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Measurement errors in degradation-based burn-in

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  • Zhai, Qingqing
  • Ye, Zhi-Sheng
  • Yang, Jun
  • Zhao, Yu

Abstract

Burn-in is an effective tool to improve product reliability and reduce field failure costs before a product is sold to customers. As many products are becoming highly reliable, traditional burn-in that tests a batch of a product until most weak units fail requires an unaffordable testing duration. If the product failure can be associated with an underlying degradation process and a weak unit degrades faster than a normal one, then degradation-based burn-in can be implemented. Due to such various factors as human errors and limited precision of the measurement device, measurement errors are often inevitable. Ignoring measurement errors in the degradation observations would lead to inferior burn-in decisions. This study uses the Wiener process to model the underlying degradation and considers Gaussian measurement errors in the observations. Two burn-in models with different cost structures are studied and the optimal cutoff level for each model is obtained analytically. The relation between the two models is discussed, leading to a new cost model.

Suggested Citation

  • Zhai, Qingqing & Ye, Zhi-Sheng & Yang, Jun & Zhao, Yu, 2016. "Measurement errors in degradation-based burn-in," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 126-135.
    • Handle: RePEc:eee:reensy:v:150:y:2016:i:c:p:126-135
    DOI: 10.1016/j.ress.2016.01.015
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    References listed on IDEAS

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    7. Cha, Ji Hwan & Finkelstein, Maxim, 2010. "Burn-in by environmental shocks for two ordered subpopulations," European Journal of Operational Research, Elsevier, vol. 206(1), pages 111-117, October.
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    Cited by:

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    2. Pan, Donghui & Wei, Yantao & Fang, Houzhang & Yang, Wenzhi, 2018. "A reliability estimation approach via Wiener degradation model with measurement errors," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 131-141.
    3. Chun-Ho Wang & Chao-Hui Huang & Deng-Guei You, 2022. "Condition-Based Multi-State-System Maintenance Models for Smart Grid System with Stochastic Power Supply and Demand," Sustainability, MDPI, vol. 14(13), pages 1-29, June.
    4. Jinsong Yu & Jie Yang & Diyin Tang & Jing Dai, 2018. "An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine," Energies, MDPI, vol. 11(11), pages 1-19, November.
    5. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2018. "Nonlinear step-stress accelerated degradation modelling considering three sources of variability," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 207-215.
    6. Pan, Donghui & Liu, Jia-Bao & Yang, Wenzhi, 2018. "A new result on lifetime estimation based on skew-Wiener degradation model," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 157-164.
    7. Yan, Bingxin & Ma, Xiaobing & Yang, Li & Wang, Han & Wu, Tianyi, 2020. "A novel degradation-rate-volatility related effect Wiener process model with its extension to accelerated ageing data analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    8. Zhao, Xian & Wang, Siqi & Wang, Xiaoyue & Cai, Kui, 2018. "A multi-state shock model with mutative failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 1-11.
    9. Li, Junxing & Wang, Zhihua & Zhang, Yongbo & Liu, Chengrui & Fu, Huimin, 2018. "A nonlinear Wiener process degradation model with autoregressive errors," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 48-57.
    10. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    11. Chen, Zhen & Pan, Ershun & Xia, Tangbin & Li, Yanting, 2020. "Optimal degradation-based burn-in policy using Tweedie exponential-dispersion process model with measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    12. Dong, Qinglai & Cui, Lirong, 2019. "A study on stochastic degradation process models under different types of failure Thresholds," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 202-212.

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