Identification with machine learning techniques of a classification model for the degree of damage to rubber-textile conveyor belts with the aim to achieve sustainability

Abstract This article presents the results of experimental research on belt conveyance systems. The main objective was to identify the correlations between the occurrence of significant damage in rubber-textile conveyor belts and the selected parameters (the type of falling material and the impact height). The conveyor belt specimens used in the experimental research were extracted from both a new and a renovated conveyor belt. Within the experimental research, four classification models were created, while the conveyor belt specimens used in the predefined experimental conditions were classified by assigning them to one of the two determined degrees of damage (significant or insignificant damage). The classification models were created by applying several machine learning methods, such as a regression analysis, logistic regression, decision trees, and the Naive Bayes classifier. The quality of the models was verified using the training and testing groups and three coefficients (overall accuracy, Kappa coefficient and AUC). An analysis of the results indicated that the type of falling material and the impact height had significant effects on the degree of conveyor belt damage, regardless of the conveyor belt type (new or renovated). An evaluation of the models indicated that all the designed classification models provided similar results. As for the quality coefficients, the classification models that were created by applying the decision tree and the Naive Bayes classifier exhibited the best classification and prediction abilities.