Research on Dynamic Characteristics of High-Speed Helical Gears with Crack Faults in Electric Vehicle Deceleration Systems

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.