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dc.contributor.authorGrishin, I. A.en
dc.contributor.authorVelikanov, V. S.en
dc.contributor.authorNazarov, O. V.en
dc.contributor.authorDyorina, N. V.en
dc.date.accessioned2022-10-19T05:20:17Z-
dc.date.available2022-10-19T05:20:17Z-
dc.date.issued2022-
dc.identifier.citationON THE POSSIBILITY OF USING THE LOCAL APPROXIMATION METHOd TO PREdICT IRREGULAR TIME SERIES OF MINING MACHINE FAILURES [О возможности использования метода локальной аппроксимации для прогноза нерегулярных временных рядов отказов горнотранспортных машин] / I. A. Grishin, V. S. Velikanov, O. V. Nazarov et al. // Ugol. — 2022. — Iss. 3. — P. 84-89.en
dc.identifier.issn415790-
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85130957864&doi=10.18796%2f0041-5790-2022-3-84-89&partnerID=40&md5=34accb6a521e154c1340bc7787cb92f3link
dc.identifier.urihttp://elar.urfu.ru/handle/10995/117903-
dc.description.abstractSustainable development of mining technologies implies the introduc-tion and implementation of the key messages and approaches of Industry 4.0. 'Industry 4.0' is generally applied to characterise new, advanced and potentially breakthrough technologies. digital transformation in the min-ing industry is primarily aimed at increasing productivity. the long-term programme for the development of the mining industry in the russian federation until 2030 sets the objective of a fivefold increase in productiv-ity and key level indicators by at least a factor of 2 to 3. at the same time, industrial and environmental safety standards require mining enterprises to operate machinery and equipment in good working order with the pos-sibility of continuous monitoring of their technical condition. reliability of mining equipment is a significant problem, so research aimed at further study of issues of forecasting breakdowns and failures, are relevant and in demand. the method of local approximation implemented in this work has the distinct advantage of using piecewise linear approximation instead of global-linear approximation, which is a typical autoregressive method. fore-casting remains an important step towards preventing mining equipment failures. Implementation at mining enterprises of an up-to-date efficient sys-tem for forecasting changes in the condition of mining and transportation equipment is a key tool for minimising its downtime, extending the service life of equipment and reducing the cost of equipment maintenance. It is established that failure rate of nodes and units of new generation mining loader-dumping machines is determined not only by mining and geological conditions of operation, but also by a considerable share of repair works that indicates a low level of maintenance and repair works of this type of machines. Implementation of a predictive model based on the method of local approximation of irregular time series of failures of mining transport machines will allow predicting the rational functioning of technological processes of mining of minerals in complex mining conditions. © I.A. grishin, V.S. Velikanov, O.V. Nazarov, N.V. dyorina, 2022en
dc.description.sponsorshipMinistry of Education and Science of the Russian Federation, Minobrnaukaen
dc.description.sponsorshipthis work was financially supported by the Ministry of Science and higher Education of the russian federation (Project No. fZru-2020-0011).en
dc.format.mimetypeapplication/pdfen
dc.language.isoruen
dc.publisherUgol' Journal Edition, LLCen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceUgolen
dc.subjectELEMENTen
dc.subjectEQUIPMENTen
dc.subjectFAILUREen
dc.subjectLOADERen
dc.subjectLOCAL APPROXIMATION METHODen
dc.subjectMININGen
dc.subjectMINING EQUIPMENT FAILURE PRE-DICTIONen
dc.subjectMINING INDUSTRYen
dc.titleО возможности использования метода локальной аппроксимации для прогноза нерегулярных временных рядов отказов горнотранспортных машинru
dc.title.alternativeON THE POSSIBILITY OF USING THE LOCAL APPROXIMATION METHOd TO PREdICT IRREGULAR TIME SERIES OF MINING MACHINE FAILURESen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.rsi48109689-
dc.identifier.doi10.18796/0041-5790-2022-3-84-89-
dc.identifier.scopus85130957864-
local.contributor.employeeGrishin, I.A., Nosov Magnitogorsk State Technical University, Magnitogorsk, 455000, Russian Federationen
local.contributor.employeeVelikanov, V.S., Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, 620002, Russian Federationen
local.contributor.employeeNazarov, O.V., SPM Siberia-Polymetals JSC, Korbalikhinsk mine, altai territory658471, Russian Federationen
local.contributor.employeeDyorina, N.V., Nosov Magnitogorsk State Technical University, Magnitogorsk, 455000, Russian Federationen
local.description.firstpage84-
local.description.lastpage89-
local.issue3-
local.contributor.departmentNosov Magnitogorsk State Technical University, Magnitogorsk, 455000, Russian Federationen
local.contributor.departmentUral Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, 620002, Russian Federationen
local.contributor.departmentSPM Siberia-Polymetals JSC, Korbalikhinsk mine, altai territory658471, Russian Federationen
local.identifier.pure29833761-
local.identifier.eid2-s2.0-85130957864-
local.fund.feuzFZRU-2020-0011-
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