IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p11953-d921800.html
   My bibliography  Save this article

Selection of Maintenance Strategies for Machines in a Series-Parallel System

Author

Listed:
  • Bożena Zwolińska

    (Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland)

  • Jakub Wiercioch

    (Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland)

Abstract

In this paper, an assessment of the failure frequency of machines in a series-parallel structure was conducted. The analyses contain the decomposition of the system according to the general theory of complex systems. Based on the results obtained, a model for an optimal determination of the mean time to failure (MTTF) according to the expected value of the gamma distribution was proposed. For this purpose, the method of moments was used to determine the optimal values of the parameters of the estimated gamma distribution. The article is designed to be analytical. The object of consideration in this analysis is the real production system working in accordance with make-to-order, with a high degree of product customisation. Moreover, in the considered system occurs a dichotomy of mutually exclusive flows: push and pull. In the article, the main emphasis was placed on the applicability of the proposed MTTF value-shaping algorithm. Then, the maintenance strategy for each machine (reactive, preventive or predictive) was proposed. Maintenance strategy selection considered sustainable development principles in the criterion of minimizing maintenance actions, fulfilling the assumption of not interrupting the flow of the processed material. Based on inductive analyses, the concepts of improvement actions individually for each machine in the analysed subsystem were deductively defined. As a result, it was proved that a reactive maintenance strategy is appropriate for machines that have manufacturing reserves and are low priority. The equipment possessing manufacturing reserves but also having an impact on the risk of interrupting the flow of the processed material should be operated in accordance with a preventive maintenance strategy. A predictive maintenance strategy was proposed for the machines with the highest priority, which simultaneously do not have manufacturing reserves and the risk of manufacturing line operation interruption is high. The considerations were conducted with a holistic approach, taking into account the main functional areas of the enterprise.

Suggested Citation

  • Bożena Zwolińska & Jakub Wiercioch, 2022. "Selection of Maintenance Strategies for Machines in a Series-Parallel System," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11953-:d:921800
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/11953/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/11953/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hu, Jiawen & Jiang, Zuhua & Liao, Haitao, 2017. "Preventive maintenance of a single machine system working under piecewise constant operating condition," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 105-115.
    2. Zhang, Xiaohong & Zeng, Jianchao, 2015. "A general modeling method for opportunistic maintenance modeling of multi-unit systems," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 176-190.
    3. Xia, Tangbin & Si, Guojin & Shi, Guo & Zhang, Kaigan & Xi, Lifeng, 2022. "Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization," Applied Energy, Elsevier, vol. 314(C).
    4. Nguyen, Kim Hanh & Kakinaka, Makoto, 2019. "Renewable energy consumption, carbon emissions, and development stages: Some evidence from panel cointegration analysis," Renewable Energy, Elsevier, vol. 132(C), pages 1049-1057.
    5. Maciej SZKODA & Grzegorz KACZOR & Robert PILCH & Maksymilian SMOLNIK & Zbigniew KONIECZEK, 2021. "Assessment Of The Influence Of Preventive Maintenance On The Reliability And Availability Indexes Of Diesel Locomotives," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 16(1), pages 5-18, March.
    6. Niklas Johansson & Eva Roth & Wiebke Reim, 2019. "Smart and Sustainable eMaintenance: Capabilities for Digitalization of Maintenance," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    7. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
    8. Pandey, Mayank & Zuo, Ming J. & Moghaddass, Ramin & Tiwari, M.K., 2013. "Selective maintenance for binary systems under imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 42-51.
    9. Percy, David F. & Kobbacy, Khairy A. H., 2000. "Determining economical maintenance intervals," International Journal of Production Economics, Elsevier, vol. 67(1), pages 87-94, August.
    10. Anatoly Lisnianski & Ilia Frenkel & Lev Khvatskin, 2021. "Modern Dynamic Reliability Analysis for Multi-state Systems," Springer Series in Reliability Engineering, Springer, number 978-3-030-52488-3, December.
    11. 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.
    12. Shouyao Xiong & Yuanyuan Feng & Kai Huang, 2020. "Optimal MTS and MTO Hybrid Production System for a Single Product Under the Cap-And-Trade Environment," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    13. Zareena Kausar & Muhammad Faizan Shah & Zeeshan Masood & Hafiz Zia Ur Rehman & Sardor Khaydarov & Muhammad Tallal Saeed & Omid Razmkhah & Haseeb Yaqoob, 2021. "Energy Efficient Parallel Configuration Based Six Degree of Freedom Machining Bed," Energies, MDPI, vol. 14(9), pages 1-23, May.
    14. Sola, Antonio Vanderley Herrero & Mota, Caroline Maria de Miranda & Kovaleski, João Luiz, 2011. "A model for improving energy efficiency in industrial motor system using multicriteria analysis," Energy Policy, Elsevier, vol. 39(6), pages 3645-3654, June.
    15. Wang, Lin & Lu, Zhiqiang & Ren, Yifei, 2020. "Joint production control and maintenance policy for a serial system with quality deterioration and stochastic demand," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Olcay Özge Ersöz & Ali Fırat İnal & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2022. "A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect," Sustainability, MDPI, vol. 14(21), pages 1-18, November.

    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. Xia, Tangbin & Si, Guojin & Shi, Guo & Zhang, Kaigan & Xi, Lifeng, 2022. "Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization," Applied Energy, Elsevier, vol. 314(C).
    2. Jiang, Junwei & An, Youjun & Dong, Yuanfa & Hu, Jiawen & Li, Yinghe & Zhao, Ziye, 2023. "Integrated optimization of non-permutation flow shop scheduling and maintenance planning with variable processing speed," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. 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).
    4. Wang, Jinhe & Zhang, Xiaohong & Zeng, Jianchao & Zhang, Yunzheng, 2020. "Joint external and internal opportunistic optimisation for wind turbine considering wind velocity," Renewable Energy, Elsevier, vol. 159(C), pages 380-398.
    5. Feng, Hanxin & Xi, Lifeng & Xiao, Lei & Xia, Tangbin & Pan, Ershun, 2018. "Imperfect preventive maintenance optimization for flexible flowshop manufacturing cells considering sequence-dependent group scheduling," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 218-229.
    6. Zhou, Xiaojun & Shi, Kailong, 2019. "Capacity failure rate based opportunistic maintenance modeling for series-parallel multi-station manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 46-53.
    7. uit het Broek, Michiel A.J. & Teunter, Ruud H. & de Jonge, Bram & Veldman, Jasper, 2021. "Joint condition-based maintenance and condition-based production optimization," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Shen, Yilan & Zhang, Xi & Shi, Leyuan, 2022. "Joint optimization of production and maintenance for a serial–parallel hybrid two-stage production system," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    9. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    10. Huang, Haiping & Huang, Baolian & Sun, Aijun, 2023. "How do mineral resources influence eco-sustainability in China? Dynamic role of renewable energy and green finance," Resources Policy, Elsevier, vol. 85(PA).
    11. Martín Olivera & Verónica Segarra, 2021. "Calidad ambiental y crecimiento económico: análisis dinámico para América Latina y el Caribe," Revista de Economía del Rosario, Universidad del Rosario, vol. 24(2), December.
    12. Adekoya, Oluwasegun B. & Olabode, Joshua K. & Rafi, Syed K., 2021. "Renewable energy consumption, carbon emissions and human development: Empirical comparison of the trajectories of world regions," Renewable Energy, Elsevier, vol. 179(C), pages 1836-1848.
    13. Liton Chandra Voumik & Md. Azharul Islam & Abidur Rahaman & Md. Maznur Rahman, 2022. "Emissions of carbon dioxide from electricity production in ASEAN countries: GMM and quantile regression analysis," SN Business & Economics, Springer, vol. 2(9), pages 1-20, September.
    14. Azizi, Fariba & Salari, Nooshin, 2023. "A novel condition-based maintenance framework for parallel manufacturing systems based on bivariate birth/birth–death processes," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    15. Maria Polorecka & Jozef Kubas & Pavel Danihelka & Katarina Petrlova & Katarina Repkova Stofkova & Katarina Buganova, 2021. "Use of Software on Modeling Hazardous Substance Release as a Support Tool for Crisis Management," Sustainability, MDPI, vol. 13(1), pages 1-15, January.
    16. Zhao, Xian & He, Zongda & Wu, Yaguang & Qiu, Qingan, 2022. "Joint optimization of condition-based performance control and maintenance policies for mission-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    17. Rusu, Eugen, 2020. "An evaluation of the wind energy dynamics in the Baltic Sea, past and future projections," Renewable Energy, Elsevier, vol. 160(C), pages 350-362.
    18. Usman, Muhammad & Khalid, Khaizran & Mehdi, Muhammad Abuzar, 2021. "What determines environmental deficit in Asia? Embossing the role of renewable and non-renewable energy utilization," Renewable Energy, Elsevier, vol. 168(C), pages 1165-1176.
    19. Shahnazi, Rouhollah & Dehghan Shabani, Zahra, 2020. "Do renewable energy production spillovers matter in the EU?," Renewable Energy, Elsevier, vol. 150(C), pages 786-796.
    20. Olcay Özge Ersöz & Ali Fırat İnal & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2022. "A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect," Sustainability, MDPI, vol. 14(21), pages 1-18, November.

    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:jsusta:v:14:y:2022:i:19:p:11953-:d:921800. 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.