Development and application of soft computing and data mining techniques in hot dip galvanising

Development and application of soft computing and data mining techniques in hot dip galvanising

In a world in which markets are more globalised and continuously evolving, industry need new tools to enhance their flexibility and maintain competitiveness. A key strategy is discovering useful knowledge through the information gathered from production processes. In recent decades, companies have i...

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Autor principal: Sanz García, Andrés
Otros Autores: Martínez de Pisón Ascacíbar, Francisco Javier (Universidad de La Rioja)
Formato: text (thesis)
Lenguaje:eng
Publicado: Universidad de La Rioja (España) 2013
Acceso en línea:https://dialnet.unirioja.es/servlet/oaites?codigo=26354
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description In a world in which markets are more globalised and continuously evolving, industry need new tools to enhance their flexibility and maintain competitiveness. A key strategy is discovering useful knowledge through the information gathered from production processes. In recent decades, companies have increased investments for improving data storage capacity. The huge volume of information stored by companies and its high complexity render traditional methods of data processing useless. However, the use of tools to extract the information hidden inside databases is still under development. The creation of such methodologies can make the key points of industrial processes more flexible. With the aim of solving this problem, new computer-based methodologies derived from data mining are being developed. By using these methods, researchers are seeking to obtain non-trivial hidden knowledge from historical records of industrial processes. For this reason, data mining has now become a crucial discipline for performing automatic searches inside historical industrial databases, contributing to industrial development and advancement. This thesis focuses on the use of data mining techniques to develop helpful methodologies for tuning industrial production lines. The goal is to increase flexibility in response to the need to meet new consumer expectations. The methodologies developed have been used to improve a continuous galvanising line. Bearing in mind its complexity, our aim is to explore the opportunities that data mining techniques can offer for improving this industrial process. The goal of the first part is to seek novel non-trivial knowledge in the form of patterns to explain failures in production. An overall methodology that integrates both data management and association rule mining is proposed to capture frequent events that coincide when there is a failure in the process. The second part focuses on improving the modelling of non-linear systems using historical information, combining different soft computing techniques. These improved the estimation of temperature set points for the annealing furnace on a galvanising line. The contributions presented in this doctoral thesis provide evidence of the huge potential that data mining has for obtaining useful comprehensible knowledge from industrial processes.
author2 Martínez de Pisón Ascacíbar, Francisco Javier (Universidad de La Rioja)
author_facet Martínez de Pisón Ascacíbar, Francisco Javier (Universidad de La Rioja)
Sanz García, Andrés
format text (thesis)
author Sanz García, Andrés
spellingShingle Sanz García, Andrés
Development and application of soft computing and data mining techniques in hot dip galvanising
author_sort Sanz García, Andrés
title Development and application of soft computing and data mining techniques in hot dip galvanising
title_short Development and application of soft computing and data mining techniques in hot dip galvanising
title_full Development and application of soft computing and data mining techniques in hot dip galvanising
title_fullStr Development and application of soft computing and data mining techniques in hot dip galvanising
title_full_unstemmed Development and application of soft computing and data mining techniques in hot dip galvanising
title_sort development and application of soft computing and data mining techniques in hot dip galvanising
publisher Universidad de La Rioja (España)
publishDate 2013
url https://dialnet.unirioja.es/servlet/oaites?codigo=26354
work_keys_str_mv AT sanzgarciaandres developmentandapplicationofsoftcomputinganddataminingtechniquesinhotdipgalvanising
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spelling oai-TES00000037002019-06-04Development and application of soft computing and data mining techniques in hot dip galvanisingSanz García, AndrésIn a world in which markets are more globalised and continuously evolving, industry need new tools to enhance their flexibility and maintain competitiveness. A key strategy is discovering useful knowledge through the information gathered from production processes. In recent decades, companies have increased investments for improving data storage capacity. The huge volume of information stored by companies and its high complexity render traditional methods of data processing useless. However, the use of tools to extract the information hidden inside databases is still under development. The creation of such methodologies can make the key points of industrial processes more flexible. With the aim of solving this problem, new computer-based methodologies derived from data mining are being developed. By using these methods, researchers are seeking to obtain non-trivial hidden knowledge from historical records of industrial processes. For this reason, data mining has now become a crucial discipline for performing automatic searches inside historical industrial databases, contributing to industrial development and advancement. This thesis focuses on the use of data mining techniques to develop helpful methodologies for tuning industrial production lines. The goal is to increase flexibility in response to the need to meet new consumer expectations. The methodologies developed have been used to improve a continuous galvanising line. Bearing in mind its complexity, our aim is to explore the opportunities that data mining techniques can offer for improving this industrial process. The goal of the first part is to seek novel non-trivial knowledge in the form of patterns to explain failures in production. An overall methodology that integrates both data management and association rule mining is proposed to capture frequent events that coincide when there is a failure in the process. The second part focuses on improving the modelling of non-linear systems using historical information, combining different soft computing techniques. These improved the estimation of temperature set points for the annealing furnace on a galvanising line. The contributions presented in this doctoral thesis provide evidence of the huge potential that data mining has for obtaining useful comprehensible knowledge from industrial processes.En un mundo donde los mercados son cada día más globales y cambiantes, la industria necesita nuevas herramientas para mejorar su flexibilidad y mantener la competitividad. Una estrategia para ello es la búsqueda de conocimiento útil a partir de la información procedente de sus procesos productivos. En las últimas décadas, las empresas han realizado importantes inversiones para mejorar el almacenamiento de dicha información. Sin embargo, aún es muy incipiente la implementación de herramientas que extraigan conocimiento implícito subyacente en dicha información almacenada. Debido al volumen y complejidad de esta información, los métodos tradicionales no pueden ser empleados hoy en día. Por ello, se desarrollan herramientas basadas en el uso de computadoras para obtener conocimiento útil a partir de datos históricos de procesos industriales. La minería de datos se ha convertido en una disciplina crucial para realizar esta búsqueda automática. La tesis doctoral pretende desarrollar metodologías basadas en minería de datos que ayuden al ajuste de las líneas industriales. El objetivo es mayor flexibilidad y eficiencia con nuevos productos. Para demostrar su aplicación, las propuestas son aplicadas a una línea de galvanizado continuo por inmersión en caliente de bobinas de acero, cuya dimensión y complejidad ponen de manifiesto las oportunidades que ofrece la minería de datos. en la mejora de estos procesos. El objetivo de la primera parte de la tesis es la extracción de conocimiento útil y no trivial en forma de patrones que expliquen fallos frecuentes en producción. Se propone una metodología global integrando tratamiento de datos y minería de reglas de asociación para mostrar eventos con alto grado de coocurrencia durante fallos en el proceso. La segunda parte se centra en mejorar el modelado de sistemas no lineales a partir de datos históricos, desarrollando dos metodologías combinando técnicas de Soft Computing. Estas mejoraron la estimación de temperaturas de consigna del horno de recocido una línea de galvanizado. Las contribuciones presentadas en esta tesis doctoral demuestran el enorme potencial de la minería de datos a la hora de proporcionar conocimiento útil y comprensible a partir de datos históricos de procesos industriales.Universidad de La Rioja (España)Martínez de Pisón Ascacíbar, Francisco Javier (Universidad de La Rioja)2013text (thesis)application/pdfhttps://dialnet.unirioja.es/servlet/oaites?codigo=26354(Tesis) ISBN 978-84-695-7622-9 engLICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. 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