Bayesian methods for preventive maintenance
Time period: 2015-05-05 to 2017-04-01
Funder: Swedish Energy Agency
Type of award: Project grant
Total fundning: 162 667 SEK
Maintenance of wind farms can be systematically categorized as preventive, corrective, and condition based maintenance. Most of the major the O&M costs are dominated by corrective maintenance. To reduce the O&M costs it is necessary to lower the amount of corrective maintenance uncertainty. This can be achieved by improving the prediction of corrective maintenance while introducing more condition based maintenance. To minimise the O&M costs for both corrective and condition based maintenance, the conceptual basis for predictive O&M should be based on a solid numerical and statistical approach. A monte carlo based Bayesian approach and application of risk analysis tools which have proven in other industries will be developed for application to wind energy to assimilate data to improve corrective based maintenance.