TY - JOUR
T1 - An adaptive two-stage Bayesian model averaging approach to planning and analyzing accelerated life tests under model uncertainty
AU - Zhao, Xiujie
AU - Pan, Rong
AU - del Castillo, Enrique
AU - Xie, Min
N1 - Funding Information:
We are grateful to the editor and two referees for their insightful comments to earlier versions of the article. Xie and Zhao were supported in part by the Research Grants Council of Hong Kong under Grant T32-101/15-R and CityU 11203815, and in part by the National Natural Science Foundation of China (71532008). Pan was partially supported by National Science Foundation CMMI 1726445.
Funding Information:
Xie and Zhao were supported in part by the Research Grants Council of Hong Kong under Grant T32-101/15-R and CityU 11203815, and in part by the National Natural Science Foundation of China (71532008). Pan was partially supported by National Science Foundation CMMI 1726445.
Publisher Copyright:
© 2019 American Society for Quality
PY - 2019
Y1 - 2019
N2 - Accelerated life testing (ALT) is commonly used to predict the lifetime of a product at its use stress by subjecting test units to elevated stress conditions that accelerate the occurrence of failures. For new products, the selection of an acceleration model for planning optimal ALT plans is challenging due to the absence of historical lifetime data. The misspecification of an ALT model can lead to considerable errors when it is used to predict the product’s life quantiles. This article proposes a two-stage Bayesian approach to constructing ALT plans and predicting lifetime quantiles. At the first stage, the ALT plan is optimized based on the prior information of candidate models under a modified V-optimality criterion that incorporates both asymptotic prediction variance and squared bias. A Bayesian model averaging (BMA) framework is used to derive the posterior model and the posterior distribution for the life quantile of interest under use stress. If the obtained test data cannot provide satisfactory model selection results, an adaptive second-stage test is conducted based on the posterior information from the first stage. A revisited numerical example demonstrates the efficiency and robustness of the resulting Bayesian ALT plans by comparing with the plans derived from previous methods.
AB - Accelerated life testing (ALT) is commonly used to predict the lifetime of a product at its use stress by subjecting test units to elevated stress conditions that accelerate the occurrence of failures. For new products, the selection of an acceleration model for planning optimal ALT plans is challenging due to the absence of historical lifetime data. The misspecification of an ALT model can lead to considerable errors when it is used to predict the product’s life quantiles. This article proposes a two-stage Bayesian approach to constructing ALT plans and predicting lifetime quantiles. At the first stage, the ALT plan is optimized based on the prior information of candidate models under a modified V-optimality criterion that incorporates both asymptotic prediction variance and squared bias. A Bayesian model averaging (BMA) framework is used to derive the posterior model and the posterior distribution for the life quantile of interest under use stress. If the obtained test data cannot provide satisfactory model selection results, an adaptive second-stage test is conducted based on the posterior information from the first stage. A revisited numerical example demonstrates the efficiency and robustness of the resulting Bayesian ALT plans by comparing with the plans derived from previous methods.
KW - Design of experiments
KW - Lognormal distribution
KW - Reliability assessment
KW - Robust design
KW - Weibull distribution
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U2 - 10.1080/00224065.2019.1571333
DO - 10.1080/00224065.2019.1571333
M3 - Article
AN - SCOPUS:85065142607
SN - 0022-4065
VL - 51
SP - 181
EP - 197
JO - Journal of Quality Technology
JF - Journal of Quality Technology
IS - 2
ER -