Reliability estimation from two types of accelerated testing considering individual difference and measurement error

Accelerated testing is a valuable approach for enhancing testing efficiency and reducing time costs, thus playing a crucial role in reliability testing. This paper introduces a novel reliability evaluation method that effectively utilizes both accelerated degradation testing (ADT) and accelerated life testing (ALT) data simultaneously, aiming to comprehensively leverage the reliability information of products. To address the challenges posed by contaminated degradation data and unit heterogeneity, the Wiener process is used to model the product degradation process, considering individual difference and measurement error. The proposed method utilizes a Bayesian framework with a MCMC algorithm for parameter estimation based on the two types of accelerated testing data. Furthermore, a simulation study shows that the proposed method can effectively improve the accuracy of product reliability estimation compared with other methods. A practical study for rubber O-rings of the torpedo fuse shows that the proposed method is highly effective in solving practical problems.