Semiparametric Models for Accelerated Destructive Degradation Test Data Analysis

Abstract

ABSTRACTAccelerated destructive degradation tests (ADDT) are widely used in industry to evaluate materials’ long-term properties. Even though there has been tremendous statistical research in nonparametric methods, the current industrial practice is still to use application-specific parametric models to describe ADDT data. The challenge of using a nonparametric approach comes from the need to retain the physical meaning of degradation mechanisms and also perform extrapolation for predictions at the use condition. Motivated by this challenge, we propose a semiparametric model to describe ADDT data. We use monotonic B-splines to model the degradation path, which not only provides flexible models with few assumptions, but also retains the physical meaning of degradation mechanisms (e.g., the degradation path is monotonic). Parametric models, such as the Arrhenius model, are used for modeling the relationship between the degradation and the accelerating variable, allowing for extrapolation to the use condition. We develop an efficient procedure to estimate model parameters. We also use simulations to validate the developed procedures and demonstrate the robustness of the semiparametric model under model misspecification. Finally, the proposed method is illustrated by multiple industrial applications. This article has online supplementary materials.