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 concept of two-disc model testing has proven to deliver valuable information for the applicability of new technologies, such as surface structuring, coatings, alternative fluids, or advanced materials, in actual machine elements. In this article an advanced two-disc machine with sophisticated control technology for dry and lubricated setup is presented. (i) All involved components are controlled via a powerful PLC unit leading to the possibility of realising extremely accurate SRRs down to 0.1%. (ii) High-speed data acquisition allows local insight into tribological phenomena by providing 72 data points along one shaft rotation. (iii) Several lubrication scenarios such as fluid, mixed, and starved lubrication, as well as dry contacts, can be considered. (iv) Raw-data of all sensors, including normal force, friction force, vibration speed, stiffness (wear), infrared temperature, contact potential, and motor speed, is presented. Some example results of dry polymer-steel tribosystems and lubricated experiments are shown to elucidate the capabilities of the novel test rig.

Due to legislative requirements companies are enforced to take on demanding engineering challenges in order to fulfil targets regarding CO2-emissions and pollutions. Especially the automotive industry has come up with a range of technologies throughout the past decade to reduce the amount of exhaust gases. Tribology has played an important role and will be significant in the future when it comes to creating environment-friendly machines. Especially machine elements, such as gears, camshaft or roller-bearings, which are referred to as non conformal machine elements from here on, are heavily loaded and have to operate under severe conditions. Often this results in the energetically unfavourable mixed friction state, which means that the total external load is shared by the oil and micro contacts. The current work is focused on methods for modelling these micro contacts in order to take them into account in state of the art EHD (elastohydrodynamic) models. This enables an in depth analysis of highly loaded machine elements in order to optimize machine elements sustainably.