Planetary journal bearings are enablers for wind turbine gearbox torque density and reliability increase due to their compactness and potentially unlimited lifetime. They are designed to withstand the load conditions during wind turbine operation. Despite their general robustness, abnormal events such as particle contamination, strong overload or operation without sufficient oil supply may be harmful to the bearings. In these cases, damage can occur quickly and with little warning time. Such spontaneous failure leads to turbine downtime and cost-intensive repair work on the wind turbine drive train. Thus, reliable load and condition monitoring systems, which allow the detection of critical operating states before damage occurs, would be beneficial. For journal bearings in wind turbine gearboxes, no commercially available monitoring system exists to date. The existing studies on journal bearing condition monitoring are limited to experiments on component test rigs or small gearboxes, and their transferability to full-size systems has yet to be proven. This work presents the results of a system test with an 850 kW wind turbine gearbox equipped with planetary journal bearings and a novel condition monitoring system based on the measurement of surface acoustic waves. First, the journal bearing design, including the sensor setup, is explained. Second, the test campaign layout is presented. The gearbox is tested under load conditions specific to wind turbines, and the condition monitoring signals are examined in detail. An algorithm based on a machine learning model is presented for evaluating the monitoring signals and predicting the friction state of the bearings. Finally, the practical feasibility and quality of the monitoring approach for planetary journal bearings presented in this work is discussed.

Tidal turbines offer great potential for base load supply in coastal and remote regions with sufficient tidal flow rates, due to their good predictability compared to established renewable energy sources such as wind and solar. Being still at the prototype stage, tidal turbines are not yet competitive in terms of levelised cost of energy (LCOE). Since there is a lack of experience and data regarding the operation of tidal turbines for long periods of time, there are no sophisticated maintenance strategies in place yet. Thus, currently most tidal turbine drivetrains are designed with high margins of safety to be robust against failures. However, designing with high margins of safety is costly increasing the LCOE. Designing with lower margins of safety requires a condition monitoring system since on the one hand the remaining useful lifetime can be estimated and on the other hand critical conditions (e.g. due to increased flow rates or turbulence) can be avoided by adjusting the control of the turbine. To calculate the remaining useful lifetime of a component, the component loads during operation must be known. While being able to calculate loads in easily accessible components from measurement data, some components do not offer cost-effective possibilities for sensor application and their loads during operation remain unknown. Therefore, the component loads need to be calculated indirectly based on available sensor data. Drivetrain component loads depend on complex elastic and dynamic interactions between individual components. To represent these complex interactions, a simulation-based digital twin is developed for the condition monitoring of the tidal turbine drivetrain components. The digital twin is developed using a multi body simulation (MBS) environment with flexible bodies. As the MBS model is not real-time capable but the condition must be monitored in real time, the MBS model is used to generate training data for an efficient, machine learning based surrogate model. To reduce computational time further, critical components are modeled with a high level of detail while other components are represented using simplified models. Therefore, in this paper the critical components are identified to choose appropriate component modeling fidelities. A load calculation model for the drivetrain component loads is developed. The component loads are calculated analytically based on measurements with existing sensors from field operation. The remaining useful lifetime (RUL) of each component is calculated by comparing the calculated component loads with the design loads. Based on the RULs, critical components are identified in terms of risk of early failure to define the required modeling fidelity of each component for the digital twin.

Climate change is one of the greatest threats faced by the modern world. The world must increase the use of renewable energy to meet the 1.5 °C target of the Paris Agreement. One renewable energy source with good potential is tidal energy. Compared to the established renewable energy sources such as solar and wind, tidal energy is highly predictable, providing a stable and secure power conversion. Despite a global capacity of up to 1200 TWh/year, only about 40 MW of tidal energy capacity has been installed as of now. The main reasons for the slow adoption of the technology are the uncertainties regarding operating and maintenance costs, which result in a low willingness to invest among investors. A cost-efficient approach for proving the reliability of tidal turbine drive trains is the use of system test benches. Currently, no specific tidal turbine system test bench exists. Due to the similarities in the systems, existing wind turbine system test benches are being used for tidal turbine testing to avoid expenditures on testing infrastructure. Although wind and tidal turbines share similarities in design, like the drive train architecture or the blades that convert kinetic energy to electricity, they differ in detail. Exemplary differences are the number of blades or the load transfer path from the nacelle to the support structure. The load transfer path is particularly different for floating tidal turbines, as the tidal turbine moves more freely through the water than wind turbines with static towers. Furthermore, the non-torque loads (NTLs) are much higher than for wind turbines of similar power levels due to the larger density of water compared to air. Besides the higher density of water, there are phenomena in the underwater environment, such as surface waves, which do not occur in the environment of wind turbines and lead to additional NTLs for the tidal turbine drive train. The described differences and influences on the drive train NTLs must be identified, analyzed, and incorporated into an optimal test bench design and realistic load cases. The test bench design must replicate the effects of the modified load transfer path, and the load case must include all relevant NTLs. To date, there is no method to support the adaptation of wind turbine system test benches to tidal turbine testing or to define realistic NTLs. To address this research gap, this paper analyzes the differences in the design of tidal and wind turbines. Additionally, the differences between the subsea and the above sea level environment are examined that cause additional NTLs for the drive train. In contrast to wind turbines, no dominant design is established for tidal turbines. Therefore, the Orbital Marine’s 02-X is used as an exemplary tidal turbine. A simulation-based method is developed to evaluate the effects of the tidal turbine specifics on the system test bench designs. Furthermore, the method supports the transfer of effects unique to tidal turbines or the effect of the modified load transfer path to NTLs, which can be applied on wind turbine system test benches.