Artificial intelligence approach to calculation of hydraulic excavator cycle time and output
Authors: Edwards, D.J.; Griffiths, I.J.
Source: Mining Technology : IMM Transactions section A, Volume 109, Number 1, April 2000 , pp. 23-29(7)
Publisher: Maney Publishing
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Abstract:
Accurate prediction of the cycle time and output of tracked hydraulic excavators is notoriously difficult, not least because such data are freely available to the mining practitioner from only a limited number of plant manufacturers. Previous research attempted to rectify this problem through the development of ESTIVATE. ESTIVATE utilized a multiple regression (MR) equation to predict machine cycle time and subsequently, on the basis of this, to estimate machine output and excavation costs. However, with a coefficient of determination (R2) of 0.88 and a mean absolute percentage error (MAPE) of 20%, the MR equation failed to provide an adequately robust predictor of machine cycle time. Improvement to ESTIVATE's predictive capacity was sought through the use of a feed-forward artificial neural network with back-propagation training. With a sum square error of 0.194 and a MAPE of 7% (that is, a 14% reduction on the equivalent MR equation) the feed-forward network provides a significant improvement over the MR equation. An aim in future work will be to expand the capability of the model to include long-reach machines.Document Type: Research article
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