Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure

Primera, Ernesto, Fernández, Daniel, Cacereño, Andrés y Rodríguez Prieto, Álvaro . (2024) Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure. Machines (2024), 12-1, 69

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Título Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure
Autor(es) Primera, Ernesto
Fernández, Daniel
Cacereño, Andrés
Rodríguez Prieto, Álvaro
Materia(s) Ingeniería Mecánica
Abstract Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit.
Palabras clave bearing failure
prognostics
data analytics
statistical modeling
predictive maintenance
Editor(es) MDPI
Fecha 2024-06-17
Formato application/pdf
Identificador bibliuned:DptoICyF-ETSI-Articulos-Arodriguez-0014
http://e-spacio.uned.es/fez/view/bibliuned:DptoICyF-ETSI-Articulos-Arodriguez-0014
DOI - identifier https://doi.org/10.3390/machines12010069
ISSN - identifier 2075-1702
Nombre de la revista Machines
Número de Volumen 12
Número de Issue 1
Publicado en la Revista Machines (2024), 12-1, 69
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales La versión registrada de este artículo, publicado por primera vez en Machines (2024), 12-1, 69, está disponible en línea en el sitio web de la editorial: MDPI https://doi.org/10.3390/machines12010069
Notas adicionales The registered version of this article, first published in Machines (2024), 12-1, 69, is available online at the publisher's website: MDPI https://doi.org/10.3390/machines12010069

 
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Creado: Mon, 08 Apr 2024, 18:33:48 CET