Erregistro soila

dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorGorostegui Gabiria, Unai
dc.contributor.otherCampos, Jaime
dc.contributor.otherSharma, Pankaj
dc.contributor.otherJantunen, Erkki
dc.contributor.otherBaglee, David
dc.date.accessioned2020-06-16T08:17:38Z
dc.date.available2020-06-16T08:17:38Z
dc.date.issued2017
dc.identifier.issn2212-8271en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=154546en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/1690
dc.description.abstractThe paper highlights the characteristics of data and big data analytics in manufacturing, more specifically for the industrial asset management. The authors highlight important aspects of the analytical system architecture for purposes of asset management. The authors cover the data and big data technology aspects of the domain of interest. This is followed by application of the big data analytics and technologies, such as machine learning and data mining for asset management. The paper also presents the aspects of visualisation of the results of data analytics. In conclusion, the architecture provides a holistic view of the aspects and requirements of a big data technology application system for purposes of asset management. The issues addressed in the paper, namely equipment health, reliability, effects of unplanned breakdown, etc., are extremely important for today's manufacturing companies. Moreover, the customer's opinion and preferences of the product/services are crucial as it gives an insight into the ways to improve in order to stay competitive in the market. Finally, a successful asset management function plays an important role in the manufacturing industry, which is dependent on the support of proper ICTs for its further success.en
dc.description.sponsorshipUnión Europeaes
dc.description.sponsorshipFinnish Funding Agency for Technology & Innovation (TEKES)es
dc.language.isoengen
dc.publisherElsevier B.V.en
dc.rights© by the authorsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAsset Managementen
dc.subjectBig dataen
dc.subjectBig data analyticsen
dc.subjectData miningen
dc.titleA Big Data Analytical Architecture for the Asset Managementen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceProcedia CIRPen
local.description.peerreviewedtrueen
local.description.publicationfirstpage369en
local.description.publicationlastpage374en
local.identifier.doihttps://doi.org/10.1016/j.procir.2017.03.019en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/662189/EU/Cyber Physical System based Proactive Collaborative Maintenance/MANTISen
local.contributor.otherinstitutionhttps://ror.org/00j9qag85es
local.contributor.otherinstitutionhttps://ror.org/04b181w54es
local.contributor.otherinstitutionhttps://ror.org/04p55hr04es
local.contributor.otherinstitutionhttps://ror.org/049tgcd06es
local.source.detailsVol. 64. Pp. 369-374. Available online 3 June, 2017eu_ES
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


Item honetako fitxategiak

Thumbnail
Thumbnail

Item hau honako bilduma honetan/hauetan agertzen da

Erregistro soila

Attribution-NonCommercial-NoDerivatives 4.0 International
Bestelakorik adierazi ezean, itemaren baimena horrela deskribatzen da: Attribution-NonCommercial-NoDerivatives 4.0 International