Estimation Techniques for Common Cause Failure Data With Difference System Sizes

Article

Kvam, Paul H.   (1996, American Statistical Association and ASQC)   Georgia Institute of Technology, Atlanta, GA

Technometrics    Vol. 38    No. 4
QICID: 13667    November 1996    pp. 382-388
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Article Abstract

Modeling of system lifetimes becomes more complicated when external events can cause the simultaneous failure of two or more system components. Models that ignore these common cause failures lead to methods of analysis that overestimate system reliability. Typical data consist of observed frequencies in which i out of m (identical) components in a system failed simultaneously, i = 1,...,m. Because this attribute data is inherently dependent on the number of components in the system, procedures for interpretation of data from different groups with more or fewer components than the system under study are not straightforward. This is a recurrent problem in reliability applications in which component configurations change from one system to the next. For instance, in the analysis of a large power-supply system that has three stand-by diesel generators in case of power loss, statistical tests and estimates of system reliability cannot be derived easily from data pertaining to different plants for which only one or two diesel generators were used to reinforce the main power source. This article presents, discusses, and analyzes methods to use generic attribute reliability data efficiently for systems of varying size.

Keywords

Alpha-factor model,Reliability,Failure analysis,Attribute data


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