Abstract: The degradation data and lifetime data of products are important reliability information. The accuracy of reliability evaluation usually can be improved by the fusion of these data. However, the existing methods are mainly based on Bayes theory where the calculation is complicated and required high computational cost to satisfy accuracy requirement. Meanwhile, the unbalanced data sample size can easily result in "data cover" problem. A phased fusion approach of degradation and lifetime data for product reliability assessment is proposed, which contained modelling the stochastic process degradation model on basis of degradation data, calculating the point estimations of parameters and updating the failure probability on basis of lifetime data. Data fusion is achieved by folding the two types of information in two phases. The estimations of parameters in degradation model and product assessment are finished by fitting the lifetime distribution finally. Fact is proved by simulation study and illustrative example that, compared with Bayes fusion approach, the proposed phased fusion method efficiently improves the accuracy and computational cost under new idea for reliability data fusion. More importantly, it also avoids the "data cover" problem effectively.

Key words: reliability evaluation, Bayes theory, stochastic process, failure probability