Sequential selection for accelerated life testing via approximate Bayesian inference

Abstract

Accelerated life testing (ALT) is typically used to assess the reliability of material's lifetime under desired stress levels. Recent advances in material engineering have made a variety of material settings readily available. A critical question is how to efficiently conduct ALT to optimize reliability performance over different material settings. We propose a sequential selection approach to solve this problem. The proposed approach contains (1) a model updating mechanism to incorporate new experimental data in each step, and (2) a design criterion to guide new experiments that maximizes the potential to find the optimal material setting. To guarantee a tractable statistical mechanism for information collection, we develop explicit model parameter update formulas via approximate Bayesian inference. Theories show that our explicit update formulas give consistent parameter estimates. To guarantee that the design criterion in each step can make improvement on the identification of optimal material setting, this paper adopts an expected improvement-based design criterion for optimizing the material setting under target stress factor levels. We also give a heuristic on this design criterion to justify the statistical consistency of approximate Bayesian estimates. Simulation studies and a case study show that the proposed sequential selection approach can significantly improve the probability of identifying the material alternative with best reliability performance compared to other design approaches.

Study on Life Prediction of Li-MnO2 Battery

“…Wu Yifan conducted voltage, capacity, and other electrical performance tests on lithiumsulfur dioxide galvanic batteries stored for 10 years, and quantitatively analyzed voltage lag and capacity attenuation of batteries [7] . Chen Bing summarized the development of accelerated life test technology in military and civil fields [8] . Xu Kai used phase-locked amplification technology, through the measurement of lithium battery AC resistance, to achieve the prediction of lithium galvanic battery residual power [9] .…”