Applications of Artificial Neural Network in Pressure Distribution Analysis of Hydrodynamic Journal Bearings

Abstract

This paper investigates experimentally the variations in pressure distribution in the hydrodynamic journal bearing system for different sets of loads, speeds and lubricants. This paper mainly consists of two parts, first part of theoretical calculations and experimental readings. second part of simulation. In the theoretical calculations, pressure distribution of journal bearing is calculated using Reynolds's equation. In experimental work practical pressure distribution developed in journal bearing system is measured on test rig. Test is performed at different set of loads and speeds using SAE20W40, SAE90, SAE140 and water as lubricants. The collected experimental data of pressure distributions are employed as training and testing data for an artificial neural network. The neural network is a feed forward network. Back propagation algorithm is used to update the weight of the network during the training. finally, neural network predictor has predicted pressure distribution which is in close agreement with practical pressure distribution given by test rig.