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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.ORCID iD: 0000-0001-7744-2155
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0003-1377-8180
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: 2021-10-15Bibliographically approved

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Hamodi, HussanLundberg, JanGhodrati, Behzad

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