Diseño de un Modelo Predictivo en el Contexto Industria 4.0

Article English OPEN
Candanedo, Inés Sittón ; González, Sarah Rodríguez ; Muñoz, Lilia (2018)
  • Publisher: KnE Publishing
  • Journal: KnE Engineering (issn: 2518-6841)
  • Related identifiers: doi: 10.18502/keg.v3i1.1458
  • Subject: Industria 4.0 | Sensores | Modelo Omnibus | Extracción de Patrones | Internet de las Cosas

The Internet of Things (IoT), the development and installation of advanced sensors for data collection, computer solutions for remote connection and other disruptive technologies are marking a transformation process in the industry; giving rise to what various sectors have called the fourth industrial revolution or Industry 4.0. With this process of change, organizations face both new opportunities and challenges. This article focuses on the modeling and integration of industrial data, generated by sensors installed in machines. The extraction of patterns is proposed, using data fusion techniques that allow the design of a predictive maintenance model. Finally, a case study is presented with a database that is applied to the Naive Bayes Algorithm to obtain predictions.Keywords: Industry 4.0, Sensors, Internet of Things, Pattern Extraction, Omnibus Models. 
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