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dc.contributor.authorRedondo Guevara, Raquel 
dc.contributor.authorHerrero Cosío, Álvaro 
dc.contributor.authorCorchado, Emilio 
dc.contributor.authorSedano, Javier
dc.date.accessioned2023-01-17T07:45:05Z
dc.date.available2023-01-17T07:45:05Z
dc.date.issued2020-06
dc.identifier.urihttp://hdl.handle.net/10259/7246
dc.description.abstractIn recent years, the digital transformation has been advancing in industrial companies, supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence, companies have large volumes of data and information that must be analyzed to give them competitive advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed, adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a multinational company in the automotive industry sector. Two real-life datasets containing data gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained results show that HUEPs is a technique that supports the continuous monitoring of machines in order to anticipate failures. This contribution to visual data analytics can help companies in decision-making, regarding FD and PdM projects.en
dc.description.sponsorshipThe authors would like to thank the vehicle interiors manufacturer, Grupo Antolin, for its collaboration in this research.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofApplied sciences. 2020, V. 10, n. 12, e4355es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectIndustry 4.0en
dc.subjectIndustrial internet of thingsen
dc.subjectSmart factoriesen
dc.subjectAdvanced manufacturingen
dc.subjectIndustrial big dataen
dc.subjectPredictive maintenanceen
dc.subjectVisualizationen
dc.subjectMachine learningen
dc.subjectClusteringen
dc.subjectExploratory projection pursuiten
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleA Decision-Making Tool Based on Exploratory Visualization for the Automotive Industryen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/app10124355es
dc.identifier.doi10.3390/app10124355
dc.identifier.essn2076-3417
dc.journal.titleApplied Sciencesen
dc.volume.number10es
dc.issue.number12es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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