Gear contact fatigue life prediction based on transfer learning

The gear contact fatigue test is characterised by extraordinarily high costs and a long period of testing. A transfer learning algorithm was applied to develop a gear contact fatigue life prediction strategy to fully use other low-cost fatigue test data, such as twin-disc test data. The twin-disc contact fatigue test data were incorporated into the neural network model to explore the combined effects of surface integrity and stress level on fatigue life, which was further transferred for gear contact fatigue life prediction. The results indicate that this method enables an effective gear fatigue life prediction under minor gear sample conditions. With a small sample amount of 7, the prediction error in the fatigue life of the model with transfer learning is 46.33%, while the typical back-propagation neural network (BPNN) model without transfer learning is 101.66%. To control life prediction error to within 50%, the model without transfer learning requires at least 17 gear samples, while the transfer model calls for only 7 gear samples. The proposed data transfer learning framework can be used to predict the gear contact fatigue S-N curve under minor sample conditions and is straightforwardly applied to extended applications.