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Economic lifetime prediction of a mining drilling machine using an artificial neural network
Luleå University of Technology, Luleå, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-8524-3321
Luleå University of Technology, Luleå, Sweden.
Luleå University of Technology, Luleå, Sweden.
2014 (English)In: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 28, no 5, 311-322 p.Article in journal (Refereed) Published
Abstract [en]

This study develops models for predicting the economic lifetime of drilling machines used in mining. It uses three cases, each represented by a MATLAB code, to develop an optimisation model. The resulting ORT is fed as input to an artificial neural network (ANN) and the results translated into a relatively simple equation. The study finds that increasing the purchase price and decreasing the operating and maintenance costs will increase a machine's ORT linearly. Decreased maintenance cost has the largest impact on ORT, followed by increased purchase price and decreased operating cost. The ANN method gives a series of basic weight and response functions which can be made available to any engineer without the use of complicated software. It also helps decision-makers determine the best time economically to replace an old machine with a new one; thus, it can be extended to more general applications in the mining industry.

Place, publisher, year, edition, pages
2014. Vol. 28, no 5, 311-322 p.
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:mdh:diva-26439DOI: 10.1080/17480930.2014.942519ISI: 000343814000006Scopus ID: 2-s2.0-84908509788OAI: oai:DiVA.org:mdh-26439DiVA: diva2:759974
Projects
XPRES
Available from: 2014-11-02 Created: 2014-10-31 Last updated: 2016-01-18Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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  • nn-NB
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Output format
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