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Modelling the Prioritisation of Technical Objects Using the EPN Indicator

Author

Listed:
  • Oliwia Powichrowska

    (Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland)

  • Jakub Wiercioch

    (Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland)

  • Bożena Zwolińska

    (Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland)

Abstract

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

Suggested Citation

  • Oliwia Powichrowska & Jakub Wiercioch & Bożena Zwolińska, 2024. "Modelling the Prioritisation of Technical Objects Using the EPN Indicator," Energies, MDPI, vol. 17(23), pages 1-28, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6170-:d:1538715
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    References listed on IDEAS

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    1. Xiao, Lei & Song, Sanling & Chen, Xiaohui & Coit, David W., 2016. "Joint optimization of production scheduling and machine group preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 68-78.
    2. Tareq Ali Al Ameeri & Mohd Nizam Ab Rahman & Norhamidi Muhamad, 2023. "Analysing Effective and Ineffective Impacts of Maintenance Strategies on Electric Power Plants: A Comprehensive Approach," Energies, MDPI, vol. 16(17), pages 1-11, August.
    3. Tangbin Xia & Xiangxin An & Huaqiang Yang & Yimin Jiang & Yuhui Xu & Meimei Zheng & Ershun Pan, 2023. "Efficient Energy Use in Manufacturing Systems—Modeling, Assessment, and Management Strategy," Energies, MDPI, vol. 16(3), pages 1-20, January.
    4. Mena, R. & Viveros, P. & Zio, E. & Campos, S., 2021. "An optimization framework for opportunistic planning of preventive maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Mahmood Shafiee & Ashraf Labib & Jhareswar Maiti & Andrew Starr, 2019. "Maintenance strategy selection for multi-component systems using a combined analytic network process and cost-risk criticality model," Journal of Risk and Reliability, , vol. 233(2), pages 89-104, April.
    6. Mikhail, Mina & Ouali, Mohamed-Salah & Yacout, Soumaya, 2024. "A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. An, Xiangxin & Si, Guojin & Xia, Tangbin & Wang, Dong & Pan, Ershun & Xi, Lifeng, 2023. "An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs," Applied Energy, Elsevier, vol. 336(C).
    8. Wu, Congshan & Pan, Rong & Zhao, Xian & Wang, Xiaoyue, 2024. "Designing preventive maintenance for multi-state systems with performance sharing," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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