IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p6170-d1538715.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/6170/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/23/6170/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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).
    6. 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).
    7. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Edward & Bao, Han & Dinh, Nam, 2024. "Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    2. Dui, Hongyan & Zhang, Chi & Tian, Tianzi & Wu, Shaomin, 2022. "Different costs-informed component preventive maintenance with system lifetime changes," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. Kammouh, Omar & Fecarotti, Claudia & Marandi, Ahmadreza, 2024. "A scalable optimization approach to the intervention planning of complex interconnected infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    4. Li, Yaping & Xia, Tangbin & Chen, Zhen & Pan, Ershun, 2023. "Multiple degradation-driven preventive maintenance policy for serial-parallel multi-station manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Zhao, Xian & Han, He & Jiao, Chunhui & Qiu, Qingan, 2024. "Reliability modeling of k-out-of-n: F balanced systems with common bus performance sharing," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    6. Gu, Liudong & Wang, Guanjun & Zhou, Yifan, 2024. "Optimal allocation of multi-state performance sharing systems with multiple common buses," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    7. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2024. "Optimizing corrective maintenance for multistate systems with storage," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    9. Wu, Jing & Qian, Cunhua & Dohi, Tadashi, 2024. "Optimal opportunity-based age replacement policies in discrete time," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Moghadam, Mehdi Akbari & Bagheri, Sajad & Salemi, Amir Hosein & Tavakoli, Mohammad Bagher, 2023. "Long-term maintenance planning of medium voltage overhead lines considering the uncertainties and reasons for interruption in a real distribution network," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    11. Lu, Yaohui & Wang, Shaoping & Zhang, Chao & Chen, Rentong & Dui, Hongyan & Mu, Rui, 2024. "Adaptive maintenance window-based opportunistic maintenance optimization considering operational reliability and cost," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    12. Alotaibi, Naif M. & Scarf, Philip & Cavalcante, Cristiano A.V. & Lopes, Rodrigo S. & de Oliveira e Silva, André Luiz & Rodrigues, Augusto J.S. & Alyami, Salem A., 2023. "Modified-opportunistic inspection and the case of remote, groundwater well-heads," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    13. Zheng, Jianfei & Ren, Jincheng & Pei, Hong & Zhang, Jianxun & Zhang, Zhengxin, 2024. "Lifetime prediction and replacement optimization for a standby system considering storage failures of spare parts," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    14. Karar, Ahmed Noaman & Labib, Ashraf & Jones, Dylan, 2024. "A resilience-based maintenance optimisation framework using multiple criteria and Knapsack methods," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Liao, Ruoyu & He, Yihai & Feng, Tianyu & Yang, Xiuzhen & Dai, Wei & Zhang, Weifang, 2023. "Mission reliability-driven risk-based predictive maintenance approach of multistate manufacturing system," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    16. Qi, Faqun & Yang, Huaqing & Wei, Lai & Shu, Xinting, 2024. "Preventive maintenance policy optimization for a weighted k-out-of-n: G system using the survival signature," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    17. McMorland, J. & Collu, M. & McMillan, D. & Carroll, J. & Coraddu, A., 2023. "Opportunistic maintenance for offshore wind: A review and proposal of future framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    18. Alan Ortiz Contreras & Mohamed Badaoui & David Sebastián Baltazar, 2024. "The Optimal Selection of Renewable Energy Systems Based on MILP for Two Zones in Mexico," Sustainability, MDPI, vol. 16(14), pages 1-26, July.
    19. Manna, Carlo & Lahariya, Manu & Karami, Farzaneh & Develder, Chris, 2023. "A data-driven optimization framework for industrial demand-side flexibility," Energy, Elsevier, vol. 278(C).
    20. Leoni, Leonardo & De Carlo, Filippo & Tucci, Mario, 2023. "Developing a framework for generating production-dependent failure rate through discrete-event simulation," International Journal of Production Economics, Elsevier, vol. 266(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6170-:d:1538715. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.