Analysis of Common Cause Failure Data in Nuclear Power Plant Safety Assessment

这项拨款支持开发新的统计方法，用于分析复杂系统的共同原因故障数据，如核电站组件组。为了对共因故障进行建模，该研究可以在用于电厂风险评估的参数冲击模型的基础上开发二项故障率混合模型。除了这个混合模型估计器之外，还将追求各种结果，包括非参数估计器和参数估计器；置信范围和其他不确定性措施；非相同分量系统的一般解；以及相关的极限性质。该混合模型将为进一步研究基于相关失效模型的统计分析提供一个出发点。本研究还探讨了在风险评估中用估计数据代替缺失数据的制图技术，并将有助于描述除蒙特卡罗模拟外用于制图过程的各种模型所涉及的不确定性。原有的二项失效率模型由于在现有的许多数据库中模型拟合较差而受到批评。与原始模型或其非参数替代模型相比，该研究中的现实泛化更为复杂，但模型拟合可以大大提高。工程文献对定性分析（筛选分析、故障树分析等）有较强的基础，但对一些定量方面的共因故障研究指导较少。如果这项研究成功，将有助于核安全领域的定量风险分析。

This grant supports the development of new statistical methods for analyzing common cause failure data for complex systems, such as nuclear power plant component groups. For the purpose of modeling common cause failures, the research can lead to the development of a binomial failure rate mixture model, based on a parametric shock model developed for power plant risk assessment. Beyond this mixture model estimator, various results will be pursued, including both non-parametric and parametric estimators; confidence bands and other measures of uncertainty; general solutions for systems of non identical components; and, pertinent limit properties. The mixture model will serve as a point of departure to further research into statistical analyses based on dependent failure models. This research also investigates techniques of mapping that replace missing data with estimated data in the risk assessment, and will help to characterize the uncertainty involved with various models used for the mapping procedure using statistical theory in addition to Monte Carlo simulation. The original binomial failure rate model has been criticized due to the many existing databases for which the model fit is poor. The realistic generalization in this investigation is more complex than the original model or its non-parametric alternatives, but model fit can be greatly improved. There exists a strong foundation of qualitative analysis in the engineering literature (screening analysis, fault tree analysis, etc.), but little guidance with respect to some quantitative aspects common cause failure research. If successful, this research will contribute to quantitative risk analyses in the nuclear safety field.