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基于随机参数逆高斯过程的加速退化建模方法

王浩伟 滕克难 奚文骏

王浩伟, 滕克难, 奚文骏等 . 基于随机参数逆高斯过程的加速退化建模方法[J]. 北京航空航天大学学报, 2016, 42(9): 1843-1850. doi: 10.13700/j.bh.1001-5965.2015.0542
引用本文: 王浩伟, 滕克难, 奚文骏等 . 基于随机参数逆高斯过程的加速退化建模方法[J]. 北京航空航天大学学报, 2016, 42(9): 1843-1850. doi: 10.13700/j.bh.1001-5965.2015.0542
WANG Haowei, TENG Kenan, XI Wenjunet al. Accelerated degradation modeling method based on Inverse Gaussian processes with random parameters[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(9): 1843-1850. doi: 10.13700/j.bh.1001-5965.2015.0542(in Chinese)
Citation: WANG Haowei, TENG Kenan, XI Wenjunet al. Accelerated degradation modeling method based on Inverse Gaussian processes with random parameters[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(9): 1843-1850. doi: 10.13700/j.bh.1001-5965.2015.0542(in Chinese)

基于随机参数逆高斯过程的加速退化建模方法

doi: 10.13700/j.bh.1001-5965.2015.0542
基金项目: 国家自然科学基金(5165487)
详细信息
    作者简介:

    王浩伟,男,博士,讲师。主要研究方向:加速试验、导弹延寿技术。Tel.:0535-6635477,E-mail:wyg2010123@126.com;滕克难,男,博士,教授,博士生导师。主要研究方向:装备综合保障、导弹延寿技术。Tel.:0535-6635101,E-mail:tkn001@126.com

    通讯作者:

    滕克难,Tel.:0535-6635101,E-mail:tkn001@126.com

  • 中图分类号: V216.5;TB114.3

Accelerated degradation modeling method based on Inverse Gaussian processes with random parameters

Funds: National Natural Science Foundation of China (51605487)
  • 摘要: 为了将随机参数退化模型应用于加速退化试验以提高可靠性评估结果的准确性,本文以逆高斯过程为例研究了基于随机参数退化模型的加速退化建模方法。利用加速系数不变原则推导出逆高斯过程各参数在不同应力下应满足的关系式,由此建立参数的加速模型,计算出加速系数,进而将加速应力下的退化数据等效折算到工作应力下。采用了随机参数的共轭先验分布,并且利用最大期望算法估计出随机参数的超参数值。仿真试验验证了所提方法的可行性和有效性,实例应用说明了所提方法具有较好的工程应用价值。

     

  • [1] WANG X.Wiener processes with random effects for degradation data[J].Journal of Multivariate Analysis,2010,101(2):340-351.
    [2] SI X S,WANG W B,HU C H,et al.A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation[J].Mechanical Systems and Signal Processing,2013,35(1-2):219-237.
    [3] 刘君强,谢吉伟,左洪福,等.基于随机Wiener过程的航空发动机剩余寿命预测[J].航空学报,2015,36(2):564-574.LIU J Q,XIE Z W,ZUO H F,et al.Residual lifetime prediction for aeroengines based on Wiener process with random effects[J].Acta Aeronautica et Astronautica Sinica,2015,36(2):564-574(in Chinese).
    [4] LAWLESS J,CROWDER M.Covariates and random effects in a Gamma process model with application to degradation and failure[J].Lifetime Data Analysis, 2004,10(3):213-227.
    [5] WANG H W,XU T X,MI Q L.Lifetime prediction based on Gamma processes from accelerated degradation data[J].Chinese Journal of Aeronautics,2015,28(1):172-179.
    [6] TSAI C C,TSENG S T,BALAKRISHNAN N.Optimal design for degradation tests based on Gamma processes with random effects[J].IEEE Transactions on Reliability,2012,61(2):604-613.
    [7] WANG X,XU D.An inverse Gaussian process model for degradation data[J].Technometrics,2010,52(2):188-197.
    [8] PENG W W,LI Y F,YANG Y J,et al.Inverse Gaussian process models for degradation analysis:A Bayesian perspective[J].Reliability Engineering and System Safety,2014,130:175-189.
    [9] YE Z S,CHEN N.The inverse Gaussian process as degradation model[J].Technometrics,2014,56(3):302-311.
    [10] PENG C Y.Inverse Gaussian processes with random effects and explanatory variables for degradation data[J].Technometrics,2015,57(1):100-111.
    [11] PARK C,PADGETT W J.Accelerated degradation models for failure based on geometric Brownian motion and Gamma processes[J].Lifetime Data Analysis,2005,11(4):511-527.
    [12] YE Z S,CHEN L P,TANG L C,et al.Accelerated degradation test planning using the Inverse Gaussian process[J].IEEE Transactions on Reliability,2014,63(3):750-763.
    [13] 王浩伟,徐廷学,赵建忠.融合加速退化和现场实测退化数据的剩余寿命预测方法[J].航空学报,2014,35(12):3350-3357.WANG H W,XU T X,ZHAO J Z.Residual life prediction method fusing accelerated degradation and field degradation data[J].Acta Aeronautica et Astronautica Sinica,2014,35(12):3350-3357(in Chinese).
    [14] LING M H,TSUI K L,BALAKRISHNAN N.Accelerated degradation analysis for the quality of a system based on the Gamma process[J].IEEE Transactions on Reliability,2015,64(1):463-472.
    [15] 周源泉,翁朝曦,叶喜涛.论加速系数与失效机理不变的条件(Ⅰ)-寿命型随机变量的情况[J].系统工程与电子技术,1996,18(1):55-67.ZHOU Y Q,WENG Z X,YE X T.Study on accelerated factor and condition for constant failure mechanism (Ⅰ)-The case for lifetime is a random variable[J].System Engineering and Electronics,1996,18(1):55-67(in Chinese).
    [16] 王浩伟,徐廷学,王伟亚.基于退化模型的失效机理一致性检验方法[J].航空学报,2015,36(3):889-897.WANG H W,XU T X,WANG W Y.Test method of failure mechanism consistency based on degradation model[J].Acta Aeronautica et Astronautica Sinica,2015,36(3):889-897(in Chinese).
    [17] BALAKRISHNAN N,LING M H.EM algorithm for one-shot device testing under the exponential distribution[J].Computational Statistics & Data Analysis,2012,56(3):502-509.
    [18] 韩立岩,蔡明生,尹力博.正态逼近与基于覆盖宽度的EM估计[J].北京航空航天大学学报,2013,39(5):654-659.HAN L Y,CAI M S,YIN L B.Approximation by normal distribution with covering width based on EM estimation[J].Journal of Beijing University of Aeronautics and Astronautics,2013,39(5):654-659(in Chinese).
    [19] 徐廷学,王浩伟,张鑫.EM算法在Wiener过程随机参数的超参数值估计中的应用[J].系统工程与电子技术,2015,37(3):707-712.XU T X,WANG H W,ZHANG X.Application of EM algorithm to estimate hyper parameters of the random parameters of Wiener processes[J].Journal of Systems Engineering and Electronics,2015,37(3):707-712(in Chinese).
    [20] MEEKER W Q,ESCOBAR A.Statistical methods for reliability data[M].New York:John Wiley & Sons,1998:630-640.
    [21] EFRON B.Better bootstrap confidence intervals[J].Journal of American Statistical Association,1987,82(397):171-185.
    [22] MARKS C E,GLEN A G,ROBINSON M W,et al.Applying bootstrap methods to system reliability[J].The American Statistician,2014,68(3):174-180.
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出版历程
  • 收稿日期:  2015-08-24
  • 网络出版日期:  2016-09-20

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