Abstract. In order to identify holistically better drivetrain concepts for onshore application, their operational behaviour needs to be considered at an early design phase. In this paper, a validated approach for estimating drivetrain concept-specific risk of unplanned maintenance based on open access data is presented. Uncertain influencing factors are described with distribution functions. This way, the poor data availability in the early design phase can be used to give an indication about the concept’s choice influence on the unplanned operational turbine behaviour. In order to get representative comparisons, Monte Carlo method is applied. This makes it possible to model the life of a fictional wind turbine based on the derived distributions. Technical availability and drivetrain influenced unplanned maintenance effort are defined as evaluation criteria. The latter is constituted by labour, material, and equipment expenses. By calculating the range of fluctuation of the evaluation criteria mean values, this approach offers an indication about the inherent risk in the operational phase induced by the drivetrain concept choice. This approach shows that open access data or expert estimations are sufficient for comparing different drivetrain concepts over the operational phase in an early design stage. The approach is applied on the five most common state-of-the-art drivetrain concepts. The comparison shows that the drivetrain concept without a gearbox and with a permanent magnet synchronous generator performs the best in terms of absolute unplanned maintenance effort over the lifetime as well as on the inherent risk. For future research, the influence of the maintenance strategy as well as site and park specific impacts on the unplanned concept behaviour should be included. For adapting this method to new concepts, a physically based approach could be developed which would make it possible to estimate probability distributions for the uncertain factors. Nevertheless, this approach will help to identify holistically better drivetrain concepts by being able to estimate the inherent risks in the operational phase.