Reliability Modeling for Systems with Multiple Degradation Processes Using Inverse Gaussian Process and Copulas

被引:62
|
作者
Liu, Zhenyu [1 ,2 ]
Ma, Xiaobing [1 ]
Yang, Jun [1 ]
Zhao, Yu [1 ]
机构
[1] Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China
[2] Beijing Inst Space Long March Vehicle, Beijing 100076, Peoples R China
基金
中国国家自然科学基金;
关键词
WIENER-PROCESSES; PRODUCTS;
D O I
10.1155/2014/829597
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We develop a reliability model for systems with s-dependent degradation processes using copulas. The proposed model accommodates assumptions of s-dependence among degradation processes and allows for different marginal distributions. This flexibility makes the model more attractive compared with the multivariate distribution model, which lay on the limitation of the homogeneous marginal distribution and can only describe linear correlation. Marginal degradation process is modeled by the inverse Gaussian (IG) process with time scale transformation. Furthermore, we incorporate random drift to account for the possible heterogeneity in population. This paper also develops the statistical inference method using EM algorithm with two-stage procedure. The comparison results of the reliability estimation under both s-dependent and s-independent assumptions are illustrated in the illustrative example to demonstrate the applicability of the proposed method.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] An Optimal Condition Based Maintenance Strategy Using Inverse-Gaussian Degradation Process
    Li, Renqing
    Liu, Xiaoxi
    Song, Yan
    Cai, Zigang
    Li, Jin
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON RELIABILITY SYSTEMS ENGINEERING (ICRSE 2017), 2017,
  • [42] Inverse Gaussian process model with frailty term in reliability analysis
    Morita, Lia H. M.
    Tomazella, Vera L.
    Balakrishnan, Narayanaswamy
    Ramos, Pedro L.
    Ferreira, Paulo H.
    Louzada, Francisco
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2021, 37 (02) : 763 - 784
  • [43] Objective Bayesian analysis for accelerated degradation data using inverse Gaussian process models
    He, Lei
    Sun, Dongchu
    He, Daojiang
    STATISTICS AND ITS INTERFACE, 2019, 12 (02) : 295 - 307
  • [44] Fuzzy Reliability Assessment of Systems With Multiple-Dependent Competing Degradation Processes
    Lin, Yan-Hui
    Li, Yan-Fu
    Zio, Enrico
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2015, 23 (05) : 1428 - 1438
  • [45] Autoregressive inverse Gaussian process and the stochastic volatility modeling
    Sujith, P.
    Balakrishna, N.
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2023, 52 (10) : 3574 - 3580
  • [46] Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process
    Liu, Di
    Wang, Shaoping
    Zhang, Chao
    Tomovic, Mileta
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2018, 180 : 25 - 38
  • [47] Inverse Gaussian process models for degradation analysis: A Bayesian perspective
    Peng, Weiwen
    Li, Yan-Feng
    Yang, Yuan-Jian
    Huang, Hong-Zhong
    Zuo, Ming J.
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2014, 130 : 175 - 189
  • [48] Reliability modeling for degradation-shock dependence systems with multiple species of shocks
    Gao, Hongda
    Cui, Lirong
    Qiu, Qingan
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2019, 185 : 133 - 143
  • [49] Surrogate Modeling with Gaussian Processes for an Inverse Problem in Polymer Dynamics
    Chouhan, Pankaj
    Shanbhag, Sachin
    INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS, 2023, 20 (08)
  • [50] Inverse Gaussian Processes With Random Effects and Explanatory Variables for Degradation Data
    Peng, Chien-Yu
    TECHNOMETRICS, 2015, 57 (01) : 100 - 111