<?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>0379-3982</journal-id>
<journal-title><![CDATA[Revista Tecnología en Marcha]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnología en Marcha]]></abbrev-journal-title>
<issn>0379-3982</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0379-39822017000300129</article-id>
<article-id pub-id-type="doi">10.18845/tm.v30i3.3279</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo de toma de decisiones de mantenimiento basado en la predicción de vida útil para componentes de sistemas eólicos en Costa Rica]]></article-title>
<article-title xml:lang="en"><![CDATA[Maintenance decision making model based on useful life prediction for wind power generation systems components installed in Costa Rica]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Loría-García]]></surname>
<given-names><![CDATA[Ana Laura]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villalobos-Granados]]></surname>
<given-names><![CDATA[Edgardo Mateo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Piedra-Santamaría]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2017</year>
</pub-date>
<volume>30</volume>
<numero>3</numero>
<fpage>129</fpage>
<lpage>141</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S0379-39822017000300129&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S0379-39822017000300129&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S0379-39822017000300129&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este estudio presenta un modelo de toma de decisiones de mantenimiento, donde se aplica el monitoreo de condición a componentes rotativos de aerogeneradores de eje horizontal instalados en Costa Rica, reduciendo la incidencia de fallas inesperadas, lo cual es consecuencia de la política de dejar fallar o apegarse a las recomendaciones del fabricante sin tomar en cuenta el entorno operativo, prácticas comunes en la industria eólica del país. Se ofrece un modelo donde se definen dos valores umbrales de probabilidad de falla, los cuales se utilizan en la toma de decisiones de reemplazo de componentes, con el fin de optimizar los costos de operación y mantenimiento. Además, se brindan las pautas iniciales para ejecutar esta estrategia de mantenimiento en un proyecto eólico. Las predicciones de los porcentajes de vida requeridas por el modelo ofrecido, se obtienen utilizando redes neuronales artificiales, las cuales tienen como entradas variables de condición representativas para cada componente en estudio (rotor, rodamiento principal, caja multiplicadora y generador eléctrico).]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This study covers a maintenance decision making model, where condition monitoring is applied to rotating components of horizontal axis wind power generation systems installed in Costa Rica, in order to reduce the incidence of unexpected failures, which are consequences of the run-to-failure policy or following strictly the manufacturers&#8217; suggestions without considering the operational environment, very common practices in the national wind industry. Aiming the optimization of operation and maintenance costs, a model where two failure probability threshold values are defined is presented. These threshold values allow the component replacement decision making. Moreover, the initial guidelines for executing this strategy in a wind farm are offered in this paper. The life percentage predictions required by the offered model, are obtained using artificial neural networks for each component (rotor, main bearing, gearbox and electric generator), which use representative condition monitoring variables as inputs.]]></p></abstract>
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<kwd lng="es"><![CDATA[Confiabilidad]]></kwd>
<kwd lng="es"><![CDATA[Turbinas Eólicas]]></kwd>
<kwd lng="es"><![CDATA[Redes Neuronales Artificiales]]></kwd>
<kwd lng="es"><![CDATA[Optimización de Costos]]></kwd>
<kwd lng="en"><![CDATA[Reliability]]></kwd>
<kwd lng="en"><![CDATA[Wind Turbines]]></kwd>
<kwd lng="en"><![CDATA[Artificial Neural Networks]]></kwd>
<kwd lng="en"><![CDATA[Cost Optimization]]></kwd>
</kwd-group>
</article-meta>
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