Transformando sistemas automáticos de almacenamiento :Un primer enfoque de mantenimiento predictivo basado en algoritmos de clasificación

Acaro Imaicela, Jonathan Andrés

dc.contributor.advisor	Paipa Galeano, Luis Alfredo
dc.contributor.author	Acaro Imaicela, Jonathan Andrés
dc.date.accessioned	2023-05-09T14:42:13Z
dc.date.available	2023-05-09T14:42:13Z
dc.date.issued	2023-02-14
dc.identifier.uri	http://hdl.handle.net/10818/55229
dc.description	61 páginas	es_CO
dc.description.abstract	La cuarta revolución industrial ya es un hecho y ahora las organizaciones buscan incorporar aspectos como el manejo de Big Data, computación en nube e internet de las cosas con el objetivo de fortalecer su oferta de valor. El presente proyecto se realizó para una empresa global que elabora y comercializa maquinaria de almacenamiento automático. Busca identificar cuál es el mejor modelo de mantenimiento predictivo a los datos recolectados por sensores en las máquinas. El mantenimiento predictivo consiste en predecir cuándo es el mejor momento para realizar un mantenimiento, de tal manera que se minimice los eventos de falla y el tiempo de reparación asociado. En los últimos años, esta organización ha transformado su oferta en un producto alineado al concepto de industria 4.0 y a través del presente proyecto se pretende seguir haciéndolo. A partir de los datos capturados por los sensores de máquinas en el campo y la metodología de aprendizaje de máquinas supervisado automatizado, se obtuvieron distintos modelos de clasificación. El preprocesamiento de datos requirió agrupación de los datos en ventanas de tiempo y técnicas de balanceo de clases. Los modelos obtenidos fueron evaluados con un conjunto de datos nuevo, el cual se obtuvo en un periodo de tiempo posterior a la extracción inicial de datos para entrenamiento. Entre los modelos recomendados se encontraron bosques aleatorios, árboles extremadamente aleatorios y boosting. El puntaje f1 fue utilizado para medir el desempeño de los modelos y se alcanzó un valor máximo de 0.4 en la validación final.	es_CO
dc.format	application/pdf	es_CO
dc.language.iso	spa	es_CO
dc.publisher	Universidad de La Sabana	es_CO
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.title	Transformando sistemas automáticos de almacenamiento :Un primer enfoque de mantenimiento predictivo basado en algoritmos de clasificación	es_CO
dc.type	master thesis	es_CO
dc.identifier.local	291661
dc.identifier.local	TE12256
dc.type.hasVersion	publishedVersion	es_CO
dc.rights.accessRights	openAccess	es_CO
dc.subject.armarc	Sistemas de recolección automática de datos
dc.subject.armarc	Mantenibilidad (Ingeniería)
dc.subject.armarc	Big Data
dc.subject.armarc	Fábricas -- Mantenimiento y reparación
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thesis.degree.discipline	Facultad de Ingeniería	es_CO
thesis.degree.level	Maestría en Analítica Aplicada	es_CO
thesis.degree.name	Magíster en Analítica Aplicada	es_CO