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Economic lifetime prediction of a mining drilling machine using an artificial neural network
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-5620-5265
Division of Product Realization, Mälardalen University, Eskilstuna.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
2014 (English)In: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 28, no 5, p. 311-322Article 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, p. 311-322
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-15070DOI: 10.1080/17480930.2014.942519ISI: 000343814000006Scopus ID: 2-s2.0-84908509788Local ID: e88326dd-a5fe-4a78-916a-a0a0fa000a58OAI: oai:DiVA.org:ltu-15070DiVA, id: diva2:988043
Note
Validerad; 2014; 20140813 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Hamodi, HussanLundberg, JanGhodrati, Behzad

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