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.
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.
Originalsprog | Engelsk |
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Artikelnummer | 664 |
Tidsskrift | Energies |
Vol/bind | 10 |
Udgave nummer | 5 |
Antal sider | 13 |
ISSN | 1996-1073 |
DOI | |
Status | Udgivet - 2017 |
Emneord
- Remaining useful life
- Wind turbine blades
- Hidden Markov model
- Dynamic Bayesian networks