<?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>1390-0129</journal-id>
<journal-title><![CDATA[Revista Politécnica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev Politéc. (Quito)]]></abbrev-journal-title>
<issn>1390-0129</issn>
<publisher>
<publisher-name><![CDATA[Escuela Politécnica Nacional]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1390-01292021000300007</article-id>
<article-id pub-id-type="doi">10.33333/rp.vol48n1.01</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Una Propuesta de Sistema de Diagnóstico de Fallos Robusto Ante la Presencia de Pérdida de Información y Ruido en Sistemas Mecánicos]]></article-title>
<article-title xml:lang="en"><![CDATA[A Proposal of Robust Fault Diagnosis System in Presence of Missing Data and Noise in Mechanical Systems]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ortiz Ortiz]]></surname>
<given-names><![CDATA[Francisco Javier,]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Llanes-Santiago]]></surname>
<given-names><![CDATA[Orestes,]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Politécnica Salesiana Programa de Maestría en Electrónica y Automatización ]]></institution>
<addr-line><![CDATA[Cuenca ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Tecnológica de La Habana José Antonio Echeverría,CUJAE Dpto. de Automática y Computación ]]></institution>
<addr-line><![CDATA[La Habana ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>10</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>10</month>
<year>2021</year>
</pub-date>
<volume>48</volume>
<numero>1</numero>
<fpage>7</fpage>
<lpage>18</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S1390-01292021000300007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S1390-01292021000300007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S1390-01292021000300007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Hoy en día, es un requisito de la industria moderna lograr elevados rendimientos económicos con un aumento continuo de la calidad de los productos finales, tener elevados niveles de seguridad industrial y reducir al mínimo las posibles afectaciones al medio ambiente; todo lo cual hace necesario la rápida detección e identificación de los fallos que se presenten en los sistemas industriales. La evolución de la Internet de las cosas y los avances tecnológicos en los medios técnicos de automatización, las redes industriales y las comunicaciones inalámbricas entre otros elementos, han permitido un crecimiento significativo del número de herramientas a usar para el tratamiento y gestión de la información obtenida por los sistemas de supervisión, control y adquisición de datos (SCADA) de los procesos industriales. Sin embargo, el desempeño de estas herramientas y en especial de los sistemas de diagnóstico de fallos se ve afectado por dos problemas concretos: la presencia de ruido en las mediciones y la pérdida de información de variables medidas. En el presente trabajo se propone una metodología para el diagnóstico de fallos en sistemas industriales mecánicos utilizando herramientas de inteligencia computacional que logra un comportamiento robusto ante la presencia de pérdida de información y el ruido logrando altos niveles de desempeño. La metodología propuesta se aplica al problema de prueba DAMADICS que representa a una válvula electro-neumática que es un tipo de actuador muy utilizado en los sistemas industriales modernos. Los resultados satisfactorios que se obtienen demuestran la efectividad y validez de la propuesta.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Currently, the modern industry requires to achieve high economic returns with a continuous increase in the quality of the final products, to have high levels of industrial safety and to minimize possible effects on the environment. For accomplishing these requirements, it is necessary a fast detection and identification of faults that occur in the industrial systems. The evolution of the Internet of things and technological advances in automation devices, industrial networks and wireless communications, among other elements, have allowed a significant growth in the number of tools to be used for the treatment and management of the information obtained from the industrial processes by the supervision, control and data acquisition systems (SCADA). However, the performance of these tools, and especially of the fault diagnosis systems, are affected by two specific problems: the presence of noise and missing information on the measured variables. In this paper, a novel methodology for fault diagnosis in mechanical industrial systems is proposed by using computational intelligence tools. The proposal presents a robust behavior in presence of missing data and noise in the measurements by achieving high levels of performance. The proposed methodology is applied to the DAMADICS actuator FDI benchmark which is an electro-pneumatic valve widely used in modern industrial systems. The satisfactory results obtained demonstrate the effectiveness and validity of the proposal.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Diagnóstico de fallos]]></kwd>
<kwd lng="es"><![CDATA[pérdida de datos]]></kwd>
<kwd lng="es"><![CDATA[ruido]]></kwd>
<kwd lng="es"><![CDATA[imputación de datos]]></kwd>
<kwd lng="es"><![CDATA[procesos industriales]]></kwd>
<kwd lng="es"><![CDATA[inteligencia computacional]]></kwd>
<kwd lng="en"><![CDATA[Fault diagnosis]]></kwd>
<kwd lng="en"><![CDATA[missing data]]></kwd>
<kwd lng="en"><![CDATA[noise]]></kwd>
<kwd lng="en"><![CDATA[data imputation]]></kwd>
<kwd lng="en"><![CDATA[industrial processes]]></kwd>
<kwd lng="en"><![CDATA[computational intelligence]]></kwd>
</kwd-group>
</article-meta>
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