<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-06-23T01:42:19Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/7246" metadataPrefix="mods">https://riubu.ubu.es/oai/request</request><GetRecord><record><header><identifier>oai:riubu.ubu.es:10259/7246</identifier><datestamp>2023-01-18T01:05:20Z</datestamp><setSpec>com_10259_3847</setSpec><setSpec>com_10259_5086</setSpec><setSpec>com_10259_2604</setSpec><setSpec>col_10259_3848</setSpec></header><metadata><mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:namePart>Redondo Guevara, Raquel</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Herrero Cosío, Álvaro</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Corchado, Emilio</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Sedano, Javier</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2023-01-17T07:45:05Z</mods:dateAvailable>
</mods:extension>
<mods:extension>
<mods:dateAccessioned encoding="iso8601">2023-01-17T07:45:05Z</mods:dateAccessioned>
</mods:extension>
<mods:originInfo>
<mods:dateIssued encoding="iso8601">2020-06</mods:dateIssued>
</mods:originInfo>
<mods:identifier type="uri">http://hdl.handle.net/10259/7246</mods:identifier>
<mods:identifier type="doi">10.3390/app10124355</mods:identifier>
<mods:identifier type="essn">2076-3417</mods:identifier>
<mods:abstract>In recent years, the digital transformation has been advancing in industrial companies,&#xd;
supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence,&#xd;
companies have large volumes of data and information that must be analyzed to give them competitive&#xd;
advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive&#xd;
Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as&#xd;
Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid&#xd;
Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory&#xd;
Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed,&#xd;
adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon&#xd;
Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a&#xd;
multinational company in the automotive industry sector. Two real-life datasets containing data&#xd;
gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained&#xd;
results show that HUEPs is a technique that supports the continuous monitoring of machines in order&#xd;
to anticipate failures. This contribution to visual data analytics can help companies in decision-making,&#xd;
regarding FD and PdM projects.</mods:abstract>
<mods:language>
<mods:languageTerm>eng</mods:languageTerm>
</mods:language>
<mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by/4.0/</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">info:eu-repo/semantics/openAccess</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">Atribución 4.0 Internacional</mods:accessCondition>
<mods:subject>
<mods:topic>Industry 4.0</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Industrial internet of things</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Smart factories</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Advanced manufacturing</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Industrial big data</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Predictive maintenance</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Visualization</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Machine learning</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Clustering</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Exploratory projection pursuit</mods:topic>
</mods:subject>
<mods:titleInfo>
<mods:title>A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry</mods:title>
</mods:titleInfo>
<mods:genre>info:eu-repo/semantics/article</mods:genre>
</mods:mods></metadata></record></GetRecord></OAI-PMH>