Predicting the availability of continuous mining systems using LSTM neural network

Abstract

This work deals with a model development to predict the availability of continuous systems at the open pits using the artificial neural networks. The main idea of this work is to improve the analytical approach with initial assumption that the time length distributions of a faulty system have an exponential distribution. Data related to the I ECC(excavator, conveyors, crushing plant) system of the Open Pit Drmno Kostolac are used for this work. The aim of this work is to improve a model for predicting the availability of continuous systems at the open pits. On the basis of [Formula: see text], [Formula: see text], and [Formula: see text] values, presented in this work, it is concluded that the model, obtained by the use of neural network, has a higher predictive power compared to the analytical approach. A corresponding simulation is created on the basis of obtained model that should a scope of the system availability for each type of failure. Also, a more precise image of the availability of continuous systems at the open pits is given on the basis of simulation.