Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades

Publikation: Forskning - peer reviewTidsskriftartikel

Abstrakt

To optimally plan maintenance of wind turbine blades, knowledge of the degradation processes and the remaining useful life is essential. In this paper, a method is proposed for calibration of a Markov deterioration model based on past inspection data for a range of blades, and updating of the model for a specific wind turbine blade, whenever information is available from inspections
and/or condition monitoring. Dynamic Bayesian networks are used to obtain probabilities of inspection outcomes for a maximum likelihood estimation of the transition probabilities in the Markov model, and are used again when updating the model for a specific blade using observations. The method is illustrated using indicative data from a database containing data from inspections of wind turbine blades.
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Detaljer

To optimally plan maintenance of wind turbine blades, knowledge of the degradation processes and the remaining useful life is essential. In this paper, a method is proposed for calibration of a Markov deterioration model based on past inspection data for a range of blades, and updating of the model for a specific wind turbine blade, whenever information is available from inspections
and/or condition monitoring. Dynamic Bayesian networks are used to obtain probabilities of inspection outcomes for a maximum likelihood estimation of the transition probabilities in the Markov model, and are used again when updating the model for a specific blade using observations. The method is illustrated using indicative data from a database containing data from inspections of wind turbine blades.
OriginalsprogEngelsk
Artikelnummer664
TidsskriftEnergies
Vol/bind10
Tidsskriftsnummer5
Antal sider13
ISSN1996-1073
DOI
StatusUdgivet - 2017

    Emneord

  • Remaining useful life, Wind turbine blades, Hidden Markov model, Dynamic Bayesian networks
ID: 257329063