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Energies 2017, 10(5), 664;

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
Author to whom correspondence should be addressed.
Academic Editor: Sukanta Basu
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)
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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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