PSA分析中可靠性参数的Kass-Steffey修正原理及应用研究

Study on Theory and Application of Kass-Steffey Adjustment for Parameter Estimation of PSA

可靠性参数是核电厂概率安全分析评价（PSA）的基础,参数经验贝叶斯方法（PEB）在处理少量失效数据样本时会低估待估可靠性参数的不确定性;Kass-Steffey修正方法采用泰勒展开对参数的后验方差进行修正可以解决参数低估问题。研究Kass-Steffey修正原理并推导出一阶修正公式,计算带Kass-Steffey修正的多个核电厂始发事件频率的参数后验估计方差及90%的置信区间值。计算结果表明,对于失效数据次数多的样本,Kass-Steffey修正对后验方差及估计区间影响较小;对于失效数据稀少的样本,Kass-Steffey修正值得关注,修正后的后验方差变化16%~99%,置信区间值变化4%~53%。

Reliability data is the basis of probabilistic safety assessment in NPPs. The parametric empirical Bayes models would underestimate the uncertainty of estimated parameter in the case with few failure data. Kass-Steffey adjustment could use Taylor series expansion to correct the posterior variance. The Kass-Steffey adjustment were derived in detail, and taking the initiating events as an example, the posterior variance and 90% credible interval are calculated. It is found that the Kass-Steffey adjustment is unimportant if there are many failure data, while it is noted that the Kass-Steffey adjustment is very important when there are few failure data, which make the posterior variance change 16%~99%, and credible interval change 4%~53%.