Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.

There is a growing demand in various industries for the collection of variables related to the conditions of production line equipment, such as electric motors. This demand has increased due to the rise of Industry 4.0 and the digital transformation that companies are deploying. Understanding that a...

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Authors: Gutiérrez-Trejo, Sergio Simanek, Romero-Guerrero, Jorge Adan, Villa-Villaseñor, Noé
Format: article
Status:Published version
Publication Date:2024
Country:México
Institution:UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO
Repository:PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI
Language:Spanish
OAI Identifier:oai:repository.uaeh.edu.mx:article/11092
Online Access:https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/11092
Access Level:Open access
Keyword:Industry 4.0
Digital Transformation
IIoT (Industrial Internet of Things)
Machine Learning
Data Analytics
Big Data
Industria 4.0
Transformación Digital
IIoT(Industrial Internet of Things)
Analítica de datos
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spelling Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.Arquitectura inteligente para motores eléctricos: IIoT y machine learning para la adquisición y análisis avanzado de datosGutiérrez-Trejo, Sergio SimanekRomero-Guerrero, Jorge Adan Villa-Villaseñor, NoéIndustry 4.0Digital TransformationIIoT (Industrial Internet of Things)Machine LearningData AnalyticsBig DataIndustria 4.0Transformación DigitalIIoT(Industrial Internet of Things)Machine LearningAnalítica de datosBig DataThere is a growing demand in various industries for the collection of variables related to the conditions of production line equipment, such as electric motors. This demand has increased due to the rise of Industry 4.0 and the digital transformation that companies are deploying. Understanding that a typical plant has between 6,000 to 12,000 pieces of equipment, selecting critical equipment to assign an investment in the installation and start-up of sensors that measure operating conditions is both an operational and investment challenge. This is where IIoT (Industrial Internet of Things) technologies become relevant, as they allow for cost mitigation by not using wiring for data collection, as well as for a faster and more flexible deployment. The next challenge is how to monitor, process, visualize, and analyze the large volume of data (Big Data) that is generated. Therefore, this work proposes an architecture that addresses these challenges, as well as a methodology that can be used for the integration of these projects, and how every day the industry demands more application of Machine Learning techniques.Existe una demanda creciente en la industria en distintas áreas para la recolección de variables relacionadas con las condiciones de los equipos de líneas de producción, como los motores eléctricos. Esta demanda ha aumentado debido al auge de la industria 4.0 y la transformación digital que las empresas están desplegando. Entendiendo que una plata típica tiene entre 6,000 a 12,000 equipos, seleccionar los equipos críticos para asignar una inversión en la instalación y puesta en marcha de sensores que midan las condiciones de operación es un desafío tanto operativo como de inversión. Es aquí es donde las tecnologías de IIoT (Industrial Internet of Things), cobran relevancia, ya que permiten mitigar costos tanto en no utilizar cableado para la recolección de datos, como en un despliegue mar rápido y flexible. El siguiente reto, es cómo monitorear, procesar, visualizar y analizar el gran volumen de datos (Big Data) que se generan. Por lo que en este trabajo se propone una arquitectura que aborde estos retos, como también que metodología puede ser usada para la integración de estos proyectos, y como cada día la industria demanda más aplicación de técnicas de Machine Learning.Universidad Autónoma del Estado de Hidalgo2024-01-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/1109210.29057/icbi.v11i22.11092Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; Vol 11 No 22 (2024): Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; 118-123Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; Vol. 11 Núm. 22 (2024): Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; 118-1232007-636310.29057/icbi.v11i22reponame:PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBIinstname:UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGOinstacron:UAEHspahttps://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/11092/10512Derechos de autor 2024 Sergio Simanek Gutiérrez-Trejo, Jorge Adan Romero-Guerrero, Noé Villa-Villaseñorhttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessoai:repository.uaeh.edu.mx:article/110922024-08-19T22:36:47Z
dc.title.none.fl_str_mv Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.
Arquitectura inteligente para motores eléctricos: IIoT y machine learning para la adquisición y análisis avanzado de datos
title Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.
spellingShingle Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.
Gutiérrez-Trejo, Sergio Simanek
Industry 4.0
Digital Transformation
IIoT (Industrial Internet of Things)
Machine Learning
Data Analytics
Big Data
Industria 4.0
Transformación Digital
IIoT(Industrial Internet of Things)
Machine Learning
Analítica de datos
Big Data
title_short Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.
title_full Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.
title_fullStr Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.
title_full_unstemmed Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.
title_sort Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.
dc.creator.none.fl_str_mv Gutiérrez-Trejo, Sergio Simanek
Romero-Guerrero, Jorge Adan
Villa-Villaseñor, Noé
author Gutiérrez-Trejo, Sergio Simanek
author_facet Gutiérrez-Trejo, Sergio Simanek
Romero-Guerrero, Jorge Adan
Villa-Villaseñor, Noé
author_role author
author2 Romero-Guerrero, Jorge Adan
Villa-Villaseñor, Noé
author2_role author
author
dc.subject.none.fl_str_mv Industry 4.0
Digital Transformation
IIoT (Industrial Internet of Things)
Machine Learning
Data Analytics
Big Data
Industria 4.0
Transformación Digital
IIoT(Industrial Internet of Things)
Machine Learning
Analítica de datos
Big Data
topic Industry 4.0
Digital Transformation
IIoT (Industrial Internet of Things)
Machine Learning
Data Analytics
Big Data
Industria 4.0
Transformación Digital
IIoT(Industrial Internet of Things)
Machine Learning
Analítica de datos
Big Data
description There is a growing demand in various industries for the collection of variables related to the conditions of production line equipment, such as electric motors. This demand has increased due to the rise of Industry 4.0 and the digital transformation that companies are deploying. Understanding that a typical plant has between 6,000 to 12,000 pieces of equipment, selecting critical equipment to assign an investment in the installation and start-up of sensors that measure operating conditions is both an operational and investment challenge. This is where IIoT (Industrial Internet of Things) technologies become relevant, as they allow for cost mitigation by not using wiring for data collection, as well as for a faster and more flexible deployment. The next challenge is how to monitor, process, visualize, and analyze the large volume of data (Big Data) that is generated. Therefore, this work proposes an architecture that addresses these challenges, as well as a methodology that can be used for the integration of these projects, and how every day the industry demands more application of Machine Learning techniques.
publishDate 2024
dc.date.none.fl_str_mv 2024-01-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/11092
10.29057/icbi.v11i22.11092
url https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/11092
identifier_str_mv 10.29057/icbi.v11i22.11092
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/11092/10512
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Autónoma del Estado de Hidalgo
publisher.none.fl_str_mv Universidad Autónoma del Estado de Hidalgo
dc.source.none.fl_str_mv Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; Vol 11 No 22 (2024): Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; 118-123
Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; Vol. 11 Núm. 22 (2024): Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; 118-123
2007-6363
10.29057/icbi.v11i22
reponame:PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI
instname:UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO
instacron:UAEH
instname_str UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO
instacron_str UAEH
institution UAEH
reponame_str PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI
collection PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI
repository.name.fl_str_mv
repository.mail.fl_str_mv
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