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Reliability Demonstration Testing (RDT) has been widely used in industry to verify whether a product has met a certain reliability requirement with a stated confidence level. To design RDTs, methods have been developed based on either the number of failures or the failure times. However, practitioners often have difficulty in determining which method to use for a specific design problem. In particular, the method based on the number of failures cannot be used when all the units are tested to failure, while the alternative based on failure times falls short in dealing with cases where no failures are expected. This paper elaborates on the two methods, and compares them from both practical and theoretical standpoints. The detailed discussions regarding the relationship between the two methods will help practitioners design RDTs, and understand when the two methods will lead to similar designs. A Weibull distribution is used in the relevant mathematical derivations, but the results can be extended to other widely used failure time distributions. Case studies are provided to demonstrate the use of the two methods in practice, and in developing equivalent RDT designs. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Numerous stochastic models for repairable systems have been developed by assuming different time trends, and repair effects. In this paper, a new general repair model based on the repair history is presented. Unlike the existing models, the closed-form solutions of the reliability metrics can be derived analytically by solving a set of differential equations. Consequently, the confidence bounds of these metrics can be easily estimated. The proposed model, as well as the estimation approach, overcomes the drawbacks of the existing models. The practical use of the proposed model is demonstrated by a much-discussed set of data. Compared to the existing models, the new model is convenient, and provides accurate estimation results. [ABSTRACT FROM AUTHOR]