| Autor: |
Iantovics, Laszlo Barna, Gligor, Adrian, Montequín, Vicente Rodríguez, Balogh, Zoltán, Budinská, Ivana, Gatial, Emil, Carrino, Stefano, Ghorbel, Hatem, Dreyer, Jonathan |
| Předmět: |
|
| Zdroj: |
Acta Marisiensis. Seria Technologica.; Dec2022, Vol. 19 Issue 2, p12-19, 8p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
