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Nonparametric predictive reliability of series of voting systems

Author

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  • Aboalkhair, Ahmad M.
  • Coolen, Frank P.A.
  • MacPhee, Iain M.

Abstract

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.

Suggested Citation

  • Aboalkhair, Ahmad M. & Coolen, Frank P.A. & MacPhee, Iain M., 2013. "Nonparametric predictive reliability of series of voting systems," European Journal of Operational Research, Elsevier, vol. 226(1), pages 77-84.
  • Handle: RePEc:eee:ejores:v:226:y:2013:i:1:p:77-84
    DOI: 10.1016/j.ejor.2012.11.001
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    References listed on IDEAS

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    1. da Costa Bueno, Vanderlei & Martins do Carmo, Iran, 2007. "Active redundancy allocation for a k-out-of-n:F system of dependent components," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1041-1051, January.
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    7. 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.
    8. 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.
    9. 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.
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    Cited by:

    1. Ben Abdallah, N. & Destercke, S. & Sallak, M., 2017. "Easy and optimal queries to reduce set uncertainty," European Journal of Operational Research, Elsevier, vol. 256(2), pages 592-604.
    2. repec:eee:reensy:v:136:y:2015:i:c:p:140-144 is not listed on IDEAS

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