• DocumentCode
    3309491
  • Title

    Ensemble of bootstrapped models for the prediction of the remaining useful life of a creeping turbine blade

  • Author

    Baraldi, Piero ; Mangili, F. ; Zio, Enrico

  • Author_Institution
    Dipt. di Energia, Politec. di Milano, Milan, Italy
  • fYear
    2012
  • fDate
    18-21 June 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    The problem of predicting the Remaining Useful Life (RUL) of a creeping turbine blade is considered in this work. We assume to have a sequence of direct measurements of the blade creep strain; failure is declared with respect to a threshold value of maximum creep strain, beyond which the blade cracks. An ensemble of bootstrapped models is developed to predict the RUL and estimate its variance. This latter quantity combines the stochastic uncertainty in the future evolution of the creep strain with the error made by approximating the actual creep growth process with the empirical model. The diverse models of the ensemble are obtained by considering the independent creep strain increments between consecutive measurements instead of the sequence of correlated creep strain measurements. The performance of the ensemble of models is evaluated by considering the accuracy in the prediction of the blade RUL and in the estimation of the corresponding prediction interval. The results point out the importance of generating diverse bootstrapped models in order to quantify the modeling error.
  • Keywords
    approximation theory; blades; creep; failure (mechanical); reliability; statistical analysis; turbines; approximation; blade creep strain; blade failure; bootstrapped model; correlated creep strain measurement; creep growth process; creeping turbine blade; empirical model; independent creep strain increment; prediction interval; remaining useful life prediction; variance estimation; Blades; Creep; Data models; Degradation; Predictive models; Strain; Turbines; bootstrap ensemble; creep; prognostics; turbine blades;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Prognostics and Health Management (PHM), 2012 IEEE Conference on
  • Conference_Location
    Denver, CO
  • Print_ISBN
    978-1-4673-0356-9
  • Type

    conf

  • DOI
    10.1109/ICPHM.2012.6299506
  • Filename
    6299506