Sammendrag
In the overall decision problem regarding optimization of operation and maintenance (O&M) for
offshore wind farms, there are many approaches for solving parts of the overall decision problem. Simulationbased
strategy models accurately capture system effects related to logistics, but model condition-based maintenance
(CBM) in a simplified manner. The influence of the CBM strategy on the failure rate can be directly
considered using a risk-based approach, but here logistics is modelled in a simplified manner. This paper presents
an efficient approach for accurate integration of CBM in simulation-based strategy models. Using Bayesian
networks, the probability distribution for the time of failure and the conditional probability distribution for
the time of CBM given the time of failure is estimated accounting for the CBM strategy, and are used by the
simulation-based strategy model to generate failures and CBM tasks. An example considering CBM for wind
turbine blades demonstrates the feasibility of the approach
offshore wind farms, there are many approaches for solving parts of the overall decision problem. Simulationbased
strategy models accurately capture system effects related to logistics, but model condition-based maintenance
(CBM) in a simplified manner. The influence of the CBM strategy on the failure rate can be directly
considered using a risk-based approach, but here logistics is modelled in a simplified manner. This paper presents
an efficient approach for accurate integration of CBM in simulation-based strategy models. Using Bayesian
networks, the probability distribution for the time of failure and the conditional probability distribution for
the time of CBM given the time of failure is estimated accounting for the CBM strategy, and are used by the
simulation-based strategy model to generate failures and CBM tasks. An example considering CBM for wind
turbine blades demonstrates the feasibility of the approach