Step-Stress Accelerated Life Testing (SSALT) is a special type of experiment that tests a product’s lifetime with time-varying stress levels. Typical testing protocols deployed in SSALTs cannot implement complete randomization of experiments; instead, they often result in grouped structures of experimental units and, thus, correlated observations. In this article, we propose a Generalized Linear Mixed Model (GLMM) approach to take into account the random group effect in SSALT. Failure times are assumed to be exponentially distributed under any stress level. Two parameter estimation methods, Adaptive Gaussian Quadrature (AGQ) and Integrated Nested Laplace Approximation (INLA), are introduced. A simulation study is conducted to compare the proposed random effect modelwith the traditional model, which pools data groups together, and with the fixed effect model.We also compareAGQand INLA with different priors for parameter estimation. Results show that the proposed model can validate the existence of group-to-group variation. Lastly, the GLMM model is applied to a real data and it shows that disregarding experimental protocols in SSALT may result in large bias in the estimation of the effect of stress variable.
- Adaptive Gaussian quadrature
- Constrained randomization
- Generalized linear mixed model
- Integrated nested Laplace approximation
- Step-stress accelerated life test
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering