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Using higher-level failure data in fault tree quantification

delete1997-04-01
delete10
PRE
AI
H
Harry F. Martz *
R
Russell G. Almond
DOI：10.1016/S0951-8320(96)00134-2delete
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Abstract

Abstract

En 中文
This paper presents a Bayesian method which can simultaneously combine basic event and statistically independent higher event-level failure data in fault tree quantification. Such higher-level data could correspond to train, subsystem or system failure events. In fact, because highest-level data are usually available for existing facilities, the method presented here allows such data to be propagated to lower levels. The method has two stages: (1) a top-down propagation scheme which allocates the higher event-level information to the basic events, at a cost of making them dependent; and (2) a scheme for sampling the probabilities of the dependent basic events. A simple example illustrates the performance of the method. (C) 1997 Elsevier Science Limited.
Keywords:
BAYESIAN-ESTIMATION
PERFECT AGGREGATION
RELIABILITY
SUBSYSTEMS
DEPENDENCE
MODELS
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Journal

R
Reliability Engineering and System Safety
IF:
11
Papers:
9.0K
Citations:
4.2W

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