Model Uncertainty in Accelerated Degradation Testing Analysis

Le Liu, Xiao-Yang Li, Enrico Zio (), Rui Kang and Tong-Min Jiang
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Enrico Zio: LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec

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Abstract: —In accelerated degradation testing (ADT), test data from higher than normal stress conditions are used to find stochas-tic models of degradation, e.g., Wiener process, Gamma process, and inverse Gaussian process models. In general, the selection of the degradation model is made with reference to one specific product and no consideration is given to model uncertainty. In this paper, we address this issue and apply the Bayesian model averaging (BMA) method to constant stress ADT. For illustration, stress relaxation ADT data are analyzed. We also make a simulation study to compare the s-credibility intervals for single model and BMA. The results show that degradation model uncertainty has significant effects on the p-quantile lifetime at the use conditions, especially for extreme quantiles. The BMA can well capture this uncertainty and compute compromise s-credibility intervals with the highest coverage probability at each quantile.

Keywords: Accelerated aging; Bayesian methods; degrada- tion; stochastic processes; uncertainty (search for similar items in EconPapers)
Date: 2017-09
New Economics Papers: this item is included in nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-01652218
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Citations: View citations in EconPapers (9)

Published in IEEE Transactions on Reliability, 2017, 66 (3), pp.603 - 615. ⟨10.1109/TR.2017.2696341⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01652218

DOI: 10.1109/TR.2017.2696341

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