文档介绍：Reliability Engineering and System Safety 66 (1999) 177–198
ate/ress
Bayesian decision analysis and reliability certification
. Papazoglou*
System Reliability and Industrial Safety Laboratory, Institute of Nuclear Technology-Radiation Protection, National Center for Scientific Research,
Demokritos, Aghia Paraskevi 15310, Greece
Received 10 November 1998; accepted 13 February 1999
Abstract
Reliability certification is set as a problem of Bayesian Decision Analysis. Uncertainties about the system reliability are quantified by
assuming the parameters of the models describing the stochastic behavior ponents as random variables. A utility function quantifies
the relative value of each possible level of system reliability after having been accepted or the opportunity loss of the same level if the system
has been rejected. A decision about accepting or rejecting the system can be made either on the basis of the existing prior assessment of
uncertainties or after obtaining further information through testing of ponents or the system at a cost. The concepts of value of perfect
information, expected value of sample information and the gain of sampling are specialized to the reliability of a multi-
component system to determine the ponent testing scheme prior to deciding on the system’s certification. ponent
importance ranking is proposed on the basis of the expected value of perfect information about the reliability of ponent. The
proposed approach is demonstrated on a ponent system failing according to a Poisson random process and with natural conjugate
prior probability density functions (pdf) for the failure rate and for a ponent system under general assumptions. ᭧ 1999 Elsevier
Science Ltd. All rights reserved.
Keywords: Reliability certification; Uncertainty quantification; Bayesian decision analysis; Uncertainty-importance ranking
Nomenclature CVSI: stands for “Conditional Value of Sample
Information”
x~: denotes that variable x is a random var