Application of artificial intelligence for the prediction of plain journal bearings performance

Artificial intelligence techniques are applied to predict the performance of a plain journal bearing instead of classical methods. AI techniques are known to be superior for prediction; they are accurate and fast compared to finite difference, finite element, and finite volume methods. To obtain the data needed for the AI prediction, the finite difference method is used to solve the dimensionless Reynolds equation at various aspect ratios. The bearing performance characteristics, such as load-carrying capacity, attitude angle, friction variable, and maximum-film-pressure ratio, are determined considering isothermal conditions. Four aspect ratios are considered from 0.25 to 4, with eccentricity ratios varying between 0.2 and 0.8. Three artificial neural networks (Feed forward, Radial basis, and Generalized regression networks) and fuzzy logic techniques were applied to the obtained data from FDM simulation to predict the performance parameters. The three trained neural networks and the fuzzy system were tested to obtain the performance characteristics for aspect ratios and eccentricity ratios that were not included in the FDM study. The current response of the trained ANN models and the fuzzy logic technique is found to be very fast and precise, with a prediction computational time of less than one second and an error of less than 2.5 percent. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).

Automated summary

This study applies artificial intelligence techniques to predict the performance of a plain journal bearing, showing their superiority over classical methods in terms of accuracy and speed. The data required for AI prediction is obtained using the finite difference method, and artificial neural networks and fuzzy logic techniques are utilized for performance parameter prediction.