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Open AccessArticle

Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades

Department of Civil Engineering, Aalborg University, Thomas Manns vej 23, DK-9220 Aalborg East, Denmark
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Academic Editor: Sukanta Basu
Energies 2017, 10(5), 664; https://doi.org/10.3390/en10050664
Received: 20 February 2017 / Revised: 4 May 2017 / Accepted: 8 May 2017 / Published: 10 May 2017
(This article belongs to the Special Issue Wind Turbine 2017)
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. View Full-Text
Keywords: remaining useful life; wind turbine blades; hidden Markov model; dynamic Bayesian networks remaining useful life; wind turbine blades; hidden Markov model; dynamic Bayesian networks
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Nielsen, J.S.; Sørensen, J.D. Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades. Energies 2017, 10, 664.

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