Research on the bogie frame stress prediction method based on vibration acceleration and neural networks

Vollständiger Abstract

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During service, bogie frames are subjected to combined low-frequency quasi-static loads and high-frequency wheel-rail excitations. They exhibit prominent frequency-band characteristics and are prone to fatigue damage. Traditional simulation methods rely heavily on boundary conditions, suspension parameters, and load inputs, making it difficult to achieve accurate stress prediction for critical locations under complex line operating conditions. To address this problem, this paper proposes a bogie frame stress prediction method that integrates finite element (FE) analysis, frequency-segmented sensor placement, and dual-band neural networks. FE analysis is performed to identify fatigue-vulnerable locations of the frame under low-frequency and high-frequency loads as stress monitoring positions. Combined with the optimal sensor placement method, this paper determines the layout scheme of vibration sensors for the frame under low-frequency and high-frequency excitation conditions. A dual-band stress prediction model for low and high frequencies is established based on synchronous acceleration-stress data acquired from line tests. A comparative study is conducted on long short-term memory (LSTM), temporal convolutional network, Transformer, and attention-enhanced models. Meanwhile, attention ablation and perturbation analyses are carried out to verify the model’s capability to capture key temporal segments. The results show that the LSTM with attention (LSTM-ATT) model achieves favorable overall performance in predicting low-frequency trend stress and high-frequency dynamic stress. The prediction error of the fatigue utilization factors at key positions is controlled within 10%, and an adequate safety margin is maintained for high-risk measuring points. The proposed method provides an effective solution for structural health monitoring and fatigue life evaluation of critical components of bogie frames.