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In the safety assessment of critical systems, the probability of system failure over the mission duration constitutes a key reliability metric guiding design and certification decisions. Based on this indicator, safety-related design and certification decisions are made. Various types of models propose solutions to compute this probability of failure. However, there are always some epistemic uncertainties on the behavior of the system components, leading to some variance of the computed probability of failure regardless of the model. We assess the influence of epistemic uncertainties in the input parameters on the system failure probability through variance-based sensitivity indices. We then study the potential of importance sampling for the low-cost evaluation of sensitivity indices. We apply this methodology on different AltaRica 3.0 use cases and illustrate the efficiency and limits of sensitivity analysis in this safety context.

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Variance-Based Sensitivity Analysis for Probabilistic Risk Assessment

  • 会议论文

  • 发表日期：

    2026年

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  • Jonathan Mboko,
  • Jérôme Morio,
  • Christel Seguin,
  • Jean-Charles Chaudemar,
  • Tatiana Prosvirnova

摘要

In the safety assessment of critical systems, the probability of system failure over the mission duration constitutes a key reliability metric guiding design and certification decisions. Based on this indicator, safety-related design and certification decisions are made. Various types of models propose solutions to compute this probability of failure. However, there are always some epistemic uncertainties on the behavior of the system components, leading to some variance of the computed probability of failure regardless of the model. We assess the influence of epistemic uncertainties in the input parameters on the system failure probability through variance-based sensitivity indices. We then study the potential of importance sampling for the low-cost evaluation of sensitivity indices. We apply this methodology on different AltaRica 3.0 use cases and illustrate the efficiency and limits of sensitivity analysis in this safety context.