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.
| Original language | English |
|---|---|
| Article number | 664 |
| Journal | Energies |
| Volume | 10 |
| Issue number | 5 |
| Number of pages | 13 |
| ISSN | 1996-1073 |
| DOIs | |
| Publication status | Published - 2017 |
Keywords
- Remaining useful life
- Wind turbine blades
- Hidden Markov model
- Dynamic Bayesian networks