Modelling common cause failures under data uncertainty of the system unavailability

Abstract This paper uses Yeh's work to model common cause failures (CCFs) during the data uncertainty analysis of the system unavailability. The unavailabilities due to independent failure of the equipments sharing a common environment are regarded during the uncertainty analysis as correlated variables in modelling common cause failures due to a known stochastic coupling. The correlation between the unavailability of the system due to independent failure and that due to definite and simultaneous unavailability of all the equipments of the system because of a common cause, has also been considered. Further, a method has been described to simulate the values of correlated log-normal variables. The results of the simulation studies carried out using this method for a 2-out-of-3 system have also been described. It was found, as expected, that as correlation co-efficients between unavailabilities of component unavailabilities or between those due to independent and dependent failures increase, the dispersion of the distribution of the system unavailability increases. Also, the distribution tends to become log-normal. It has been pointed out that, while several other approaches for common cause analysis can provide “point estimates”, the approach studied here helps in the treatment of data uncertainties. The approach of this paper appears to have a sounder physical and statistical basis than most of the existing models and is simpler as it requires only two additional parameters to be estimated for a redundant system with any number of identical components, while other models require more.