Abstract
It is difficult to estimate the confidence interval of a system's reliability with zero failures experienced. We approach this problem by proposing a hybrid model that integrates the Bayesian model with the variance propagation technique. The Bayesian model will compute the moments of component reliability estimates, and the variance propagation technique is used to estimate the system reliability variance. The confidence interval for the system reliability is then derived by matching the moments with a beta distribution. As a major contribution, the distribution for reliability estimates with zero failures is explicitly derived. The performance of the new model is compared with existing methods, and further validated by simulation data. The results show that the hybrid model generally outperforms existing methods in terms of estimation accuracy. Because the new model does not require multiple integral calculations, it can be applied to design complex systems configured in mixed series-parallel or networked components.
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