Hydrodynamic journal bearings are used within a wide range of machines, such as combustion engines, gas turbines, or wind turbines. For a safe operation, awareness of the lubrication regime, in which the bearing is currently operating, is of great importance. In the current study, highspeed data signals of a torque sensor, sampled with a frequency of 1000 hz in a time range of 2.5 s, obtained on a journal bearing test-rig under various operating conditions, are used to train machine learning models, such as neural networks and logistic regression. Results indicate that a fast Fourier transform (fft) of the highspeed torque signals enables accurate predictions of lubrication regimes. The trained models are analysed in order to identify distinctive frequencies for the respective lubrication regime.

The acquisition and evaluation of acoustic emissions (AE) in tribology have proven to be a meaningful tool for condition monitoring and offer possibilities to deepen the understanding of tribological processes. The authors used this technology with the aim of expanding existing test methodologies towards increased visualization capability of tribological processes and investigated the correlation between tribological processes and acoustic emissions on a Ring-on-Disc and a close-to-component journal bearing test setting. The results of this study include the description of friction as well as wear processes and prove the usability of several AE evaluation parameters whereby a close correlation between AE and tribological processes can be shown. Consequently, it was possible to expand the visualization and evaluation capabilities of the test settings offering additional insights by making use of AE.

The common approach for the flow factor calculation is based on using the Reynolds equation to simulate the micro-level flow. However, for structured surfaces the fluid flow cannot be represented correctly, due to the assumptions made when deriving the Reynolds equation. In this work, a novel method using the Navier-Stokes equations for the calculation of the micro-level flow is presented and validated against results from Patir and Cheng. The three-dimensional lubrication gap was generated by a rough Gaussian random surface and a perfectly smooth moving counter surface, in order to be available for different numerical methods. The presented results illustrate similar trends for both the approaches. Additionally, the use of the Navier-Stokes equations allows for the observance of surface induced effects which cannot be resolved by the approach of Patir and Cheng. Furthermore, a numerical approach for a shear flow factor calculation with a rough moving surface is presented and validated against other simulation methods. While the validation is maintained with pressure- and temperature-independent density and viscosity, these effects will be taken into account for later research activities of textured surfaces.

The current paper addresses the field of experimental research of journal bearing systems. In this regard, the challenges are dealt with concerning simultaneous testing with a close correlation to the industrial application and with a high resolution of tribological processes. Concerning this aspect, two damage equivalent laboratory test methodologies for journal slide bearing systems are presented, and their ability to visualize certain performance parameters of bearing systems are emphasized (for instance friction performance, (start stop) wear processes, and seizure events). The results clearly emphasize that the applied methodologies provide accurate findings regarding specific effects of selective parameters/changes on the performance of bearing systems, such as polymer overlays may result in improved mixed friction sliding conditions if designed properly, and they provide superior start stop wear resistance; the use of specific corrosion inhibitors can successfully prevent tribo-corrosion on bronze bearings; a decrease of oil viscosity increases solid friction share but decreases fluid friction; lubricant anti-wear additives are able to improve seizure resistance and sliding properties of bearing systems depending on formulation harmonization; and novel bearing material coatings, e.g., sputtered SnCu, can significantly improve emergency running capabilities.

Hydrodynamic journal bearings are subjected to progressively rough loading conditions leading to an increased share of operation in mixed and boundary lubrication. This results in increased frictional losses, additional wear and a higher chance of failure, which calls for the understanding of wear processes and the necessity of a numerical assessment. We conducted wear investigations of journal bearings by making use of a close-to-component test setting, and the progress of wear could be linked to the introduced frictional energy and in combination with a comprehensive surface analysis tribological effects could be resolved in detail. Achieved wear coefficients were implemented in a novelly developed numerical framework, which allows for the dynamic numerical evaluation of operation in the fluid and mixed lubrication regime and simultaneously occurring wear processes. By comparing numerical and experimental results, we evaluated the numerical framework’s capability to conduct holistic simulations including aspects like dynamically changing operation conditions, fluid and mixed lubrication as well as wear.