Mis-specification analysis of inverse Gaussian degradation processes model

被引：1
|
作者：
Chen X. [1 ]
Sun X. [1 ]
Ji G. [1 ]
Li Z. [2 ]
机构：
[1] The Rocket Force University of Engineering, Xi'an
[2] Naval Academy, Beijing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2019年 / 41卷 / 03期
关键词：
Inverse Gaussian (IG) process; Mean-time-to-failure (MTTF); Mis-specification; Quasi maximum likelihood estimation (QMLE);
D O I：
10.3969/j.issn.1001-506X.2019.03.32
中图分类号：
学科分类号：
摘要：
The mis-specification effects of stochastic process-based degradation models are rarely studied and mainly focus on linear models. This paper investigates two types of mis-specification in inverse Gaussian (IG) processes, that is, a non-stationary IG process without random effects which is wrongly assumed to be a nonlinear Wiener process without random effects, and an IG process with a random effect which is wrongly assumed to be a simple IG process. In such situations, the distribution characteristics of quasi maximum likelihood estimation (QMLE) of the mean-time-to-failure (MTTF) are derived according to the theory of QMLE asymptotic normality. Through a case study about fatigue-crack-growth data, the effects of the corresponding model's mis-specification on the MTTFs are compared and analyzed. The results also show that the effects of mis-specification become large under some settings of parameters, or combinations of the number of the sample size and measurements, which can be used as references to engineering applications. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：693 / 700
页数：7
相关论文
共 22 条
  • [1] Ye Z.S., Xie M., Stochastic modelling and analysis of degradation for highly reliable products, Applied Stochastic Models in Business and Industry, 31, 1, pp. 16-32, (2015)
  • [2] Wang H.W., Teng K.N., Review of reliability evaluation technology based on accelerated degradation data, Systems Engineering and Electronics, 39, 12, pp. 2877-2885, (2017)
  • [3] Peng C.Y., Inverse Gaussian processes with random effects and explanatory variables for degradation data, Technometrics, 57, 1, pp. 100-111, (2015)
  • [4] Peng B.H., Zhou J.L., Sun Q., Et al., Residual lifetime prediction of products based on fusion of degradation data and lifetime data, Systems Engineering and Electronics, 33, 5, pp. 1073-1078, (2011)
  • [5] Meeker W.Q., Escobar L.A., Statistical Methods for Reliability Data, pp. 317-335, (1998)
  • [6] Shu Y., Feng Q.M., Kao E., Et al., Levy-driven non-Gaussian Ornstein-Uhlenbeck processes for degradation-based reliability analysis, IIE Transactions, 48, 11, pp. 993-1003, (2016)
  • [7] Doksum K.A., Hoyland A., Models for variable-stress accelerated life testing experiments based on Wiener-processes and the inverse Gaussian distribution, Technometrics, 34, 1, pp. 74-82, (1992)
  • [8] Doksum K.A., Normand S.L.T., Gaussian models for degradation processes-part I: Methods for the analysis of biomarker data, Lifetime Data Analysis, 1, 2, pp. 131-144, (1995)
  • [9] Ye Z.S., Chen N., Shen Y., A new class of Wiener process models for degradation analysis, Reliability Engineering and System Safety, 139, pp. 58-67, (2015)
  • [10] Wang X., Wiener processes with random effects for degradation data, Journal of Multivariate Analysis, 101, 2, pp. 340-351, (2010)
123