Background:
Angular contact ball bearing is an important component of many high-speed
rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to
operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance
of the mechanical systems. However, as bearing rotation speed increases, the temperature rise
is still the dominant limiting factor for improving the performance and service life of angular contact
ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings
lubricated with oil-air.
Objective:
The purpose of this study is to provide an overview of temperature calculation of bearing
from many studies and patents, and propose a new prediction method for temperature rise of angular
contact ball bearing.
Methods:
Based on the artificial neural network and genetic algorithm, a new prediction methodology
for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of
oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature
rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and
the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural
Network (ANN) model based on these key influence factors was built up, two groups of experimental
data were used to train and validate the ANN model.
Results:
Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy
and better stability, the output of ANN-GA model shows a good agreement with the experimental
data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA
model.
Conclusion:
A new method was proposed to predict the temperature rise of oil-air lubricated angular
contact ball bearings based on the artificial neural network and genetic algorithm. The results show that
the prediction model has good accuracy, stability and robustness.
Design and Implementation of High Speed Artificial Neural Network Based Sprott 94 S System on FPGA
