DocumentCode :
2426158
Title :
Bayes reliability demonstration test plan for series-systems with binomial subsystem data
Author :
Ten, Lin Mei ; Xie, Min
Author_Institution :
DSO National Labs., Singapore
fYear :
1998
fDate :
19-22 Jan 1998
Firstpage :
241
Lastpage :
246
Abstract :
One reason that the Bayesian approach to reliability demonstration has not gained popularity in industry is the difficulty in establishing the prior. The problem becomes more complicated when only subsystem data are available. It has received little attention in the existing literature and this paper makes an attempt to do that. A method is proposed to derive the Bayesian reliability demonstration test plan for series systems with binomial subsystem data. The method makes use of Mann´s approximately optimum lower confidence bound model to derive the system prior based on binomial subsystem data. The system Bayesian reliability demonstration test plan can then be derived using existing methods for meeting posterior confidence requirements. The proposed method is easy to apply and no complicated computation is involved in deriving the system prior distribution. It uses objective subsystem test data. No subjective judgement is required. This method is most beneficial for systems that already have substantial subsystem test data before the reliability demonstration
Keywords :
Bayes methods; failure analysis; reliability theory; Bayes reliability demonstration test plan; Mann´s approximately optimum lower confidence bound model; binomial subsystem data; posterior confidence requirements; prior establishment; series-systems; Bayesian methods; Computer industry; Costs; Curve fitting; Distributed computing; Laboratories; Large-scale systems; Meeting planning; Reliability; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Reliability and Maintainability Symposium, 1998. Proceedings., Annual
Conference_Location :
Anaheim, CA
ISSN :
0149-144X
Print_ISBN :
0-7803-4362-X
Type :
conf
DOI :
10.1109/RAMS.1998.653780
Filename :
653780
Link To Document :
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