Characterization of time-frequency response in the two-stage herringbone gear transmission system based on crack-pitting coupling time-varying mesh stiffness

Due to factors such as eccentric load and error, the gear will produce cracks and pitting corrosion and other failure modes, resulting in the change of the dynamic characteristics of the two-stage herringbone gear transmission system (TSHBGTS), which will have a serious impact on the stability of the operation of the high-torque engine. To investigate the impact of crack-pitting coupling on the vibration characteristics of the TVMS, a dynamic model with 48 degrees of freedom was developed. This model considers various factors such as errors, time-varying meshing stiffness (TVMS), torsional stiffness, support stiffness, tooth friction, and repulsion slot variations. The potential energy method is employed to calculate the TVMS for each herringbone gear pair in the system, accounting for the effects of crack-pitting coupling and changes in the retractor slot parameter. The dynamic model is solved using the Runge–Kutta numerical integration method, enabling the analysis of the system’s time-domain and frequency-domain responses under varying degrees of crack-pitting coupling. The vibration testing explores how different degrees of crack-pitting coupling affect the system’s time-frequency response characteristics. The results reveal that the system’s stiffness decreases with an increasing degree of crack-pitting coupling, initially dominated by pitting and eventually by crack influence. The time-domain response characteristics exhibit shock behavior that varies with the meshing cycle, becoming more pronounced with increased crack-pitting coupling. Additionally, the frequency-domain response is characterized by side frequency signals near the octave frequency, indicative of crack-pitting coupling effects. This behavior exacerbates as the crack-pitting coupling intensifies, leading to deteriorating vibration stability. Comparative analysis of test and theoretical data demonstrates consistency in trends, affirming the model’s accuracy. The research results are of great significance for the dynamic stability and quality evaluation of TVMS.