Bayesian optimal design of sequential stress accelerated degradation testing

Accelerated degradation testing (ADT) is commonly used to obtain degradation data under the hasher than normal usage conditions, and to assess lifetime and reliability for high reliable and long life products in a short time. The optimal design for ADT is to propose the test plan based on the performance degradation process with a reasonable optimal criteria and the constraints of test resources. Both the traditional ADT optimal design and Bayesian ADT optimal design are similar that the historical data is needed to determine the initial values or the prior distributions of the model parameters. However, it may be inaccurate to estimate the parameter values or the prior distributions only according to the historical information, which will result in large deviations between the parameter values or the prior distributions and the actual ones. Therefore, the optimal plan could not meet the statistical accuracy. In this paper, we propose a Bayesian ADT dynamic optimal design method with sequential stress. The degradation information under the most recent stress level will be treated as the prior of the next stress level. Under the assumption of Wiener process, an optimal model with the objective of quadratic loss function is built with the decision variables, e.g. stress levels and inspection times. Finally, the simulation study is introduced to illustrate the proposed method, and the results show that Bayesian ADT optimal design with sequential stress can adjust the test plan dynamically to improve the test efficiency.