Anomalies Detection in records of operational failures using IoT devices and data mining

• Izaque Esteves da Silva Universidade Federal de Juiz de Fora
• Regina Braga Universidade Federal de Juiz de Fora
• José Maria N. David Universidade Federal de Juiz de Fora https://orcid.org/0000-0002-3378-015X
• Victor Stroele Universidade Federal de Juiz de Fora https://orcid.org/0000-0001-6296-8605

DOI: https://doi.org/10.5753/sbsi.2021.15381

Resumo

The industry underwent several transformations initiated by the first Industrial Revolution at the end of the 18th century. Today we are experiencing the Fourth Industrial Revolution, where equipment is capable of processing data and connect to communication networks. Maintenance planning can use large volume of data generated by IoT devices to act preventively . This work aims to propose an architecture that uses an outlier detection algorithm, Local Outlier Factor, to detect anomalies in machine failure records, producing information to support equipment maintenance decisions.

Palavras-chave: data mining, outliers detection, industry 4.0, artificial intelligence

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