Bivariate Constant-Stress Accelerated Degradation Model and Inference Based on the Inverse Gaussian

• 作 者: DUAN Fengjun WANG Guanjun
• 机 构: School of Economics, Nanjing University of Finance and Economics, Nanjing 210023, China;School of Ma
• 出 处: 上海交通大学学报（英文版）
• 发表日期: 2018年6期
• 关键词: Bayesian MCMC method inverse Gaussian process bivariate constant-stress accelera

Mod highly reliable products may have two or more quality characteristics (QCs) because of their complex structures and abundant functions.Relations between the QCs should be considered when assessing the reliability of these products.This paper conducts a Bayesian analysis for a bivariate constant-stress accelerated degradation model based on the inverse Gaussian (IG) process.We assume that the product considered has two QCs and each of the QCs is goved by an IG process.The relationship between the QCs is described by a Frank copula function.We also assume that the stress on the products affects not only the parameters of the IG processes,but also the parameter of the Frank copula function.The Bayesian MCMC method is developed to calculate the maximum likelihood estimators (MLE) of the model parameters.The reliability function and the mean-time-to-failure (MTTF) are estimated through the calculation of the posterior samples.Finally,a simulation example is presented to illustrate the proposed bivariate constant-stress accelerated degradation model.