Health State Prediction and Performance Evaluation of Belt Conveyor Based on Dynamic Bayesian Network in Underground Mining

Abstract

To deal with the problem of weak prediction and performance evaluation capabilities of traditional prediction and evaluation methods, a method of health state prediction and performance evaluation of belt conveyor based on Dynamic Bayesian Network (DBN) is proposed. First, the belt conveyor sensor monitoring data are preprocessed to obtain the feature data set with labels. At the same time, qualitative and quantitative analyses and interval discretization are carried out from belt conveyor fault-causing elements to construct the DBN network. Then, the sample data are applied to the DBN network for training, and the DBN-based prediction and performance evaluation model is established. Finally, taking the real-time monitoring data of a belt conveyor in an underground mine as an example, a DBN-based belt conveyor health prediction and evaluation model is constructed to evaluate and predict the health of the equipment. The results show that the model could identify different operating conditions and failure modes and further improves the prediction accuracy.