Browsing by Author "Poll, Gerhard"
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- ItemEffect of Harmful Bearing Currents on the Service Life of Rolling Bearings: From Experimental Investigations to a Predictive Model(2024) Schneider, Volker; Krewer, Marius; Poll, Gerhard; Marian, MaxThis study investigates the effects of harmful bearing currents on the service life of rolling bearings and introduces a model to predict service life as a function of surface roughness. Harmful bearing currents, resulting from electrical discharges, can cause significant surface damage, reducing the operational lifespan of bearings. This study involves comprehensive experiments to quantify the extent of electrical stress caused by these currents. For this purpose, four series of tests with different electrical stress levels were carried out and the results of their service lives were compared with each other. Additionally, a novel model to correlate the service life of rolling bearings with varying degrees of surface roughness caused by electrical discharges was developed. The basis is the internationally recognized method of DIN ISO 281, which was extended in the context of this study. The findings show that the surface roughness continues to increase as the electrical load increases. In theory, this in turn leads to a deterioration in lubrication conditions and a reduction in service life.
- ItemEnhancing practical modeling: A neural network approach for locally-resolved prediction of elastohydrodynamic line contacts(2024) Kelley, Josephine; Schneider, Volker; Poll, Gerhard; Marian, MaxWhen modeling bearings in the context of entire transmissions or drivetrains, there are practical limits to the calculation resources available to calculate single bearings or even contacts. In settings such as these, curve-fitting methods have historically been deployed to estimate the elastohydrodynamic lubrication conditions. Machine learning methods have the potential to enable more sophisticated physical modeling in the context of larger computation environments, as the evaluation time of a trained model is typically negligible. We present a neural network that accurately evaluates the locally variable elastohydrodynamic film pressure and film thickness distributions and explore its application to (e.g.) cylindrical roller bearings. Employing a neural network for the EHL film thickness calculations rather than the curve-fitted, simplified methods that are today’s standard can enable a more physically precise modeling strategy at almost no additional computational cost.