Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades

Jannie S. Nielsen and John D. Sørensen
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Jannie S. Nielsen: Department of Civil Engineering, Aalborg University, Thomas Manns vej 23, DK-9220 Aalborg East, Denmark
John D. Sørensen: Department of Civil Engineering, Aalborg University, Thomas Manns vej 23, DK-9220 Aalborg East, Denmark

Energies, 2017, vol. 10, issue 5, 1-13

Abstract: 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.

Keywords: remaining useful life; wind turbine blades; hidden Markov model; dynamic Bayesian networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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