Global energy demand continuously increases, paralleled by a growing emphasis on sustainable energy sources. Wind turbines, especially offshore, have become a focal point due to their high energy potential. These systems further evolve in scale and capacity. However, offshore wind turbines have a significant challenge through limited accessibility, significantly complicating maintenance efforts. Although various condition monitoring systems are already in use, many opportunities remain to expand these systems. In particular, the early detection of unwanted events transitioning to manageable events would reduce the downtime, which is of interest to all stakeholders. To better understand the processes within a wind turbine, the processes and data flows must be known and understood, not only for a single subsystem but for the entire wind turbine. We aim to comprehensively address the data for wind turbines and compile it as services into a digital twin, which can later be applied to specific wind turbine scenarios. Here, we present our ideas and approaches, focussing on predictive maintenance. In doing so, predictive maintenance can be performed by a wide range of analyses. The developed approach provides the modelling and integration of any kind of predictive maintenance into a digital twin. With this intent, we want to combine the different condition monitoring systems to create new possibilities for application scenarios and set up services for digital twins. With this as the start of our journey, the paper concludes with a test example, showing the results achieved so far.