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A Novel Model Validation Method Based on Area Metric Disagreement between Accelerated Storage Distributions and Natural Storage Data

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  • Bin Suo

    (School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China)

  • Yang Qi

    (School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China)

  • Kai Sun

    (Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China)

  • Jingyuan Xu

    (Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China)

Abstract

It has been a challenge to quantify the credibility of the accelerated storage model until now. This paper introduces a quantitative measurement named the CMADT (Creditability Metric of Accelerated Degradation Test), which quantifies the credibility of the accelerated aging model based on available data. The relevant criterion data are obtained from the natural storage test. CMADT is a credibility metric obtained by measuring the difference in the metric area between the probability distribution of the accelerated storage model and its criterion data. In addition, the accelerated aging model might include multiple parameters resulting in several single-parameter CMADTs. This paper proposes a method that integrates several single-parameter CMADT metrics into a single metric to assess the overall credibility of the accelerated storage model. Moreover, CMADT is universal for different scales of sample data. The cases addressed in this paper show that CMADT helps designers and decision-makers judge the credibility of the result obtained by the accelerated storage model intuitively and makes it easier to compare various products horizontally.

Suggested Citation

  • Bin Suo & Yang Qi & Kai Sun & Jingyuan Xu, 2023. "A Novel Model Validation Method Based on Area Metric Disagreement between Accelerated Storage Distributions and Natural Storage Data," Mathematics, MDPI, vol. 11(11), pages 1-18, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2511-:d:1159394
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    References listed on IDEAS

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    1. Scott Ferson & William L. Oberkampf, 2009. "Validation of imprecise probability models," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 3(1/2/3), pages 3-22.
    2. Li, Wei & Chen, Wei & Jiang, Zhen & Lu, Zhenzhou & Liu, Yu, 2014. "New validation metrics for models with multiple correlated responses," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 1-11.
    3. Luo, Wei & Zhang, Chun-hua & Chen, Xun & Tan, Yuan-yuan, 2015. "Accelerated reliability demonstration under competing failure modes," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 75-84.
    4. Cai, Xia & Tian, Yubin & Ning, Wei, 2019. "Change-point analysis of the failure mechanisms based on accelerated life tests," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 515-522.
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