IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i16p1997-d618629.html
   My bibliography  Save this article

Sequential Interval Reliability for Discrete-Time Homogeneous Semi-Markov Repairable Systems

Author

Listed:
  • Vlad Stefan Barbu

    (Laboratory of Mathematics Raphaël Salem, University of Rouen-Normandy, UMR 6085, Avenue de l’Université, BP. 12, F76801 Saint-Étienne-du-Rouvray, France)

  • Guglielmo D’Amico

    (Department of Economics, University “G. d’Annunzio” of Chieti-Pescara, 66013 Pescara, Italy)

  • Thomas Gkelsinis

    (Laboratory of Mathematics Raphaël Salem, University of Rouen-Normandy, UMR 6085, Avenue de l’Université, BP. 12, F76801 Saint-Étienne-du-Rouvray, France)

Abstract

In this paper, a new reliability measure, named sequential interval reliability, is introduced for homogeneous semi-Markov repairable systems in discrete time. This measure is the probability that the system is working in a given sequence of non-overlapping time intervals. Many reliability measures are particular cases of this new reliability measure that we propose; this is the case for the interval reliability, the reliability function and the availability function. A recurrent-type formula is established for the calculation in the transient case and an asymptotic result determines its limiting behaviour. The results are illustrated by means of a numerical example which illustrates the possible application of the measure to real systems.

Suggested Citation

  • Vlad Stefan Barbu & Guglielmo D’Amico & Thomas Gkelsinis, 2021. "Sequential Interval Reliability for Discrete-Time Homogeneous Semi-Markov Repairable Systems," Mathematics, MDPI, vol. 9(16), pages 1-18, August.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1997-:d:618629
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/16/1997/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/16/1997/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bulla, Jan & Bulla, Ingo, 2006. "Stylized facts of financial time series and hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2192-2209, December.
    2. Moura, Márcio das Chagas & Droguett, Enrique López, 2009. "Mathematical formulation and numerical treatment based on transition frequency densities and quadrature methods for non-homogeneous semi-Markov processes," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 342-349.
    3. Gianfranco Corradi & Jacques Janssen & Raimondo Manca, 2004. "Numerical Treatment of Homogeneous Semi-Markov Processes in Transient Case–a Straightforward Approach," Methodology and Computing in Applied Probability, Springer, vol. 6(2), pages 233-246, June.
    4. Samis Trevezas & Nikolaos Limnios, 2011. "Exact MLE and asymptotic properties for nonparametric semi-Markov models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 719-739.
    5. Guglielmo D’Amico & Jacques Janssen & Raimondo Manca, 2010. "Initial and Final Backward and Forward Discrete Time Non-homogeneous Semi-Markov Credit Risk Models," Methodology and Computing in Applied Probability, Springer, vol. 12(2), pages 215-225, June.
    6. I. Votsi & G. Gayraud & V. S. Barbu & N. Limnios, 2021. "Hypotheses testing and posterior concentration rates for semi-Markov processes," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 707-732, October.
    7. Bulla, Jan, 2006. "Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series," MPRA Paper 7675, University Library of Munich, Germany.
    8. Guglielmo D’Amico & Raimondo Manca & Filippo Petroni & Dharmaraja Selvamuthu, 2021. "On the Computation of Some Interval Reliability Indicators for Semi-Markov Systems," Mathematics, MDPI, vol. 9(5), pages 1-23, March.
    9. Guglielmo D’Amico & Jacques Janssen & Raimondo Manca, 2016. "Downward migration credit risk problem: a non-homogeneous backward semi-Markov reliability approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(3), pages 393-401, March.
    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. Guglielmo D’Amico & Thomas Gkelsinis, 2024. "On a Mixed Transient–Asymptotic Result for the Sequential Interval Reliability for Semi-Markov Chains," Mathematics, MDPI, vol. 12(12), pages 1-18, June.
    2. D’Amico, Guglielmo & Petroni, Filippo, 2023. "ROCOF of higher order for semi-Markov processes," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    3. P.-C.G. Vassiliou & Andreas C. Georgiou, 2021. "Markov and Semi-Markov Chains, Processes, Systems, and Emerging Related Fields," Mathematics, MDPI, vol. 9(19), pages 1-6, October.
    4. Ming-Na Tong & Fu-Qiang Shen & Chen-Xing Cui, 2022. "The Inverse Transformation of L-Hermite Model and Its Application in Structural Reliability Analysis," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    5. Fanping Wei & Jingjing Wang & Xiaobing Ma & Li Yang & Qingan Qiu, 2023. "An Optimal Opportunistic Maintenance Planning Integrating Discrete- and Continuous-State Information," Mathematics, MDPI, vol. 11(15), pages 1-19, July.

    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. Guglielmo D’Amico & Thomas Gkelsinis, 2024. "On a Mixed Transient–Asymptotic Result for the Sequential Interval Reliability for Semi-Markov Chains," Mathematics, MDPI, vol. 12(12), pages 1-18, June.
    2. Vlad Stefan Barbu & Guglielmo D’Amico & Andreas Makrides, 2022. "A Continuous-Time Semi-Markov System Governed by Stepwise Transitions," Mathematics, MDPI, vol. 10(15), pages 1-12, August.
    3. Milan Kumar Das & Anindya Goswami, 2019. "Testing of binary regime switching models using squeeze duration analysis," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-20, March.
    4. Hunt, Julien & Devolder, Pierre, 2011. "Semi Markov regime switching interest rate models and minimal entropy measure," LIDAM Discussion Papers ISBA 2011010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Wang, Shixuan & Gupta, Rangan & Zhang, Yue-Jun, 2021. "Bear, Bull, Sidewalk, and Crash: The Evolution of the US Stock Market Using Over a Century of Daily Data," Finance Research Letters, Elsevier, vol. 43(C).
    6. Holzmann, Hajo & Schwaiger, Florian, 2016. "Testing for the number of states in hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 318-330.
    7. Laurie Davies & Walter Kraemer, 2016. "Stylized Facts and Simulating Long Range Financial Data," CESifo Working Paper Series 5796, CESifo.
    8. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    9. Andrea Arfè & Stefano Peluso & Pietro Muliere, 2021. "The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes," Statistical Inference for Stochastic Processes, Springer, vol. 24(1), pages 1-15, April.
    10. Maruotti, Antonello & Petrella, Lea & Sposito, Luca, 2021. "Hidden semi-Markov-switching quantile regression for time series," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    11. Guglielmo D'Amico & Filippo Petroni, 2020. "A micro-to-macro approach to returns, volumes and waiting times," Papers 2007.06262, arXiv.org.
    12. Milan Kumar Das & Anindya Goswami & Sharan Rajani, 2019. "Inference of Binary Regime Models with Jump Discontinuities," Papers 1910.10606, arXiv.org, revised Mar 2022.
    13. P.-C.G. Vassiliou, 2020. "Non-Homogeneous Semi-Markov and Markov Renewal Processes and Change of Measure in Credit Risk," Mathematics, MDPI, vol. 9(1), pages 1-27, December.
    14. Kaihua Deng, 2018. "Another Look at Large-Cap Stock Return Comovement: A Semi-Markov-Switching Approach," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 227-262, February.
    15. Gallo, Giampiero M. & Otranto, Edoardo, 2008. "Volatility spillovers, interdependence and comovements: A Markov Switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3011-3026, February.
    16. Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
    17. Elizabeth Fons & Paula Dawson & Jeffrey Yau & Xiao-jun Zeng & John Keane, 2019. "A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing," Papers 1902.10849, arXiv.org.
    18. Milan Kumar Das & Anindya Goswami, 2018. "Testing of Binary Regime Switching Models using Squeeze Duration Analysis," Papers 1807.04393, arXiv.org, revised Aug 2018.
    19. Puneet Pasricha & Dharmaraja Selvamuthu & Guglielmo D’Amico & Raimondo Manca, 2020. "Portfolio optimization of credit risky bonds: a semi-Markov process approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-14, December.
    20. Apergis, Nicholas & Gozgor, Giray & Lau, Chi Keung Marco & Wang, Shixuan, 2019. "Decoding the Australian electricity market: New evidence from three-regime hidden semi-Markov model," Energy Economics, Elsevier, vol. 78(C), pages 129-142.

    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:9:y:2021:i:16:p:1997-:d:618629. 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.