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Residual-life estimation for components with non-symmetric priors

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  • Santanu Chakraborty
  • Nagi Gebraeel
  • Mark Lawley
  • Hong Wan

Abstract

Condition monitoring uses sensory signals to assess the health of engineering systems. A degradation model is a mathematical characterization of the evolution of a condition signal. Our recent research focuses on using degradation models to compute residual-life distributions for degrading components. Residual-life distributions are important for providing probabilistic estimates of failure time for use in maintenance planning and spare parts inventory management. To obtain residual-life distributions, our earlier work assumed the degradation model's stochastic parameters to be normally distributed. This paper investigates the performance of these residual-life distributions when the underlying normality assumptions are not satisfied. The paper also develops methods for estimating residual-life when the stochastic parameters of the degradation model follow more general distributions.

Suggested Citation

  • Santanu Chakraborty & Nagi Gebraeel & Mark Lawley & Hong Wan, 2009. "Residual-life estimation for components with non-symmetric priors," IISE Transactions, Taylor & Francis Journals, vol. 41(4), pages 372-387.
    • Handle: RePEc:taf:uiiexx:v:41:y:2009:i:4:p:372-387
    DOI: 10.1080/07408170802369409
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    Cited by:

    1. Silvia Rodríguez-Narciso & J. Andrés Christen, 2016. "Optimal sequential Bayesian analysis for degradation tests," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 405-428, July.
    2. Peng, Weiwen & Li, Yan-Feng & Yang, Yuan-Jian & Huang, Hong-Zhong & Zuo, Ming J., 2014. "Inverse Gaussian process models for degradation analysis: A Bayesian perspective," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 175-189.
    3. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.

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