Prediction of Gear Bending Fatigue Life Based on Grey GM (1,1) Prediction

It is very important to analyze and predict the fatigue life of gear, which is the key part of transmission. Due to the small amount of bending fatigue life data, two sample expansion methods, intermediate interpolation method and Lagrange interpolation method, are used to expand the amount of data, establish equal spacing and non-equal spacing grey GM (1,1) prediction models respectively, and test the models. The results show that the most accurate prediction results can be obtained without interpolation for non-equal spacing models, while the most accurate prediction results can be obtained by Lagrange interpolation for non-equal spacing models. Compare the gray GM (1,1) prediction model with three traditional prediction methods, the results show that the gray GM(1,1) prediction model can obtain the most accurate prediction results for small data. It provides the manufacturer with the processing method of small data and the prediction method of gear bending fatigue life under unknown stress.