A novel accelerated degradation test design considering stress optimization

Constant stress accelerated degradation tests (CSADT) are widely used in life perdition for highly reliable products to infer the lifetime distribution under operating conditions. Optimal design of an CSADT can improve life prediction accuracy and reduce test costs significantly. In the literature of CSADT design, most approaches focus on how to determine the sample allocation scheme, inspection frequency and test duration, but the issue of how to optimize the stress levels is seldom considered. In this work, we propose a novel method to optimize the CSADT considering both stress levels selection and samples allocation. First, an accelerated degradation model based on the Wiener process is used to model the degradation data. Next, under the constraint of sample size, a local-search based iterative algorithm is proposed to optimize parameters including stress levels and sample number under each level so as to obtain an accurate estimate of the distribution statistics. Finally, a case study of lithium-ion batteries is presented to validate the proposed method.