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


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


Arulanthu, P. and Perumal, E. (2020). An intelligent iot with cloud centric medical decision support system for chronic kidney disease prediction. International Journal of Imaging Systems and Technology, 30(3):815–827.

Barnett, V. and Lewis, T. (1984). Outliers in statistical data. Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics.

Cheng, C.-H., Guelfirat, T., Messinger, C., Schmitt, J. O., Schnelte, M., and Weber, P. (2015). Semantic degrees for industrie 4.0 engineering: deciding on the degree of semantic formalization to select appropriate technologies. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, pages 1010–1013.

Drath, R. and Horch, A. (2014). Industrie 4.0: Hit or hype?[industry forum]. IEEE industrial electronics magazine, 8(2):56–58.

Frank, A. G., Dalenogare, L. S., and Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210:15–26.

Gorunescu, F. (2011). Data Mining: Concepts, models and techniques, volume 12. Springer Science & Business Media.

Jaloudi, S. (2019). Mqtt for iot-based applications in smart cities. Palestinian Journal of Technology and Applied Sciences (PJTAS), (2).

Kagermann, H., Wahlster, W., Helbig, J., et al. (2013). Recommendations for implementing the strategic initiative industrie 4.0: Final report of the industrie 4.0 working group. Forschungsunion: Berlin, Germany.

Lee, J., Kang, B., and Kang, S.-H. (2011). Integrating independent component analysis and local outlier factor for plant-wide process monitoring. Journal of Process Control, 21(7):1011–1021.

Ma, Y., Shi, H., Ma, H., and Wang, M. (2013). Dynamic process monitoring using adaptive local outlier factor. Chemometrics and Intelligent Laboratory Systems, 127:89–101.

Norvig, P. R. and Intelligence, S. A. (2002). A modern approach. Prentice Hall Upper Saddle River, NJ, USA:.

Santos, B. P., Silva, L. A., Celes, C., Borges, J. B., Neto, B. S. P., Vieira, M. A. M., Vieira, L. F. M., Goussevskaia, O. N., and Loureiro, A. (2016). Internet das coisas: da teoria à pratica. Minicursos SBRC-Simposio Brasileiro de Redes de Computadores e Sistemas Distribuıdos, 31.

Trappenberg, T. P. (2019). Machine learning with sklearn. In Fundamentals of Machine Learning, pages 38–65. Oxford University Press.
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DA SILVA, Izaque Esteves; BRAGA, Regina; DAVID, José Maria N.; STROELE, Victor. Anomalies Detection in records of operational failures using IoT devices and data mining. In: TEMAS EMERGENTES: INTERAÇÃO HUMANO-IA NA ERA DA DIGITALIZAÇÃO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 17. , 2021, On-line. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 209-216. DOI: