Machine learning-based prediction of friction torque and friction coefficient in statically loaded radial journal bearings

Abstract

In this research, we utilized machine learning (ML) algorithms to predict the friction torque and friction coefficient in a statically loaded radial journal bearing. The study investigated the influence of temperature, bearing load, and rotational speed on the variation in friction torque and friction coefficient. Three different ML algorithms, namely, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Regression Trees (RT), were applied to experimental tribological data. Performance assessment demonstrated that ML-based models can successfully predict the variation of friction torque and friction coefficient. Furthermore, we conducted a comparative analysis to evaluate the performance of ML-based models in relation to each other. The results of this study have useful implications for the design and optimization of statically loaded radial journal bearings.