A hybrid physics-based and data-driven method for gear contact fatigue life prediction

Abstract

A hybrid physics-based and data-driven method is proposed for gear contact fatigue life prediction. The parameters influencing the fatigue life are determined by the physics-based model. A deep belief network (DBN) model is developed to reveal the relationships between these parameters and fatigue life. A variational autoencoder (VAE) model is presented to expand the size of the training dataset. The proposed method is verified by a gear contact fatigue test, and the predictions are all within a factor of 1.5 scatter band of the experimental results. This work provides an effective method for life prediction with small sample sets.