Bayesian reliability analysis of high-censoring motor lifetime data
In order to enhance the assessment accuracy of high-censoring feature lifetime data and obtain interval estimates of failure probability as well as point estimates of distribution models,this paper proposes an estimation method for simulating failure probabilities based on the MCMC Gibbs algorithm combined with the rejection method.It also calculates confidence intervals.Firstly,the ordinal characteristics of failure probabilities in practical scenarios are considered.Under the framework of Bayesian theory,a hyperparameter-optimized model is constructed by taking into account the range of failure probabilities and employing the rejection method.This approach helps capture the features of failure probabilities more effectively.Subsequently,failure probabilities are extracted based on the likelihood equation.Through simulations and the analysis of lifetime data from motor structures with high-censoring features,it has been demonstrated that this method,under the assumption of the Weibull distribution model,outperforms the E-Bayes method in terms of the accuracy of failure probability estimation.It provides a better fit to the data.
万方数据
维普
