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Nonparametric predictive reliability of series of voting systems


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  • Aboalkhair, Ahmad M.
  • Coolen, Frank P.A.
  • MacPhee, Iain M.
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    Nonparametric Predictive Inference (NPI) for system reliability reflects the dependence of reliabilities of similar components due to limited knowledge from testing. NPI has recently been presented for reliability of a single voting system consisting of multiple types of components. The components are all assumed to play the same role within the system, but with regard to their reliability components of different types are assumed to be independent. The information from tests is available per type of component. This paper presents NPI for systems with subsystems in a series structure, where all subsystems are voting systems and components of the same type can be in different subsystems. As NPI uses only few modelling assumptions, system reliability is quantified by lower and upper probabilities, reflecting the limited information in the test data. The results are illustrated by examples, which also illustrate important aspects of redundancy and diversity for system reliability.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 226 (2013)
    Issue (Month): 1 ()
    Pages: 77-84

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    Handle: RePEc:eee:ejores:v:226:y:2013:i:1:p:77-84

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    Keywords: k-out-of-m system; Lower and upper probabilities; Nonparametric predictive inference; Redundancy; System reliability; Voting system;


    References listed on IDEAS
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    1. Coolen, F. P. A. & Coolen-Schrijner, P., 2003. "A nonparametric predictive method for queues," European Journal of Operational Research, Elsevier, vol. 145(2), pages 425-442, March.
    2. Utkin, Lev V., 2004. "Interval reliability of typical systems with partially known probabilities," European Journal of Operational Research, Elsevier, vol. 153(3), pages 790-802, March.
    3. Ruiz-Castro, Juan Eloy & Li, Quan-Lin, 2011. "Algorithm for a general discrete k-out-of-n: G system subject to several types of failure with an indefinite number of repairpersons," European Journal of Operational Research, Elsevier, vol. 211(1), pages 97-111, May.
    4. Wang, Yong & Li, Lin & Huang, Shuhong & Chang, Qing, 2012. "Reliability and covariance estimation of weighted k-out-of-n multi-state systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 138-147.
    5. Abellán, Joaquín & Baker, Rebecca M. & Coolen, Frank P.A., 2011. "Maximising entropy on the nonparametric predictive inference model for multinomial data," European Journal of Operational Research, Elsevier, vol. 212(1), pages 112-122, July.
    6. 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.
    7. Houlding, B. & Coolen, F.P.A., 2012. "Nonparametric predictive utility inference," European Journal of Operational Research, Elsevier, vol. 221(1), pages 222-230.
    8. Long, Q. & Xie, M. & Ng, S.H. & Levitin, Gregory, 2008. "Reliability analysis and optimization of weighted voting systems with continuous states input," European Journal of Operational Research, Elsevier, vol. 191(1), pages 240-252, November.
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