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Modeling of Age-Dependent Failure Tendency from Incomplete Data

  • P. Hagmark
  • J. LaitinenEmail author
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

This paper addresses modeling of age-dependent failure rates from incomplete data that includes interval-censored failure ages. Two estimators for cumulative failure rates are presented: a simple non-parametric estimator and a maximum-likelihood method based on the gamma distribution and the non-homogeneous Poisson process. The maximum-likelihood fit of familiar parametric models (e.g., the power law) to the available field data from an aircraft component was far from satisfactory, so a special three-parameter model function had to be worked out. The maximum-likelihood estimate obtained is then used for repeated random generation of different data sets akin to the field data. This way the effect of data set size, censoring rate, and randomness on the non-parametric estimate can be analyzed to get practical appraisals.

Keywords

Failure Data Failure Tendency Service Sojourn Bouquet Format Cumulative Failure Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Ross SM (1996) Stochastic Processes, 2nd edn. Wiley, USAzbMATHGoogle Scholar
  2. 2.
    Crow LH, Basu AP (1988) Reliability growth estimation with missing data II. In: Annual proceedings of reliability and maintainability symposium. 1988 Jan.; Los Angeles, USA. pp 248–253Google Scholar
  3. 3.
    Antoch J, Jaruskova D (2007) Testing a homogeneity of stochastic processes. Kybernetica 43(4):415–430MathSciNetzbMATHGoogle Scholar
  4. 4.
    Pasupathy R (2011) Generating non-homogeneous poisson processes. Wiley encyclopedia of operations research and management science (in press)Google Scholar
  5. 5.
    Arkin BL, Leemis LM (2000) Nonparametric estimation of the cumulative intensity function from a non-homogeneous Poisson process from overlapping realizations. Manage Sci 46(7):989–998CrossRefzbMATHGoogle Scholar
  6. 6.
    Taghipour S, Banjevic D (2011) Trend analysis of the power law process using expectation-maximization algorithm for data censored by inspection intervals. Reliab Eng Syst Saf 96(10):1340–1348CrossRefGoogle Scholar
  7. 7.
    Guo H, Watson S, Tavner P, Xiang J (2009) Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation. Reliab Eng Syst Saf 94(6):1057–1063CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Department of Mechanics and DesignTampere University of TechnologyTampereFinland

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