SELECTION OF BETA PRIOR DISTRIBUTION PARAMETERS FROM COMPONENT FAILURE DATA

被引：4

作者：

SHULTIS, JK

ECKHOFF, ND

机构：

[1] Dept. of Nuclear Engineering, Kansas State University, Manhattan

来源：

IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS | 1979年 / 98卷 / 02期

关键词：

D O I：

10.1109/TPAS.1979.319361

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A description of classical and Bayesian techniques to estimate component failure probabilities is presented. Of particular concern is the estimation, from typically sparse component failure data, of values for the parameters of the assumed beta prior distribution (used in the Bayesian analysis) and of the failure probability distribution for a particular component with an observed performance history. Three methods for the parameter estimation are described and compared, viz. (i) matching data moments to the prior distribution moments, (ii) matching data moments to marginal distribution moments, and (iii) the maximum likelihood method. Results are presented for data from standby diesel generators used in several nuclear power plants. Copyright © 1979 by the Institute of Electrical and Electronics Engineers, Inc.

引用

页码：400 / 407

页数：8