To increase the power density of the electromechanical drive train of wind turbines, journal bearings can be used as planetary gear bearings instead of rolling bearings. This technological change presents new challenges. For example, wind turbine drive systems are subject to dynamic and low-speed operating conditions, which can lead to accelerated abrasive wear of the journal bearings. In addition, oil supply failure or peak loads due to wind gusts and grid and power converter faults could potentially result in catastrophic failure due to adhesive wear in a very short time. Such operating characteristics are, therefore, critical regarding the journal bearing wear lifetime and must be considered in the design. The successful implementation of journal bearings in wind turbines depends on a reliable estimation of adhesive and abrasive wear. In this paper, five different models for the wear calculation of journal bearings are evaluated regarding their suitability for the wear calculation of planetary gear bearings in wind turbines. For this purpose, the following evaluation criteria were defined: parameter uncertainty, parametrization effort, in particular number of parameters, parameterization method, load case dependency of parameters and calculation effort. In order to be able to evaluate the wear models, the wear models are numerically implemented, and the wear of a test journal bearing is exemplarily calculated under load conditions, which are comparable to load conditions in a wind turbine. Relevant influences from the wind turbine system, such as lubricant, material and manufacturing-dependent surface influences, such as roughness and hardness, are considered. The wear models are evaluated with respect to their fulfillment of the defined criteria. The resulting evaluation allows the selection of a wear model that can be used to calculate the wear of planetary gear journal bearings in wind turbines, considering the available input variables.

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.

The use of journal bearings as planetary gear bearings in wind turbines instead of roller bearings is becoming common, as journal bearings are advantageous in terms of the torque density of the gearbox. The transition from roller bearings to journal bearings results in different bearing stiffness and damping characteristics, thereby altering the dynamics of the wind turbine gearbox and the load-bearing behavior of the gears. The dynamic behavior of journal bearings is highly dependent on operating conditions, as fluid film heights and hydrodynamic pressures vary with speed. To conduct a detailed analysis of these bearings and their effect on the gearbox dynamics, elasto-hydrodynamic simulations (EHD) coupled with multibody simulations (MBS) are generally performed. These simulations typically require significant computational resources. In order to reduce the calculation times for optimizing the gearbox with regard to Noise-Vibration-Harshness and load-bearing capacity, a simplified representation of the bearing in multi-body simulations is desired, ideally as a simple stiffness and damping matrix. While standard approaches exist for deriving stiffness and damping matrices for journal bearings, these methods do not adequately capture the complex deformations and load conditions present in planetary journal bearings. As a result, these bearings exhibit stiffness and damping behavior that depends on local conditions and operating points. Therefore, this paper presents an approach to extract these stiffness and damping values from EHD simulations across the operating range of a wind turbine. These extracted values are applied to a multi-body simulation model of a wind turbine gearbox. This paper introduces a novel approach for modelling planetary journal bearings. By dividing the bearing into small slices, a multi-slice bearing is created that can capture the complex stiffness and damping characteristics present in planetary journal bearings. This sliced bearing modelling method is compared with a coupled EHD-MBS model of the same wind turbine gearbox to analyze the effects on system dynamics. The novel planetary journal bearing modelling will enable faster design of wind turbine gearbox components and mitigate vibrations efficiently.

Recent years revealed a notable trend for rolling bearings to be increasingly replaced by plain bearings for particular applications. A prominent example is the planetary stage in wind turbine gearboxes. The advantages of plain bearings are higher realizable power density due to smaller installation space compared to rolling bearings as well as the possibility of wear-free operation under ideal operational conditions. In real applications, however, plain bearings are subject to wear, for example due to start-stop processes, which lead to mixed friction conditions. Since the wear of plain bearings strongly depends on the operational conditions, monitoring is essential to detect inadmissible operating conditions. To avoid extra installation space and to achieve a minimal influence on the surrounding system, this work aims to integrate an energy-autarkic sensor system into the bearing volume to monitor the operational status of the plain bearing. To achieve autarky, the resulting data must be transmitted wirelessly and the external power supply must be eliminated. In this work, the software and hardware architecture of a sensor-integrating plain bearing prototype for monitoring the minimum oil gap height and the displacement angle of the shaft based on in-situ temperature measurements is presented. Furthermore, energy harvesting and energy saving measures are implemented and evaluated which intend to enable completely autarkic operation of the sensor integrating machine element in the future. A prototype of the sensor system is in turn used in-the-field to investigate required sampling rates and the spatial resolution of the temperature measurements.

To increase the power density of the electromechanical drive train of wind turbines, journal bearings can be used as planetary gear bearings instead of rolling bearings. This technological change presents new challenges. For example, wind turbine drive systems are subject to dynamic and low-speed operating conditions which can lead to an accelerated abrasive wear of the journal bearings. In addition, oil supply failure or peak loads due to wind gusts and grid and power converter faults could potentially result in catastrophic failure due to adhesive wear in a very short time. Such operating characteristics are, therefore, critical regarding the journal bearing wear lifetime and must be considered in the design. The successful implementation of journal bearings in wind turbines depends on a reliable estimation of adhesive and abrasive wear. In this paper, five different models for the wear calculation of journal bearings are evaluated regarding their suitability of wear calculation of planetary gear bearings in wind turbines. For this purpose, the following evaluation criteria were defined: parameter uncertainty, parametrization effort, in particular number of parameters, parameterization method and load case dependency of parameters and calculation effort. In order to be able to evaluate the wear models, the wear models are numerically implemented and the wear of a test journal bearing is exemplarily calculated under load conditions, which are comparable to load conditions in a wind turbine. Relevant influences from the wind turbine system such as lubricant, material and manufacturing dependent surface influences like roughness and hardness are considered. The wear models are evaluated with respect to their fulfillment of the defined criteria. The resulting evaluation allows the selection of a wear model that can be used to calculate the wear of planetary gear journal bearings in wind turbines, considering the available input variables.

The increasing application of plain bearings in various industries, especially under challenging conditions like thin lubricating films and high temperatures, necessitates effective monitoring to prevent failures and ensure reliable performance. While sensor-based monitoring incurs significant costs and complex installation due to physical sensors and data acquisition systems, model-based tracking offers a more cost-effective alternative. Model-based monitoring relies on mathematical or physics-based models to estimate system behaviour, reducing the need for extensive sensor data. However, reliable results depend on real-time capable and precise simulation models. Conventional real-time modelling techniques, including analytical calculations, empirical formulas, and data-driven methods, exhibit significant limitations in real-world applications. Analytical methods often have a restricted range of applicability and do not match the accuracy of numerical methods. Meanwhile, data-driven approaches rely heavily on the quality and quantity of training data and are inherently constrained to their training domain. Recently, Physics-Informed Neural Networks (PINNs) have emerged as a promising solution for model-based monitoring to capture complex system behaviour. This approach combines physical modelling with data-driven learning, allowing for better generalisation beyond the training domain while reducing reliance on extensive data. Thus, this study presents an approach for load monitoring in radial plain bearings using PINNs. It extends the application of PINNs by relying solely on simple sensor inputs, such as radial load and rotational speed, to predict the hydrodynamic pressure and oil film thickness distribution under varying stationary conditions. The real-time model is trained, validated, and evaluated within and beyond the training domain using elastohydrodynamic simulation results. The developed real-time model enables load monitoring in plain bearings by identifying critical hydrodynamic pressure and oil film thickness values using readily available speed and load sensor data under varying stationary conditions.