A friction temperature model for dynamic bearing operation based on neural network and genetic algorithm

Abstract

This study used intelligent algorithms and experimental data to investigate the dynamic temperature of bearings. A test rig was created for angular contact ball bearings to measure the temperature of two bearings precisely while independently controlling rotational speed, axial load, radial load, and grease content. The study analyzed the steady temperature of bearings under different operating conditions using a friction temperature model based on SKF bearings. The bearings temperature was computed using traditional backpropagation neural network (BPNN) and genetic algorithms (GA). The study compared the results obtained through the SKF model and the actual temperature against those produced methods. GA displayed a higher level of accuracy and robustness. Upon evaluation, it was determined that both intelligent methods are suitable for processing variable data input. This article presents an intelligent approach to computing bearing temperature using statistical data analysis, which does not require analyzing dynamics, thermodynamics, or lubrication principles. The method's ability to compute transient temperature without boundary conditions or constraints makes it highly versatile. This feature renders it a valuable tool for bearing design and temperature control.