NARX neural network model for predicting availability of a heavy duty mining equipment

In this work a neural network NARX model has been developed in order to predict availability of a heavy duty equipment of an important copper mining site in Chile. Four exogenous inputs have been considered (Number of Detentions, Mean Time to Repair, Mean Time between Failures and Use of Physical Availability) while Availability is the autoregressive variable. A 30 days moving average has been performed over the data. Results confirm that availability can be adequately multiple-step-ahead predicted using this arranged data and a NARX model including the 4 above mentioned variables as exogenous inputs.