DEPENDENT PARAMETERS DEGRADATION RELIABILITY ASSESSMENT BASED ON JEFFREYS NONINFORMATIVE PRIOR PARAMETERS

YIN ZeKai; GUO Yu

doi:10.16579/j.issn.1001.9669.2024.01.033

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DEPENDENT PARAMETERS DEGRADATION RELIABILITY ASSESSMENT BASED ON JEFFREYS NONINFORMATIVE PRIOR PARAMETERS

更新时间：2024-04-18

- DEPENDENT PARAMETERS DEGRADATION RELIABILITY ASSESSMENT BASED ON JEFFREYS NONINFORMATIVE PRIOR PARAMETERS
- Journal of Mechanical Strength Vol. 46, Issue 1, Pages: 249-254(2024)
- 作者机构：
  
  1. 江苏徐工国重实验室科技有限公司可靠性技术研究所
  2. 国防科技大学空天科学学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2024.01.033
  CLC： O213.2;TB114.3
- Published：15 February 2024，
- 稿件说明：
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YIN ZeKai, GUO Yu. DEPENDENT PARAMETERS DEGRADATION RELIABILITY ASSESSMENT BASED ON JEFFREYS NONINFORMATIVE PRIOR PARAMETERS. [J]. Journal of Mechanical Strength 46(1):249-254(2024)
DOI：

YIN ZeKai, GUO Yu. DEPENDENT PARAMETERS DEGRADATION RELIABILITY ASSESSMENT BASED ON JEFFREYS NONINFORMATIVE PRIOR PARAMETERS. [J]. Journal of Mechanical Strength 46(1):249-254(2024) DOI： 10.16579/j.issn.1001.9669.2024.01.033.

摘要

通过Jeffreys 无信息先验分布描述了Gamma退化过程中参数的相关性，由贝叶斯模型得到各参数满条件分布，使用马尔科夫链蒙特卡洛（Markov Chain Monte Carlo，MCMC）方法得到参数后验期望估计，最后给出可靠度评价模型。工程实例表明，所得可靠性评估较独立情形更为保守，能够更早地给出产品修理建议。同时，仿真表明，可靠度要求越高，相关与独立情形寿命估计结果偏差越大，0.999 9可靠度下偏差率最大可达9.26%。

Abstract

The correlation between the parameters of the Gamma degradation process was described by the Jeffreys uninformative prior distribution. And the Bayesian model was used to obtain the full conditional distribution of each parameter. The MCMC method was used to get parameter posterior expectation estimates. Finally

reliability was calculated according to the engineering examples and 100 simulations

the obtained reliability assessment was more conservative than the independent case in engineering practice. Thus

the product repair suggestion could be given earlier. And the higher the reliability requirement was .the oreater the deviation between the estimation results of the correlated case and the independent case was

and the life estimation error rate under the reliability of 0. 999 9 was up to 9.26%.

关键词

Gamma过程参数相关Jeffreys无信息先验马尔科夫链蒙特卡洛方法

Keywords

Gamma processDependent parametersJeffreys uninformative prior distributionMarkov chain Monte Carlo

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