摘要：The dynamic behaviour of a double-helical gear system supported by journal bearings is theoretically and experimentally investigated in this study. A bending–torsional–axial coupling model for dynamic analysis of double-helical gear system is developed. Influence of the time-varying mesh stiffness and damping is considered. Oil film stiffness and damping of the supporting journal bearing are supposed to be time-varying, and the time-varying oil film stiffness and damping are predicted by a back propagation neural network, which is optimized by genetic algorithm. A double-helical gear–rotor–journal bearing system test rig is also established to carry out the experimental investigations, such as the dynamic transmission errors of gear pairs. The comparisons between theoretical and experimental results show that the time-varying oil film dynamic coefficients of journal bearings are an important internal excitation. The theoretical model with time-varying oil film stiffness and damping can predict the gear dynamics more accurate than the model with time-invariant oil film stiffness and damping, and the neural network optimized by genetic algorithm can obtain the time-varying oil film stiffness and damping efficiently and accurately for the dynamic analysis of double-helical gear system.
