ESTIMATION TECHNIQUES FOR COMMON-CAUSE FAILURE DATA WITH DIFFERENT SYSTEM SIZES

Modeling of system lifetimes becomes more complicated when external ev ents can cause the simultaneous failure of two or more system componen ts. 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, pr ocedures 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 comp onent configurations change from one system to the next. For instance, in the analysis of a large power-supply system that has three stand-b y diesel generators in case of power loss, statistical tests and estim ates of system reliability cannot be derived easily from data pertaini ng to different plants for which only one or two diesel generators wer e used to reinforce the main power source. This article presents, disc usses, and analyzes methods to use generic attribute reliability data efficiently for systems of varying size.