Acta Marisiensis.
Seria Technologica

Author(s):
Laszlo Barna IANTOVICS, Adrian GLIGOR, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures
Vicente Rodríguez MONTEQUÍN, University of Oviedo, Oviedo, Spain
Zoltán BALOGH, Ivana BUDINSKÁ, Emil GATIAL, Institute of Informatics, Slovak Academy of Sciences, Bratislava, Slovakia
Stefano CARRINO, Hatem GHORBEL, Jonathan DREYER, Haute École Arc Ingénierie HES-SO, St-Imier, Switzerland

Abstract:
Predictive methods represent techniques commonly met in Industry 4.0 that offer a way to early predict or detect faults of machines, devices or tools. This is useful to anticipate failures with the main goal of improving maintenance planning. Making such predictions could decrease the unexpected malfunction operation or manufacturing downtime and consequently the overall maintenance costs. In this paper we present the basis of the architecture designed for predictive maintenance in the project Social Network of Machines (SOON) under the paradigm of Industry 4.0, as well as a brief literature stateof-the-art survey of the topic. A particular implementation of this architecture, a testbed for electrical motors failure detection, is shown and evaluated.

DOI: https://doi.org/10.2478/amset-2022-0012

Pages: 12-19

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