王平平, 汤银才, 程恭品. 基于变点伽马过程的退化数据分析---贝叶斯角度[J]. 应用概率统计, 2022, 38(5): 674-692. DOI: 10.3969/j.issn.1001-4268.2022.05.004
引用本文: 王平平, 汤银才, 程恭品. 基于变点伽马过程的退化数据分析---贝叶斯角度[J]. 应用概率统计, 2022, 38(5): 674-692. DOI: 10.3969/j.issn.1001-4268.2022.05.004
WANG Pingping, TANG Yincai, CHENG Gongpin. Degradation Data Analysis Based on Change-Point Gamma Process: A Bayesian Perspective[J]. Chinese Journal of Applied Probability and Statistics, 2022, 38(5): 674-692. DOI: 10.3969/j.issn.1001-4268.2022.05.004
Citation: WANG Pingping, TANG Yincai, CHENG Gongpin. Degradation Data Analysis Based on Change-Point Gamma Process: A Bayesian Perspective[J]. Chinese Journal of Applied Probability and Statistics, 2022, 38(5): 674-692. DOI: 10.3969/j.issn.1001-4268.2022.05.004

基于变点伽马过程的退化数据分析---贝叶斯角度

Degradation Data Analysis Based on Change-Point Gamma Process: A Bayesian Perspective

  • 摘要: 随着科技的进步和材料科学的发展,产品的可靠性不断增强, 具有较长的寿命. 因此传统的寿命试验逐渐被退化实验所替代.在退化实验中, 质量特征(QC)随着时间退化, 它可以反应产品的可靠性状态.有些质量特征的退化轨道通常随时间单调递增且非光滑的函数.本文受N通道功率金属氧化物半导体场效应晶体管(MOSFET)的实验退化数据的启发,提出了层次贝叶斯框架下的两阶段伽马过程模型对单调非光滑的退化数据建模.模拟研究验证了三种情形下所提出模型的有用性. 为了证明所提出方法的应用性,分析了一个说明性的抗辐射性能案例数据.

     

    Abstract: As the development of the technology and material science, products become highly reliable with long lifetimes. Consequently, the traditional life tests are gradually replaced by the degradation tests. In the degradation tests, the quality characteristic (QC) which degrades over time, can reflect the reliability status of the product. Some of the degradation paths of the QC are usually monotonically increasing and non-smooth function of time. In this paper, motivated by the degradation data from the tests of N-channel power metal oxide semiconductor field-effect transistor (MOSFET), we propose a two-phase gamma process under hierarchical Bayesian framework to model the monotonic and non-smooth degradation data. A simulation study is performed to verify the utility of the proposed model under three scenarios. An illustrative anti-radiation performance case study is analyzed to show the applicable power of the proposed model.

     

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