IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i1p150-d717631.html
   My bibliography  Save this article

Methodology for the Assessment of Imprecise Multi-State System Availability

Author

Listed:
  • Joanna Akrouche

    (CNRS, Laboratoire Heudiasyc (Heuristics and Diagnosis of Complex Systems), Université de Technologie de Compiègne, CS 60 319, 60203 Compiègne, France)

  • Mohamed Sallak

    (CNRS, Laboratoire Heudiasyc (Heuristics and Diagnosis of Complex Systems), Université de Technologie de Compiègne, CS 60 319, 60203 Compiègne, France)

  • Eric Châtelet

    (12 Rue Marie Curie-CS 42060 10010, Université de Technologie de Troyes, UR InSyTE, 10300 Troyes, France)

  • Fahed Abdallah

    (Luxembourg Institute of Socio-Economic Research (LISER), 11 Porte des Sciences, L-4366 Esch-sur-Alzette, Luxembourg
    Faculty of Engineering, Lebanese University, Beirut 14-6573, Lebanon)

  • Hiba Hajj Chehade

    (Faculty of Engineering, Lebanese University, Beirut 14-6573, Lebanon)

Abstract

Most existing studies of a system’s availability in the presence of epistemic uncertainties assume that the system is binary. In this paper, a new methodology for the estimation of the availability of multi-state systems is developed, taking into consideration epistemic uncertainties. This paper formulates a combined approach, based on continuous Markov chains and interval contraction methods, to address the problem of computing the availability of multi-state systems with imprecise failure and repair rates. The interval constraint propagation method, which we refer to as the forward–backward propagation (FBP) contraction method, allows us to contract the probability intervals, keeping all the values that may be consistent with the set of constraints. This methodology is guaranteed, and several numerical examples of systems with complex architectures are studied.

Suggested Citation

  • Joanna Akrouche & Mohamed Sallak & Eric Châtelet & Fahed Abdallah & Hiba Hajj Chehade, 2022. "Methodology for the Assessment of Imprecise Multi-State System Availability," Mathematics, MDPI, vol. 10(1), pages 1-25, January.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:1:p:150-:d:717631
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/1/150/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/1/150/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lisnianski, Anatoly, 2007. "Extended block diagram method for a multi-state system reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1601-1607.
    2. Zio, E. & Marella, M. & Podofillini, L., 2007. "A Monte Carlo simulation approach to the availability assessment of multi-state systems with operational dependencies," Reliability Engineering and System Safety, Elsevier, vol. 92(7), pages 871-882.
    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. Song, Xiaogang & Zhai, Zhengjun & Liu, Yidong & Han, Jie, 2018. "A stochastic approach for the reliability evaluation of multi-state systems with dependent components," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 257-266.
    2. Wu, Bei & Cui, Lirong & Fang, Chen, 2020. "Multi-state balanced systems with multiple failure criteria," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    3. Jafary, Bentolhoda & Fiondella, Lance, 2016. "A universal generating function-based multi-state system performance model subject to correlated failures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 16-27.
    4. Yu, Shui & Wang, Zhonglai & Zhang, Kewang, 2018. "Sequential time-dependent reliability analysis for the lower extremity exoskeleton under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 45-52.
    5. Sheu, Shey-Huei & Chang, Chin-Chih & Chen, Yen-Luan & George Zhang, Zhe, 2015. "Optimal preventive maintenance and repair policies for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 78-87.
    6. Dong, Wenjie & Liu, Sifeng & Tao, Liangyan & Cao, Yingsai & Fang, Zhigeng, 2019. "Reliability variation of multi-state components with inertial effect of deteriorating output performances," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 176-185.
    7. Lisnianski, Anatoly & Ding, Yi, 2009. "Redundancy analysis for repairable multi-state system by using combined stochastic processes methods and universal generating function technique," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1788-1795.
    8. Girish Kumar & Vipul Jain & Umang Soni, 2019. "Modelling and simulation of repairable mechanical systems reliability and availability," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1221-1233, October.
    9. Yen-Luan Chen & Chin-Chih Chang & Dwan-Fang Sheu, 2016. "Optimum random and age replacement policies for customer-demand multi-state system reliability under imperfect maintenance," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(5), pages 1130-1141, April.
    10. Shan, Xiaofang & Wang, Peng & Lu, Weizhen, 2017. "The reliability and availability evaluation of repairable district heating networks under changeable external conditions," Applied Energy, Elsevier, vol. 203(C), pages 686-695.
    11. Serkan Eryilmaz, 2014. "A new look at dynamic behavior of binary coherent system from a state-level perspective," Annals of Operations Research, Springer, vol. 212(1), pages 115-125, January.
    12. Durga Rao, K. & Gopika, V. & Sanyasi Rao, V.V.S. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2009. "Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 872-883.
    13. Cavalieri, Francesco, 2020. "Seismic risk assessment of natural gas networks with steady-state flow computation," International Journal of Critical Infrastructure Protection, Elsevier, vol. 28(C).
    14. Rebello, Sinda & Yu, Hongyang & Ma, Lin, 2018. "An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 124-135.
    15. Verlinden, Steven & Deconinck, Geert & Coupé, Bernard, 2012. "Hybrid reliability model for nuclear reactor safety system," Reliability Engineering and System Safety, Elsevier, vol. 101(C), pages 35-47.
    16. Zhang, Yongjin & Zhao, Ming & Zhang, Yanjun & Pan, Ruilin & Cai, Jing, 2020. "Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers," European Journal of Operational Research, Elsevier, vol. 283(2), pages 491-510.
    17. Huang, Tudi & Xiahou, Tangfan & Mi, Jinhua & Chen, Hong & Huang, Hong-Zhong & Liu, Yu, 2024. "Merging multi-level evidential observations for dynamic reliability assessment of hierarchical multi-state systems: A dynamic Bayesian network approach," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    18. Y Liu & H-Z Huang & G Levitin, 2008. "Reliability and performance assessment for fuzzy multi-state elements," Journal of Risk and Reliability, , vol. 222(4), pages 675-686, December.
    19. Xu, Ming & Chen, Tao & Yang, Xianhui, 2012. "Optimal replacement policy for safety-related multi-component multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 87-95.
    20. Tian, Zhigang & Levitin, Gregory & Zuo, Ming J., 2009. "A joint reliability–redundancy optimization approach for multi-state series–parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1568-1576.

    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:jmathe:v:10:y:2022:i:1:p:150-:d:717631. 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.