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Simulation-Informed Probabilistic Methodology for Common Cause Failure Analysis

机译：常见原因失效分析的仿真通知概率方法

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摘要

Common Cause Failures (CCFs) are critical risk contributors in complex technological systems as they challenge multiple redundant systems simultaneously. To improve the CCF analysis in Probabilistic Risk Assessment (PRA), this research develops the Simulation-Informed Probabilistic Methodology (S-IPM) for CCFs. This new methodology utilizes simulation models of physical failure mechanisms to capture underlying causalities and to generate simulation-based data for the CCF probability estimation. To operationalize the S-IPM in PRA, a computational algorithm is developed that generates simulation-based estimates of CCF parameters and, using the Bayesian approach, integrates them with the data-driven CCF parameters (if relevant data available) from the existing PRA. This computational algorithm is equipped with the Probabilistic Validation that quantifies the degree of confidence in the simulation-based parameter estimates by characterizing and propagating epistemic uncertainty in multiple levels of analysis. The S-IPM can (i) provide more realistic CCF probability estimates by considering CCF data generated from simulations; (ii) reflect as-built, as-operated plant conditions, considering the updates in design, operational, and maintenance policies; and (iii) contribute to more effective prevention and mitigation of CCFs by providing "cause-specific" quantitative risk insights. The paper shows a case study that applies S-IPM to the CCFs of emergency service water pumps of NPPs.
机译：常见原因故障（CCF）是复杂技术系统中的关键风险因素，因为它们会同时挑战多个冗余系统。为了改进概率风险评估（PRA）中的CCF分析，本研究开发了针对CCF的模拟信息概率方法（S-IPM）。这种新方法利用物理故障机制的仿真模型来捕获潜在的因果关系，并为CCF概率估计生成基于仿真的数据。为了在PRA中实现S-IPM，开发了一种计算算法，该算法生成基于仿真的CCF参数估计值，并使用贝叶斯方法将它们与现有PRA的数据驱动CCF参数（如果有相关数据）集成在一起。该计算算法配备了概率验证，该概率验证通过在多个分析级别中表征和传播认知不确定性来量化基于仿真的参数估计中的置信度。 S-IPM可以（i）通过考虑从模拟生成的CCF数据来提供更现实的CCF概率估计； （ii）考虑设计，运营和维护政策的更新，以反映建成，运营中的工厂状况； （iii）通过提供“特定于原因”的定量风险洞见，有助于更有效地预防和减轻CCF。本文展示了将S-IPM应用于核电厂应急水泵CCF的案例研究。

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