A Real-Time Wheel–Rail Force Identification Method via Axlebox Acceleration-Based Res-FBLM Model

Wheel-rail force is a critical physical parameter in rail transit, playing a vital role in assessing both the operational safety of vehicles and the durability of structural service. However, existing studies have shown that wheel-rail force identification requires multiple types of data input, which poses a significant challenge for engineering applications. To address this issue, this paper proposes a Residual Frequency-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism (Res-FBLM) model, which relies solely on triaxial signals from the axlebox as input. Subsequently, a multibody dynamics model of an urban railway vehicle is established and simulated under various conditions to generate wheel-rail force and acceleration data. The research results show that the proposed model achieves high-precision identification of wheel-rail forces, with an R-squared (R²) value exceeding 98% on the test set. Additionally, the proposed method was further validated on the wheel-rail interaction test rig. The computation time on the edge deployment device, a Raspberry Pi, is 12.4ms/sample, which satisfies the real-time constraints imposed by high-frequency sampling. Research indicates that the proposed model achieves high accuracy and real-time performance using only axlebox acceleration as input, thereby offering technical support for the safety monitoring of railway vehicles.