Failure Prediction of Mining Dump Trucks Using Artificial Neural Networks

Abstract

Aim. The main focus is on developing a method for assessing and predicting the dependability of mining dump trucks using artificial neural networks. The key objectives are to solve the problem of telemetry data imbalance and identify complex time dependencies in sensor readings for accurate failure prediction.  Methods. The authors used standard data processing methods (linear interpolation, normalisation), fast Fourier transform for signal compression, and a hybrid neural network architecture combining LSTM and fully connected layers. The model was evaluated using the Precision, Recall, F1-score, and ROC-AUC metrics.  Results. The developed model demonstrated complete failure detection Recall = 1,0 with satisfactory separating ability AUC-ROC = 0,919. Applying a probabilistic approach to the target variable enabled a failure time window prediction accuracy of 97%.  Conclusion. The study confirmed the effectiveness of the proposed method for predictive maintenance of mining equipment. The proposed method can be recommended for industrial implementation. It may also extended to predict failures of other types of equipment.