<?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>2737-6419</journal-id>
<journal-title><![CDATA[Revista Athenea]]></journal-title>
<abbrev-journal-title><![CDATA[Athenea]]></abbrev-journal-title>
<issn>2737-6419</issn>
<publisher>
<publisher-name><![CDATA[AutanaBooks, con el apoyo institucional de la Universidad Nacional Experimental Politécnica "Antonio José de Sucre", Vicerrectorado Puerto Ordaz, Venezuela]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2737-64192026000100028</article-id>
<article-id pub-id-type="doi">10.47460/athenea.v7i23.126</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Comparison of the effectiveness between the Wavelet Transform and the Wigner-Ville Transform for the diagnosis of low insulation in the starting transient of induction motors]]></article-title>
<article-title xml:lang="es"><![CDATA[Comparación de la efectividad entre la Transformada Wavelet y la Transformada de Wigner-Ville para el diagnóstico de bajo aislamiento en el transitorio de arranque de motores de inducción]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Marot]]></surname>
<given-names><![CDATA[Alfredo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Velásquez]]></surname>
<given-names><![CDATA[Sergio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,UNEXPO Vicerrectorado Puerto Ordaz Doctorado en Ciencias de la Ingeniería]]></institution>
<addr-line><![CDATA[Puerto Ordaz ]]></addr-line>
<country>Venezuela</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,UNEXPO Vicerrectorado Puerto Ordaz ]]></institution>
<addr-line><![CDATA[Puerto Ordaz ]]></addr-line>
<country>Venezuela</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2026</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2026</year>
</pub-date>
<volume>7</volume>
<numero>23</numero>
<fpage>28</fpage>
<lpage>40</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_arttext&amp;pid=S2737-64192026000100028&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_abstract&amp;pid=S2737-64192026000100028&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_pdf&amp;pid=S2737-64192026000100028&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This study demonstrates the superiority of the Discrete Wavelet Transform over the Wigner-Ville Transform in detecting insulation faults in induction motors during startup transients. The comprehensive analysis of 360 simulated signals revealed that the wavelet technique with Daubechies 10 achieves significantly higher classification accuracy (74.44% vs. 67.78%), substantially outperforming its counterpart. Certain decomposition levels showed maximum sensitivity with variations up to +354%, while diagnostic reliability indicators confirm its robustness. This technique positions itself as an optimal solution for predictive monitoring systems, enabling early fault detection that substantially reduces downtime and industrial maintenance costs.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este estudio demuestra la superioridad de la Transformada Wavelet Discreta frente a la Transformada de Wigner-Ville en la detección de fallas de aislamiento en motores de inducción durante transitorios de arranque. El análisis exhaustivo de 360 señales simuladas reveló que la técnica wavelet con Daubechies 10 alcanza una precisión de clasificación notablemente superior (74.44% vs. 67.78%), superando significativamente a su contraparte. Ciertos niveles de descomposición mostraron sensibilidad máxima con variaciones de hasta +354%, mientras que los indicadores de confiabilidad diagnóstica confirman su robustez. Esta técnica se posiciona como solución óptima para sistemas de monitoreo predictivo, permitiendo la detección temprana de fallas que reduce sustancialmente los tiempos de inactividad y los costos de mantenimiento industrial.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[wavelet transform]]></kwd>
<kwd lng="en"><![CDATA[Wigner-Ville transform]]></kwd>
<kwd lng="en"><![CDATA[induction motor]]></kwd>
<kwd lng="en"><![CDATA[fault diagnosis]]></kwd>
<kwd lng="en"><![CDATA[insulation failure]]></kwd>
<kwd lng="en"><![CDATA[signal processing]]></kwd>
<kwd lng="es"><![CDATA[transformada wavelet]]></kwd>
<kwd lng="es"><![CDATA[transformada de Wigner-Ville]]></kwd>
<kwd lng="es"><![CDATA[motor de inducción]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico de fallas]]></kwd>
<kwd lng="es"><![CDATA[falla de aislamiento]]></kwd>
<kwd lng="es"><![CDATA[procesamiento de señales]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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