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Handling parameter and model uncertainties by continuous gates in fault tree analyses

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  • F Brissaud
  • A Barros
  • C Bérenguer

Abstract

Fault tree analyses are widely used in probabilistic risk assessments (PRA) to model and evaluate safety system reliability. Coherent results can then be obtained by expressing probabilities according to input information. However, if uncertainties in parameters (e.g. failure rates) and models (e.g. relationships between events) lead to high uncertainty in results, the latter may not be robust enough to be helpful. It is therefore necessary to perform uncertainty analyses and, in order to handle both parameter and model uncertainties in a fault tree framework, a continuous gate denoted the ‘C-gate’ is proposed. By acting on ‘weights’, it is then allowed to continuously graduate model part structures between parallel and series. C-gates can also be translated into equivalent fault trees, using fictitious events, so that classical reliability tools are used to perform reliability evaluations with both parameter (failure rates) and model (through the weights) uncertainty analyses. An application on a new technology-based transmitter shows that results can be obtained with relatively low uncertainty and, in some cases, even with lower variances than any of the inputs. These properties are discussed and tend to demonstrate that the lack of knowledge on the structures of some parts of the model can be handled and partially compensated for by the proposed fault tree approach to perform PRA.

Suggested Citation

  • F Brissaud & A Barros & C Bérenguer, 2010. "Handling parameter and model uncertainties by continuous gates in fault tree analyses," Journal of Risk and Reliability, , vol. 224(4), pages 253-265, December.
  • Handle: RePEc:sae:risrel:v:224:y:2010:i:4:p:253-265
    DOI: 10.1243/1748006XJRR313
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    References listed on IDEAS

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    1. Nicolas Duflot & Christophe Bérenguer & Laurence Dieulle & Dominique Vasseur, 2009. "A min cut-set-wise truncation procedure for importance measures computation in probabilistic safety assessment," Post-Print hal-02284361, HAL.
    2. Duflot, Nicolas & Bérenguer, Christophe & Dieulle, Laurence & Vasseur, Dominique, 2009. "A min cut-set-wise truncation procedure for importance measures computation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1827-1837.
    3. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
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    Cited by:

    1. Simon, Christophe & Bicking, Frédérique, 2017. "Hybrid computation of uncertainty in reliability analysis with p-box and evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 629-638.
    2. Brissaud, Florent, 2017. "Using field feedback to estimate failure rates of safety-related systems," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 206-213.

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