Abstract:

During the step stress accelerated degradation test (SSADT) experiment, the time of changing stress level is usually determined in advance. It may be unreasonable to highly reliable products. The degradation path  is described as a Wiener process. During the experiment, the stress level is changed when the degradation value crosses a pre specified value. Therefore, the time of changing stress level is regarded as a random variable which follows inverse Gaussian distribution. Based on this situation, an SSADT model is proposed. Due to the computational complexity of the model, the Bayesian  Markov chain Monte Carlo (MCMC) method for the parameters is applied to obtain the maximum likelihood estimation. Finally, some simulation examples are presented to validate the proposed model and method.