The usage of journal bearings as planetary bearings in wind turbines instead of roller bearings has become more common in recent years. Their usage is advantageous, due to smaller installation space needed compared to roller bearings allowing for higher power densities of wind turbine drive trains. However, this technology presents a challenge since there is currently no standardized approach for the design of planetary journal bearings regarding wear. Due to varying wind speeds and dynamic operating events a large variation of loads has to be considered in the design process of a planetary journal bearing for wind turbines. Some of these loads are considered potentially critical to the journal bearing in terms of wear. Identifying these critical load areas early in the design phase supports a reliable bearing design and wind turbine operation.This paper introduces a method to identify critical operating conditions for planetary journal bearings using a simulation tool chain, which couples a multi body simulation (MBS) model of a wind turbine with an elasto-hydrodynamic (EHD) model of the planetary journal bearing. Based on the EHD results critical operating conditions are determined for the planetary bearing. Furthermore, methods are implemented to reduce the number of required EHD simulations for analysing the bearing design. The combination of the identification of critical operating conditions, while reducing the computational effort leads to a simulation methodology, which enables a faster bearing design assessment considering the wide variation of wind turbine operating conditions. The applicability of this method is demonstrated by a simplified use case.Firstly, this paper introduces the MBS model and the parameter space that describes possible combinations of bearing loads such as forces, moments and rotational speed. Due to the number of combinations and the EHD computing effort, the identified parameter space is secondly sampled statistically to reduce the simulation effort. A risk map is derived from the EHD results, to easily indicate potentially critical operating conditions for the planetary journal bearing.

Journal bearings are used more and more in wind turbine (WT) gearboxes. Compared to rolling element bearings they are advantageous in terms of power density and reliability. Despite their reliability and theoretically unlimited fatigue life, journal bearings can be damaged by particularly critical operating conditions that do not represent normal WT operation. As journal bearing damage can occur very suddenly in the worst case, continuous monitoring of the bearing’s condition is advisable. Particle contamination in the lubricant and an outage of the oil supply can be particularly harmful to the bearing. Condition monitoring systems (CMS) have the potential to detect such critical operating conditions in journal bearings before damage occurs. A detection of these conditions is crucial for preventing bearing damage and thus gearbox failure which results in turbine downtime and yield loss. If failures of journal bearings in WT gearboxes can be avoided through the use of CMS this, in the long term, has the potential to reduce maintenance and repair costs in the field application. In this work the novel surface acoustic wave (SAW) measurement method is presented for the detection of particle contamination and lubrication outage. The SAW method is advantageous compared to conventional monitoring methods such as vibration measurements, as it is based on measuring the propagation behavior of actively introduced SAW into the bearing. This makes the method particularly robust against disturbing noise. For the evaluation of the signals and the detection of the aforementioned operational anomalies a machine learning approach is used. The latter is implemented such that an online monitoring can be performed with only a short latency between data input and evaluation. The presented method was validated on a component test rig for journal bearings. For the experiments the SAW measurement was implemented into the test bearings. In the test campaign, the anomalies were actively induced and the bearing behavior observed over time. This work provides insight into the signals measured during the occurrence of operational anomalies and proves that a lubrication outage and particle contamination can be detected using SAW.

Decreasing the levelized cost of energy is a major design objective for wind turbines. Accordingly, the control is generally optimized to achieve a high energy production and a high-power coefficient. In partial load range, speed and torque are controlled via the generator torque but the rotor torque determines the power coefficient of the turbine. High uncertainties for the uncalibrated low-speed shaft torque measurement and varying drivetrain efficiencies which depend on the speed, load and temperature lead to a torque control error that reduces the power coefficient of the wind turbine. In this paper the rotor torque control error and the impact on the power coefficient of wind turbines is quantified. For this purpose, the variation of drivetrain efficiency is analyzed. An efficiency model for the wind turbine drivetrain is build and validated on the test bench. Then, the influence of the drivetrain speed, torque loads, non-torque loads, and temperature on the efficiency is quantified. Finally, the influence of the rotor torque control error on the power coefficient was simulated with an aerodynamic model. The results show that of all examined influences only torque and temperature significantly impacting the efficiency leading to rotor torque control errors that reduce the power coefficient and consequently increase the levelized cost of energy. Improved efficiency measurement on WT test benches or drivetrain efficiency modelling can reduce the rotor torque control error and therefore decrease the LCOE.

Hydrodynamic journal bearings are a driver of increasing power density and reliability of wind turbine gearboxes. Despite their high fatigue reliability operating in hydrodynamic conditions, journal bearings can be damaged quickly in the event of a critical operating condition with solid body contact (e.g. severe overload and mixed friction or failure of the lubricant supply). In the worst-case scenario, bearing damage occurs suddenly with no clear warning period. To reduce the risk of damage condition monitoring systems (CMS) can be installed for the continuous monitoring of the bearing’s operating condition. In the event of a critical condition countermeasures can be taken immediately (e.g. derating the wind turbine) to reduce the damage risk. This approach has the potential to further increase wind turbine reliability and reduce costs resulting from downtime and maintenance. However, commercial CMS for journal bearings in wind turbines with the required level of readiness for field operation have not yet been fully developed and thoroughly tested. One monitoring technique potentially capable of detecting the change from normal to critical operating conditions immediately is based on surface acoustic waves (SAW). This work presents the results of an experimental study of planetary journal bearings equipped with a CMS based on the SAW method. The aim of this work is the proof of concept of the condition monitoring method based on SAW in a planetary journal bearing and the demonstration of the transferability of the SAW method from small radial journal bearings to planetary journal bearings. The results show that different friction states can be detected using the SAW method. The paper gives insights to the application of the measuring equipment, the operation conditions analyzed and into the analysis of the data. The CMS for the planetary journal bearing based on SAW presented here is the first of its kind and represents a major step towards the continuous monitoring of journal bearings in wind turbine drive trains. Through a future introduction of a fully developed SAW-based CMS in the field application in wind turbines an early detection of critical operating conditions for journal bearings can be achieved. Thus, the prevention of gearbox damage and the reduction of costs for operation and maintenance due to journal bearing failures can potentially be made possible.

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.

The use of journal bearings instead of rolling bearings as planetary bearings in wind turbine (WT) gearboxes is advantageous for the power density of the drive train. In addition, they can increase the reliability of the gearboxes as they can be operated wear-free in hydrodynamic operation, i.e. with fluid friction. The dynamic loading conditions in wind turbines, as well as special conditions such as insufficient lubrication and particle contamination, can lead to mixed friction operation and consequently to wear in the journal bearings. If mixed friction is not detected, damage and failure of the journal bearing and consequently of the WT gearbox may occur. Therefore, it is required to develop a Condition Monitoring System (CMS) to detect mixed friction in the journal bearings during operation of the WT. Preliminary investigations have shown that various friction conditions can be detected by acoustic measurements in combination with machine learning classifiers. Current investigations on CMS methods for journal bearings using acoustic measurement methods are limited to component level applications. A condition monitoring methodology for wind turbine gearbox journal bearings does not currently exist. A major challenge for CMS development for journal bearings in WT gearboxes is the transfer of methods already proven at component level to gearbox applications. In preparation for CMS application at the gearbox level, this paper presents an approach for monitoring different friction conditions of journal bearings based on acoustic measurements at a component test rig. For the classification of the friction state, different machine learning (ML)-based approaches trained on the acquired acoustic measurement data are compared with respect to the achieved classification accuracy. Knowledge of the robustness of the classification method, e.g. with respect to the distance of the sensor to the bearing, provides the necessary basis for the use of the CMS at the gearbox level. The investigations are carried out under operating conditions typical for planetary bearings in wind turbines. Classification performance is evaluated using a validated elasto-hydrodynamic simulation model. The aim of the work is to develop a method that detects friction classes in the journal bearing based on structure-borne sound measurements. Here, simulation results are used to train the algorithms. Finally, the demonstrated method will be successfully applied to a test rig for wind turbine gearbox journal bearing. Based on the results, an ML approach will be selected for application in gearboxes.