Dongsheng Li

As a key component of pure electric vehicles, the reducer plays a vital role in power transmission and overall drive system performance. This study investigates the nonlinear dynamic characteristics of helical gears with tooth root crack faults in high-speed reducers. A coupled bending–torsional–shaft dynamic model is developed, in which the time-varying mesh stiffness of cracked helical gears is calculated using an improved potential energy method. The system’s nonlinear dynamic responses under varying mesh error excitation, gear backlash, and damping ratio are numerically obtained via the variable-step Runge–Kutta method. The results reveal that under high input speed conditions, the motion of the faulted system evolves from single-period to quasi-periodic motion as bifurcation parameters change. In the stable state, fault characteristic signals are apparent, whereas under strong nonlinear vibrations and chaotic motion, they become difficult to distinguish in traditional time- and frequency-domain analyses. To address this limitation, the DBSCAN clustering algorithm is introduced, which applies machine learning to cluster the Poincaré cross-sections of the system under different motion states. This approach enables the effective classification and identification of crack-induced and fault-related noise, thereby improving the accuracy of fault detection in nonlinear dynamic gear systems.

The drive system of pure electric vehicles is characterized by high transmission efficiency and a rapid torque response, with the centralized drive configuration being the most commonly adopted. To improve the dynamic performance and reliability of such systems, this study investigates the nonlinear dynamic characteristics of high-speed helical gear reducers used in electric vehicles. A coupled bending–torsional–shaft dynamic model is established, in which the time-varying mesh stiffness is calculated using an improved potential energy method. The system responses under varying mesh errors, backlash, and damping ratios are obtained through numerical integration via the variable-step Runge–Kutta method. The results demonstrate that at high input speeds, the helical gear system exhibits complex nonlinear behavior. Small backlash and minor manufacturing errors lead to stable periodic or quasi-periodic motion, whereas increasing these parameters induces dynamic instability. Moreover, enhancing the mesh damping ratio effectively suppresses chaotic responses and improves overall system stability.