IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v340y2024i1d10.1007_s10479-024-05930-9.html
   My bibliography  Save this article

A mathematical maintenance model for a production system subject to deterioration according to a stochastic geometric process

Author

Listed:
  • Hasan Rasay

    (Kermanshah University of Technology)

  • Fariba Azizi

    (Alzahra University)

  • Farnoosh Naderkhani

    (Concordia University)

Abstract

Given the deteriorative nature of industrial systems, implementation of advanced Preventive Maintenance (PM) strategies becomes of paramount importance to cope with the maintenance needs of ever-changing complex industrial and safety-critical systems. Conventionally, PM approaches are developed based on the perfect maintenance assumption, i.e., the underlying system is renewed to the as-good-as-new state after each preventive repair, or corrective maintenance action. Such an assumption, however, is not realistic in applications such as military machinery, power generation networks, and Cyber-Physical Systems (CPS), rendering conventional PM strategies impractical. In such application domains, it is not feasible to perform all the required maintenance actions during the available time leading to imperfect maintenance. Overlooking imperfect maintenance is critically problematic as it further deteriorates the reliability of the underlying system shortening its life span. Therefore, it is critical to perform optimal maintenance decisions under imperfect maintenance assumptions. While Geometric Process (GP) is broadly used for imperfect maintenance modeling and analysis of repairable systems, its utilization to describe the failure mechanism of production systems/processes is still in its infancy. Existing works, typically, consider restrictive assumptions to simplify the maintenance models, which limits their applicability. This paper addresses this gap and proposes a rigorous mathematical model without the incorporation of restrictive assumptions. More specifically, we consider a system for which the operational states are observable through inspections performed at specified time points and only the failure state is immediately observable. Upon the inspection, if the system is found to be in a partially-failed state, a Minor Repair (MIR) action is conducted. The effect of MIR is imperfect and MIR can only be conducted a maximum number of N times during a production cycle. After performing a MIR action, the failure mechanism of the system changes according to a stochastic decreasing GP. When the system enters the failure state, a Major Repair (MJR) action is conducted, which brings the system back to the as-good-as-new state. A comprehensive set of numerical examples, comparative studies, and sensitivity analyses are conducted to evaluate the efficacy of the proposed maintenance policy.

Suggested Citation

  • Hasan Rasay & Fariba Azizi & Farnoosh Naderkhani, 2024. "A mathematical maintenance model for a production system subject to deterioration according to a stochastic geometric process," Annals of Operations Research, Springer, vol. 340(1), pages 451-478, September.
  • Handle: RePEc:spr:annopr:v:340:y:2024:i:1:d:10.1007_s10479-024-05930-9
    DOI: 10.1007/s10479-024-05930-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-024-05930-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-024-05930-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. W Wang, 2011. "Maintenance models based on the np control charts with respect to the sampling interval," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 124-133, January.
    2. Wang, Wenbin, 2007. "A two-stage prognosis model in condition based maintenance," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1177-1187, November.
    3. Zhang, Yuan Lin & Wang, Guan Jun, 2007. "A deteriorating cold standby repairable system with priority in use," European Journal of Operational Research, Elsevier, vol. 183(1), pages 278-295, November.
    4. Kim, Michael Jong & Jiang, Rui & Makis, Viliam & Lee, Chi-Guhn, 2011. "Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure," European Journal of Operational Research, Elsevier, vol. 214(2), pages 331-339, October.
    5. Tang, Ya-yong & Lam, Yeh, 2006. "A [delta]-shock maintenance model for a deteriorating system," European Journal of Operational Research, Elsevier, vol. 168(2), pages 541-556, January.
    6. Liping Liu & Lining Jiang & Ding Zhang, 2017. "An integrated model of statistical process control and condition-based maintenance for deteriorating systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1452-1460, November.
    7. Hasan Rasay & Mohammad Saber Fallahnezhad & Yahia Zaremehrjerdi, 2019. "An integrated model of statistical process control and maintenance planning for a two-stage dependent process under general deterioration," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 13(2), pages 149-177.
    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. Wang, Wenbin, 2012. "An overview of the recent advances in delay-time-based maintenance modelling," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 165-178.
    2. Rasay, Hasan & Taghipour, Sharareh & Sharifi, Mani, 2022. "An integrated Maintenance and Statistical Process Control Model for a Deteriorating Production Process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. Hanagal David D. & Kanade Rupali A., 2010. "Optimal Replacement Policy Based on the Number of Down Times," Stochastics and Quality Control, De Gruyter, vol. 25(1), pages 3-12, January.
    4. Caiyun Niu & Xiaolin Liang & Bingfeng Ge & Xue Tian & Yingwu Chen, 2016. "Optimal replacement policy for a repairable system with deterioration based on a renewal-geometric process," Annals of Operations Research, Springer, vol. 244(1), pages 49-66, September.
    5. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    6. Jørgen Vitting Andersen & Roy Cerqueti & Giulia Rotundo, 2017. "Rational expectations and stochastic systems," Documents de travail du Centre d'Economie de la Sorbonne 17060, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Oct 2019.
    7. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    8. Zhou, Zhi-Jie & Hu, Chang-Hua & Xu, Dong-Ling & Chen, Mao-Yin & Zhou, Dong-Hua, 2010. "A model for real-time failure prognosis based on hidden Markov model and belief rule base," European Journal of Operational Research, Elsevier, vol. 207(1), pages 269-283, November.
    9. Kampitsis, Dimitris & Panagiotidou, Sofia, 2022. "A Bayesian condition-based maintenance and monitoring policy with variable sampling intervals," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    10. Boumallessa, Zeineb & Chouikhi, Houssam & Elleuch, Mounir & Bentaher, Hatem, 2023. "Modeling and optimizing the maintenance schedule using dynamic quality and machine condition monitors in an unreliable single production system," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    11. Eryilmaz, Serkan, 2012. "On the mean residual life of a k-out-of-n:G system with a single cold standby component," European Journal of Operational Research, Elsevier, vol. 222(2), pages 273-277.
    12. Yu, Miaomiao & Tang, Yinghui & Liu, Liping & Cheng, Jiang, 2013. "A phase-type geometric process repair model with spare device procurement and repairman’s multiple vacations," European Journal of Operational Research, Elsevier, vol. 225(2), pages 310-323.
    13. Wang, Wei & Wu, Zhiying & Xiong, Junlin & Xu, Yaofeng, 2018. "Redundancy optimization of cold-standby systems under periodic inspection and maintenance," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 394-402.
    14. Sinisterra, Wilfrido Quiñones & Lima, Victor Hugo Resende & Cavalcante, Cristiano Alexandre Virginio & Aribisala, Adetoye Ayokunle, 2023. "A delay-time model to integrate the sequence of resumable jobs, inspection policy, and quality for a single-component system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    15. Vahid Andalib & Jyotirmoy Sarkar, 2022. "A System with Two Spare Units, Two Repair Facilities, and Two Types of Repairers," Mathematics, MDPI, vol. 10(6), pages 1-13, March.
    16. Yuan, Li & Xu, Jian, 2011. "An optimal replacement policy for a repairable system based on its repairman having vacations," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 868-875.
    17. Ponchet, Amélie & Fouladirad, Mitra & Grall, Antoine, 2010. "Assessment of a maintenance model for a multi-deteriorating mode system," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1244-1254.
    18. Kui Wang & Chao Deng & Lili Ding, 2020. "Optimal Condition-Based Maintenance Strategy for Multi-Component Systems under Degradation Failures," Energies, MDPI, vol. 13(17), pages 1-12, August.
    19. Jørgen Vitting Andersen & Roy Cerqueti & Jessica Riccioni, 2023. "Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems," Post-Print hal-03634370, HAL.
    20. Fu, Bo & Wang, Wenbin & Shi, Xin, 2012. "A risk analysis based on a two-stage delayed diagnosis regression model with application to chronic disease progression," European Journal of Operational Research, Elsevier, vol. 218(3), pages 847-855.

    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:spr:annopr:v:340:y:2024:i:1:d:10.1007_s10479-024-05930-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.