This paper addresses the issue of fault diagnosis in high-speed train bogie bearings under complex working conditions and proposes a method for calculating the characteristic frequency of rolling bearings that takes into account the influence of radial clearance. By establishing a five-degree-of-freedom nonlinear dynamic model, this study systematically analyzes the modulation mechanism of radial clearance on the fault characteristic frequency of bearings and verifies the findings through an experimental platform. The results indicate that an increase in clearance not only leads to significant attenuation of the fault characteristic frequency amplitude, but also induces sideband modulation effects, thereby interfering with fault diagnosis accuracy. The experimental data show good agreement with the theoretical calculations, verifying the effectiveness of the proposed method. Specifically, the nonlinear stiffness-based characteristic frequency calculation reduces the prediction error from 6.9–5.7% under traditional theory to 2.3–3.4% across a wide range of rotational speeds. Meanwhile, the clearance-induced amplitude attenuation predicted by the model is also experimentally confirmed, with measured amplitude reductions of 35–42% as clearance increases from 0.2 μm to 0.5 μm. These results not only demonstrate the accuracy and engineering applicability of the method but also provide new theoretical foundations and practical references for health monitoring and early fault diagnosis of high-speed train bearings.

This paper proposes a mathematical model-based analytical approach to address the cutting force prediction and performance optimization challenges in planing-type anti-climbers for high-speed train passive safety systems. The method overcomes the reliance on experimental calibration inherent to conventional approaches, enabling the efficient quantitative evaluation of anti-climber cutting performance. By equivalently modeling the collision energy dissipation process as an orthogonal cutting model, a theoretical framework integrating material dynamic response characteristics and impact boundary conditions was developed for direct cutting force prediction without experimental calibration. Finite element modeling implemented on the ABAQUS platform was employed for simulation analysis, supplemented by dynamic impact tests for validation. The results demonstrate that the model achieves ≤15% relative error compared with the simulation data and ≤5% deviation from the experimental measurements, confirming its engineering applicability. Sensitivity analysis reveals that cutting depth exhibits the most pronounced positive correlation with cutting force, while increased tool rake angle reduces cutting force. The dynamic equilibrium between thermal softening effects and strain rate strengthening leads to cutting force reduction with elevated cutting speed. This research establishes theoretical and technical foundations for the intelligent optimization of passive safety systems in rail transit equipment.