<?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>0121-7488</journal-id>
<journal-title><![CDATA[Ciencia en Desarrollo]]></journal-title>
<abbrev-journal-title><![CDATA[Ciencia en Desarrollo]]></abbrev-journal-title>
<issn>0121-7488</issn>
<publisher>
<publisher-name><![CDATA[Universidad Pedagógica y Tecnológica de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-74882025000100052</article-id>
<article-id pub-id-type="doi">10.19053/uptc.01217488.v16.n1.2025.16963</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Detección de fallas en motores sin escobillas mediante procesamiento de audio y aprendizaje automático]]></article-title>
<article-title xml:lang="en"><![CDATA[Fault detection in brushless motors through audio processing and machine learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Prieto]]></surname>
<given-names><![CDATA[Rommel S.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bravo]]></surname>
<given-names><![CDATA[Diego A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rengifo]]></surname>
<given-names><![CDATA[Carlos F.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Cauca  ]]></institution>
<addr-line><![CDATA[Popayán ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Cauca  ]]></institution>
<addr-line><![CDATA[Popayán ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad del Cauca  ]]></institution>
<addr-line><![CDATA[Popayán ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<volume>16</volume>
<numero>1</numero>
<fpage>52</fpage>
<lpage>61</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-74882025000100052&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-74882025000100052&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-74882025000100052&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los dispositivos electromecánicos tienden a desgastarse con el uso; la detección temprana de fallas es una herramienta importante para reducir los costos operativos y mejorar la vida útil de un dispositivo industrial. Este trabajo trata sobre la detección de fallas de motores de corriente continua sin escobillas utilizando el procesamiento de señales de audio y la extracción de características estadísticas y espectrales para entrenar modelos clásicos de aprendizaje automático como: k-vecinos más cercanos, árboles de decisión y máquinas de soporte vectorial. Luego, los modelos entrenados se implementan en una aplicación de internet de las cosas creada con Django. La metodología implementada muestra un porcentaje de acierto de hasta un 92 % de precisión para la detección de fallas en motores brushless usando procesamiento de audio y aprendizaje automático.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Electromechanical devices tend to wear off with use; Early fault detection is an important tool to reduce operating costs and improve the life of an industrial device. This work deals with fault detection of brushless DC motors, using audio signal processing and extracting statistical and spectral features to train classical Machine Learning models as k-Nearest Neighbors, Decision Trees and Máquinas de soporte vectorial (SVM). The trained models are then deployed to an IoT application built using Django. The implemented methodology shows a success rate of up to 92 % accuracy for fault detection in brushless motors using audio processing and machine learning.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Detección de Fallas]]></kwd>
<kwd lng="es"><![CDATA[Apredizaje Automático]]></kwd>
<kwd lng="es"><![CDATA[Motores Brushless]]></kwd>
<kwd lng="es"><![CDATA[Procesamiento de Audio]]></kwd>
<kwd lng="en"><![CDATA[Fault Detection]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
<kwd lng="en"><![CDATA[Brushless Motors]]></kwd>
<kwd lng="en"><![CDATA[Audio Processing]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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