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Uncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension

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  • Wu, Ji-Peng
  • Kang, Rui
  • Li, Xiao-Yang

Abstract

Accelerated degradation testing (ADT) has been widely used for the reliability and lifetime evaluations of highly reliable products. Generally speaking, the obtained ADT data are composed of two parts - the deterministic degradation trend and uncertainties. When only limited ADT data can be obtained, there will be a lack of knowledge on recognizing the population and lead to epistemic uncertainties. However, current accelerated degradation models fail to clearly distinguish and properly quantify the epistemic uncertainties in time and unit dimensions. Motivated by this problem, this paper constructs a new uncertain accelerated degradation model based on uncertainty theory and belief reliability theory, and presents the uncertain statistical method for parameter estimations. An application case is used to illustrate the proposed methodology and conduct discussions for the deterministic degradation trend and uncertainty analyses of the proposed methodology and the analyses of the proposed methodology to the data sizes in time and unit dimensions. Results show that under limited data sizes in time and unit dimensions, the proposed model can clearly distinguish and properly quantify the epistemic uncertainties in time and unit dimensions and is more suitable for the ADT modeling and analysis than the other two widely used accelerated degradation models.

Suggested Citation

  • Wu, Ji-Peng & Kang, Rui & Li, Xiao-Yang, 2020. "Uncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:reensy:v:201:y:2020:i:c:s0951832019313924
    DOI: 10.1016/j.ress.2020.106967
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    3. Pang, Zhenan & Si, Xiaosheng & Hu, Changhua & Du, Dangbo & Pei, Hong, 2021. "A Bayesian Inference for Remaining Useful Life Estimation by Fusing Accelerated Degradation Data and Condition Monitoring Data," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    4. Li, Xiao-Yang & Chen, Da-Yu & Wu, Ji-Peng & Kang, Rui, 2022. "3-Dimensional general ADT modeling and analysis: Considering epistemic uncertainties in unit, time and stress dimension," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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    6. Chen, Wen-Bin & Li, Xiao-Yang & Kang, Rui, 2022. "Integration for degradation analysis with multi-source ADT datasets considering dataset discrepancies and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Chen, Zhen & Li, Yaping & Zhou, Di & Xia, Tangbin & Pan, Ershun, 2021. "Two-phase degradation data analysis with change-point detection based on Gaussian process degradation model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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