Abstract
Degradation data for some products tends to present two-phase patterns during testing periods. It is caused by defects or contaminants remaining after manufacturing processes. The change-point of the two-phase degradation path represents the time when a burn-in related phase transits to an inherent degradation phase with a slower and more stable rate. This chapter discusses a hierarchical Bayesian change-point regression model to fit the two-phase degradation patterns. A Gibbs sampling algorithm is developed for the inference of the parameters in the change-point regression model. The results indicate that reliability estimation can be improved substantially by using the change-point model to account for product burn-in. Based on the hierarchical Bayesian change-point degradation model, degradation-based burn-in tests are devised according to a reliability criterion or a cost criterion.
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Bae, S.J., Yuan, T. (2017). Hierarchical Bayesian Change-Point Analysis for Nonlinear Degradation Data. In: Chen, DG., Lio, Y., Ng, H., Tsai, TR. (eds) Statistical Modeling for Degradation Data. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-5194-4_2
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DOI: https://doi.org/10.1007/978-981-10-5194-4_2
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