Competing Failure Model Based On Inverse Gassian Process

With the development of science and technology,the complexity of products is also increasing.For complex systems reflect the characteristics of long life,high cost,less samples and diversified failure modes.In this paper,a competitive failure model is established to analyze the reliability of complex systems by considering the degradation data and truncated data.Firstly,by modeling the soft failure as the inverse Gaussian process,and the hard failure as a Weibull distribution,we obtain the competing failure model involving both catastrophic and degradation failures.Degradation and truncated test data are adopted to establish the model and conduct the maximum likelihood estimation,and asymptotic normality is used to calculate the interval estimation of model parameters.The results of the numerical simulation prove the feasibility of the established model,and its comparison with the model based on accurate failure data proves the superiority of the established model.Finally,the real data analysis verifies that the competing failure model based on inverse Gaussian process fits well.Secondly,since classical statistical methods would ignore some prior information,non-information priors for the model were selected to conduct Bayesian estimation.This paper selects two different non-information priors and analyzes the posterior distribution corresponding to the priors.Using Gibbs sampling to match the two priors to the posterior samples,the parameters in the model are estimated by Bayes,and the point estimates and interval estimates of the parameters are obtained.In the numerical simulation,the results obtained by two different priors are analyzed and compared.Through the analysis of real data,it is verified that the fitting condition of model estimation using Bayesian method is good.Finally,accelerated life is an effective tool for evaluating long-life,high-cost products.In this paper,a constant-stress accelerated life test scheme is designed for the established competitive failure model based on inverse Gaussian process.The maximum likelihood estimation for the competitive failure model with accelerated life is carried out,and the estimation is based on the interval of the asymptotic normality construction parameters.The reliability of the product under normal stress is derived from the parameter estimation results of the product under accelerated stress.The results of numerical simulation verify the feasibility of the scheme.