively, LS-SVM demonstrated to be the most accurate predictive model.