Citation

Hughes RP. Reliab. Eng. 1987; 17(3): 211-236.

Copyright

(Copyright © 1987, Elsevier Publishing)

DOI

10.1016/0143-8174(87)90044-8

PMID

unavailable

Abstract

This paper presents a new approach to the problem of the quantification of common cause failure in systems. The basis of the new approach is the variability of a component's failure probability with 'environment', where the 'environment' means not just the obvious ambient conditions, but all the details which have a material effect on the component's performance. This variability is represented by a probability distribution for the failure probability. Different forms for this distribution describe a class of common cause failure models, which is shown to include the β-factor model and the Binomial Failure Rate model as special cases. It is also shown how this distribution can be estimated directly from data on multiple failures, so avoiding the use of any specific model. This direct procedure represents a novel way in which judgement is used to decide the relevance of particular data items to particular situations, and those considered relevant are used to construct the required probability distribution. The consequence of this direct use of data rather than fitting it to models, is a method that is general, simple, realistic rather than conservative, distinguishes between different levels of redundancy, and can be applied to 'diffuse' systems (i.e. those which rely on many or most of their components, such as control rod systems). Examples are given which show the ease with which the method can be applied, even using multiple failure data of poor quality.