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Reliability Estimation in Load-Sharing System Model with Application to Real Data

Author

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  • Pramendra Singh Pundir

    (University of Allahabad)

  • Puneet Kumar Gupta

    (University of Allahabad)

Abstract

This study deals with the reliability analysis of a multi-component load sharing system where failure of any component within the system induces higher failure rate on the remaining surviving components. It is assumed that each component failure time follows Chen distribution. In classical set up, the maximum likelihood estimates of the load sharing parameters, system reliability and hazard rate along with their standard errors are computed. Since maximum likelihood estimates are not in closed form, so asymptotic confidence intervals and two bootstrap confidence intervals for the unknown parameters have also been constructed. Further, by assuming both informative and non-informative prior for the unknown parameters, Bayes estimates along with their posterior standard errors and HPD intervals of the parameters are obtained. Thereafter, a simulation study elicitates the theoretical developments. A real data analysis, at the end, eshtablishes the applicability of the proposed theory.

Suggested Citation

  • Pramendra Singh Pundir & Puneet Kumar Gupta, 2018. "Reliability Estimation in Load-Sharing System Model with Application to Real Data," Annals of Data Science, Springer, vol. 5(1), pages 69-91, March.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:1:d:10.1007_s40745-017-0120-5
    DOI: 10.1007/s40745-017-0120-5
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    References listed on IDEAS

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    Cited by:

    1. Azeem Ali & Sanku Dey & Haseeb Ur Rehman & Zeeshan Ali, 2019. "On Bayesian reliability estimation of a 1-out-of-k load sharing system model of modified Burr-III distribution," 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 1052-1081, October.

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