Prediction of Frictional Moment of Cylindrical Roller Bearing Using Experimental Data-Driven Artificial Neural Networks

Abstract

Abstract Accurate prediction of the frictional moment of the bearing contributes to the correct determination of the power loss in drivetrains and the antifriction design of bearings. This paper investigates a method for accurately predicting the frictional moment of the cylindrical roller bearing (CRB) under a wide range of operating conditions. The complex relationship between the bearing frictional moment and multiple operating parameters such as the shaft speed, roller–raceway contact load, cage slip ratio and lubricating property is established using an experimental data-driven artificial neural network (ANN) model. To provide actual data for training and testing the ANN model, the frictional moment and multiple operating parameters of the test CRB are synchronously measured under many test conditions. Compared with the prediction results from conventional physical models, the experimental data-driven ANN model reveals a higher prediction performance of the frictional moment.