<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0121-750X</journal-id>
<journal-title><![CDATA[Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[ing.]]></abbrev-journal-title>
<issn>0121-750X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Distrital Francisco José de Caldas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-750X2022000300202</article-id>
<article-id pub-id-type="doi">10.14483/23448393.17742</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Metodología para el mantenimiento predictivo de transformadores de distribución basada en aprendizaje automático]]></article-title>
<article-title xml:lang="en"><![CDATA[Methodology for Predictive Maintenance of Distribution Transformers based on Machine Learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alvarez Q.]]></surname>
<given-names><![CDATA[Laura I.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lozano M.]]></surname>
<given-names><![CDATA[Carlos A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bravo M.]]></surname>
<given-names><![CDATA[Diego A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Santiago de Cali Valle del Cauca]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Santiago de Cali Valle del Cauca]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad del Cauca  ]]></institution>
<addr-line><![CDATA[Popayán Cauca]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>27</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-750X2022000300202&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-750X2022000300202&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-750X2022000300202&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Contexto: En este artículo describimos una metodología &#769;&#305;a que se ha establecido para programar el mantenimiento predictivo de transformadores de distribución en el Departamento del Cauca (Colombia) mediante aprendizaje automático.  Método: La metodología propuesta se basa en un modelo predictivo de clasificación que encuentra el número mínimo de transformadores de distribución propensos a fallar. Para verificar esto, el modelo fue implementado y probado con datos reales en el Departamento del Cauca (Colombia).  Resultados: Es posible lograr una solución efectiva para programar el mantenimiento predictivo de los transformadores de distribución mediante el uso de aprendizaje automático.  Conclusiones:  El modelo propuesto es una herramienta eficaz para los problemas de programación del mantenimiento preventivo de los transformadores de distribución.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Context: In this paper, we describe a methodology set up to schedule the predictive maintenance of distribution transformers in the Department of Cauca (Colombia) by means of machine learning.  Method:  The proposed methodology relies on a predictive classification model that finds the minimum number of distribution transformers prone to failure. To verify this, the model was implemented and tested with real data in the Department of Cauca (Colombia).  Results:  It is possible to achieve an effective solution for scheduling the predictive maintenance of distribution transformers by means of machine learning.  Conclusions:  The proposed model is an effective tool for problems involving the scheduling of preventive maintenance scheduling problems for distribution transformers.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[transformadores de distribución]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[mantenimiento predictivo.]]></kwd>
<kwd lng="en"><![CDATA[Distribution Transformers]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
<kwd lng="en"><![CDATA[Predictive maintenance.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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