<?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>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462024000401811</article-id>
<article-id pub-id-type="doi">10.13053/cys-28-4-4913</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Preprocesamiento de datos en el pronóstico de fallos de rodamientos para el mantenimiento predictivo]]></article-title>
<article-title xml:lang="en"><![CDATA[Data Preprocessing in Bearing Failure Prognostics for Predictive Maintenance]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Molina Salgado]]></surname>
<given-names><![CDATA[José Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López Sánchez]]></surname>
<given-names><![CDATA[Máximo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Santaolaya Salgado]]></surname>
<given-names><![CDATA[René]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castro Sánchez]]></surname>
<given-names><![CDATA[Noé Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico Nacional de México  ]]></institution>
<addr-line><![CDATA[Cuernavaca ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>28</volume>
<numero>4</numero>
<fpage>1811</fpage>
<lpage>1821</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462024000401811&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462024000401811&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462024000401811&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Este trabajo presenta un método de preprocesamiento de datos de vibración para la clasificación y predicción de fallos en rodamientos. Este método extiende el tiempo de previsión de fallos en comparación con trabajos anteriores, utilizando menos recursos computacionales. Actualmente, el mantenimiento industrial se realiza con el apoyo de sistemas computacionales, los cuales gestionan la información relacionada con el estado de salud de las máquinas. Las estrategias más utilizadas son el mantenimiento basado en la condición y el mantenimiento predictivo, los que se usan para maximizar la vida útil restante de una máquina o de un elemento de ella. La motivación para este trabajo surge de la observación de los procesos metodológicos utilizados para realizar el mantenimiento industrial, ya que se percibe que los procesos de procesamiento de datos requieren un conocimiento computacional significativo. Adicionalmente, estos procesos se llevan a cabo mediante técnicas que descuidan información importante como el origen o la manera en que se adquieren los datos. El método propuesto se utilizó para demostrar el comportamiento de diferentes algoritmos de clasificación como Máquinas de Vectores de Soporte, Bosques Aleatorios, Árboles de Decisión y algoritmos de regresión como la Regresión Lineal y Redes Neuronales sobre un conjunto de datos reales. Con esto, es posible realizar la previsión de fallos en rodamientos con más antelación, obteniendo una mejora del 74.4% en comparación con trabajos relacionados. Utilizar este método de preprocesamiento de datos para la creación de modelos de aprendizaje automático nos permite reducir la complejidad del proceso mientras disminuimos el número de procesos realizados. Esto representa una gran ventaja para la industria favoreciendo estrategias de mantenimiento.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: This work presents a method of vibration data preprocessing for the classification and prediction of bearing failures. This method extends the failure prediction forecast time compared to previous works, using fewer computational resources. Currently, industrial maintenance is carried out with the support of computational systems, which manage the information related to the health status of the machines. The most commonly used strategies are condition-based maintenance and predictive maintenance, which are used to maximize the remaining life of a machine or an element of it. The motivation for this work arises from the observation of the methodological processes used to perform industrial maintenance, as it is perceived that data processing processes require significant computational knowledge. Additionally, these processes are carried out through techniques that neglect important information such as the origin or the way the data is acquired. The proposed method was used to demonstrate the behavior of different classification algorithms such as Support Vector Machines, Random Forest, Decision Trees, and regression algorithms like Linear Regression and Neural Networks on a dataset of real data. With this, it is possible to make the forecast of bearing failures with more advance notice, obtaining an improvement of 74.4% compared to related works. Using this data preprocessing method for the creation of Machine Learning models allow us to reduce the complexity of the process while decreasing the number of processes performed. This represents a great advantage for the industry by favoring maintenance strategies.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Mantenimiento 4.0]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[preprocesamiento de datos]]></kwd>
<kwd lng="es"><![CDATA[vida útil restante]]></kwd>
<kwd lng="es"><![CDATA[previsión]]></kwd>
<kwd lng="es"><![CDATA[rodamientos]]></kwd>
<kwd lng="en"><![CDATA[Maintenance 4.0]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[data preprocessing]]></kwd>
<kwd lng="en"><![CDATA[remaining useful life]]></kwd>
<kwd lng="en"><![CDATA[forecast]]></kwd>
<kwd lng="en"><![CDATA[bearings]]></kwd>
</kwd-group>
</article-meta>
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