<?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>1815-5928</journal-id>
<journal-title><![CDATA[Ingeniería Electrónica, Automática y Comunicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[EAC]]></abbrev-journal-title>
<issn>1815-5928</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59282023000300026</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Arquitectura distribuida para la detección de fallos en equipos industriales con mejor puntuación de precisión e índice de robustez]]></article-title>
<article-title xml:lang="en"><![CDATA[Distributed architecture for fault detection in industrial equipment with improved Precision Score and Robustness Index]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Ramos]]></surname>
<given-names><![CDATA[Yandy]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fernández-Aballí Altamirano]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cárdenas Barrera]]></surname>
<given-names><![CDATA[Julian L.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera Fernández]]></surname>
<given-names><![CDATA[Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,CEO SORBOTICS  ]]></institution>
<addr-line><![CDATA[Jacksonville Florida]]></addr-line>
<country>USA</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,University of New Brunswick  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Canada</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Central "Marta Abreu "  ]]></institution>
<addr-line><![CDATA[Las Villas ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>44</volume>
<numero>3</numero>
<fpage>26</fpage>
<lpage>40</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59282023000300026&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59282023000300026&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59282023000300026&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La creación de algoritmos y sistemas capaces de procesar y almacenar grandes cantidades de datos representa un gran reto científico, económico y práctico. La aplicación del aprendizaje automático (ML) a estos problemas no es trivial, y menos aún si el procesamiento de estos algoritmos necesita ser distribuido para manejar la gran carga computacional del análisis de datos y la toma de decisiones. Este trabajo presenta una arquitectura distribuida y robusta para entrenar, desplegar y ejecutar pipelines distribuidos de algoritmos de detección de fallos mejorando su Robustez y Precisión. La solución se basa en Smart Operational Realtime Bigdata Analytics (SORBA), una arquitectura distribuida patentada. La arquitectura combina las métricas de robustez y precisión para optimizar automáticamente la selección de algoritmos de aprendizaje automático de detección de fallos industriales y sus hiperparámetros. Se desarrolla un sistema de módulos para la adquisición, normalización, acondicionamiento de datos, entrenamiento, despliegue y ejecución en línea de pipelines de algoritmos de aprendizaje automático. La solución se validó comparando los resultados de Machine Learning (ML) de dos casos de uso: un motor industrial y una batería de locomotora, con los obtenidos con Spark. Los experimentos mostraron una mejora media de la puntuación de precisión del 28,76% y del índice de robustez del 10,9%. La solución agiliza la implementación de aplicaciones de éxito y mejora el rendimiento de estos indicadores con respecto a las soluciones disponibles actualmente en la MLlib de Spark.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Creating algorithms and systems that can process and store large amounts of data represents a great scientific, economic, and practical challenge. The application of machine learning (ML) to these problems is not trivial, and even less so if the processing of these algorithms needs to be distributed to handle the large computational load of data analysis and decision making. This paper presents a distributed and robust architecture to train, deploy, and execute distributed failure detection algorithm pipelines improving their Robustness and Precision. The solution is based on Smart Operational Realtime Bigdata Analytics (SORBA), a patented distributed architecture. The architecture combines the metrics of Robustness and Precision to automatically optimize the selection of industrial failure detection machine learning algorithm pipelines and their hyperparameters. A system of modules is developed for the acquisition, normalization, data conditioning, training, deployment, and online execution of machine learning algorithm pipelines. The solution was validated by comparing the Machine Learning (ML) results of two use cases: an industrial motor and a locomotive battery, with those achieved with Spark. The experiments showed an average improvement on the Precision Score of 28.76% and Robustness Index of 10.9%. The solution streamlines the implementation of successful applications and improves the performance of these indicators with respect to the solutions currently available in the Spark MLlib.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Industrial failure detection]]></kwd>
<kwd lng="en"><![CDATA[distributed architecture]]></kwd>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="en"><![CDATA[Industrial data processing]]></kwd>
<kwd lng="en"><![CDATA[Edge Computing.]]></kwd>
<kwd lng="es"><![CDATA[Detección de fallos industriales]]></kwd>
<kwd lng="es"><![CDATA[Arquitectura distribuida]]></kwd>
<kwd lng="es"><![CDATA[Machine learning]]></kwd>
<kwd lng="es"><![CDATA[Procesamiento de datos industriales]]></kwd>
<kwd lng="es"><![CDATA[Computación en la Nube]]></kwd>
</kwd-group>
</article-meta>
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