Modelling the Prioritisation of Technical Objects Using the EPN Indicator DOI Creative Commons
Oliwia Powichrowska, Jakub Wiercioch, Bożena Zwolińska

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(23), P. 6170 - 6170

Published: Dec. 7, 2024

The objective of this article is to analyse and evaluate the effectiveness predictive maintenance for machines performing key functions within a production structure. This presents methodology determining Equipment Priority Number (EPN), calculated based on parameters such as energy consumption, criticality in value stream, their impact continuity supply chain. experimental implementation system monitoring operational parameters—including current vibrations, torque moments—enabled prediction potential failures planning actions, which contributed improving stability reducing risk unplanned downtime. obtained results confirm proposed demonstrate that supported by EPN indicator enables accurate prioritisation activities an actual system. findings also show implementing algorithm allows more precise highly customised environments. Furthermore, analysis collected data suggests further optimisation through integration data-driven diagnostics artificial intelligence methods, could enhance efficiency competitiveness study’s conclusions provide foundation advancing methods industrial production.

Language: Английский

Modelling the Prioritisation of Technical Objects Using the EPN Indicator DOI Creative Commons
Oliwia Powichrowska, Jakub Wiercioch, Bożena Zwolińska

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(23), P. 6170 - 6170

Published: Dec. 7, 2024

The objective of this article is to analyse and evaluate the effectiveness predictive maintenance for machines performing key functions within a production structure. This presents methodology determining Equipment Priority Number (EPN), calculated based on parameters such as energy consumption, criticality in value stream, their impact continuity supply chain. experimental implementation system monitoring operational parameters—including current vibrations, torque moments—enabled prediction potential failures planning actions, which contributed improving stability reducing risk unplanned downtime. obtained results confirm proposed demonstrate that supported by EPN indicator enables accurate prioritisation activities an actual system. findings also show implementing algorithm allows more precise highly customised environments. Furthermore, analysis collected data suggests further optimisation through integration data-driven diagnostics artificial intelligence methods, could enhance efficiency competitiveness study’s conclusions provide foundation advancing methods industrial production.

Language: Английский

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