SOON: Social Network of Machines Solution for Predictive Maintenance of Electrical Drive in Industry 4.0

Laszlo Barna Iantovics; Adrian Gligor; Vicente Rodríguez Montequín; Zoltán Balogh; Ivana Budinská; Emil Gatial; Stefano Carrino; Hatem Ghorbel; Jonathan Dreyer

doi:10.2478/amset-2022-0012

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 state-of-the-art survey of the topic. A particular implementation of this architecture, a testbed for electrical motors failure detection, is shown and evaluated.

References

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SOON: Social Network of Machines Solution for Predictive Maintenance of Electrical Drive in Industry 4.0

Abstract

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