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Volume 42 Issue 9
Sep.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(9): 1843-1850.
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)
shu

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

doi: 10.13700/j.bh.1001-5965.2015.0542
  • 1.

    Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, China

  • 2. Department of Command, Naval Aeronautical and Astronautical University, Yantai 264001, China

Funds:  National Natural Science Foundation of China (51605487)
  • Received Date: 24 Aug 2015
  • Publish Date: 20 Sep 2016
    Abstract
  • In order to apply degradation models with random parameters to accelerated degradation tests to improve the accuracy of reliability evaluation, an accelerated degradation modeling method based on degradation models with random parameters was studied with Inverse Gaussian processes taken as examples. First, acceleration coefficient constant principle was used to deduce the relationships that the parameters of Inverse Gaussian process should satisfy under different stresses. Then, the acceleration models of parameters were constructed and acceleration coefficients were computed. So accelerated degradation data was extrapolated from accelerated stress levels to normal stress level. The conjugate prior distributions of random parameters were used and maximization expectation algorithm was utilized to estimate hyper-parameters. Simulation tests validate the feasibility and effectiveness of the proposed method, and a case study demonstrates that the proposed method has good engineering application value.

     

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    • References(22)
  • [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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