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Data analysis of step-stress accelerated life tests with heterogeneous group effects

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  • Kangwon Seo
  • Rong Pan

Abstract

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 model with the traditional model, which pools data groups together, and with the fixed effect model. We also compare AGQ and 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.

Suggested Citation

  • Kangwon Seo & Rong Pan, 2017. "Data analysis of step-stress accelerated life tests with heterogeneous group effects," IISE Transactions, Taylor & Francis Journals, vol. 49(9), pages 885-898, September.
    • Handle: RePEc:taf:uiiexx:v:49:y:2017:i:9:p:885-898
    DOI: 10.1080/24725854.2017.1312038
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    References listed on IDEAS

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    1. Jinsuk Lee & Rong Pan, 2010. "Analyzing step-stress accelerated life testing data using generalized linear models," IISE Transactions, Taylor & Francis Journals, vol. 42(8), pages 589-598.
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    Cited by:

    1. Hassan S. Bakouch & Fernando A. Moala & Shuhrah Alghamdi & Olayan Albalawi, 2024. "Bayesian Methods for Step-Stress Accelerated Test under Gamma Distribution with a Useful Reparametrization and an Industrial Data Application," Mathematics, MDPI, vol. 12(17), pages 1-24, September.
    2. Zhuang, Liangliang & Xu, Ancha & Pang, Jihong, 2021. "Product reliability analysis based on heavily censored interval data with batch effects," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    3. Hao Zeng & Xuxue Sun & Kuo Wang & Yuxin Wen & Wujun Si & Mingyang Li, 2024. "A Bayesian Approach for Lifetime Modeling and Prediction with Multi-Type Group-Shared Missing Covariates," Mathematics, MDPI, vol. 12(5), pages 1-23, February.

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