Wear Prediction of Cylindrical Gears Based on Deep Neural Networks

Abstract

Gears serve as core transmission components, and their wear evolution directly affects equipment stability and service life under long-duration complex loading. Especially under complex loading and long-term service conditions, the tooth surface topography undergoes continuous evolution. However, traditional wear prediction methods based on physical models or empirical formulas have significant limitations in addressing nonlinear problems involving multiple coupled variables. This study proposes a deep neural network (DNN)-based method for gear wear prediction. Geometric parameters, loading conditions, and surface topography characteristics are integrated as model inputs to enable point-by-point prediction of tooth-profile wear. Experimental results demonstrate that the proposed model achieves excellent predictive performance in the mild-wear regime, with a mean absolute error (MAE) below 2.5 × 10−4 mm, a root mean square error (RMSE) below 5.0 × 10−4 mm, and R2 values ranging from 0.92 to 0.99. The model also achieves satisfactory prediction accuracy at previously unseen measurement positions and for previously unseen superfinished gear samples. The proposed DNN effectively learns implicit wear-evolution patterns from experimental data and exhibits strong generalization capability, providing a practical approach for gear health monitoring and predictive maintenance.