Inversion estimation and field verification of rail vibration acceleration based on vibration acceleration of track slabs

HE Qing; ZENG Chuqi; WANG Qihang; FU Bin; WU Jun; WANG Ping

doi:10.11860/j.issn.1673-0291.20230041

Abstract：To explore the spatiotemporal correlation patterns between high-speed railway track slabsand rails, this paper proposes a Variational Mode Decomposition-Transformer (VMD-T) inversion model, which estimates rail vibration acceleration from decomposed track slab vibration accelerationdata. Firstly, the data undergoes preprocessing, including vibration endpoint detection using a dual-threshold method to separate the vibration signals from silent and interference signals. The extracted vibration signals are integrated and fed into the VMD-T model. Subsequently, the VMD model breaks down the track slab vibration acceleration data into various sub-modes. A grid search identifies the sub-modes with the highest correlation coefficients with rail vibration acceleration, aiming to sim-plify the original dataset and reduce its non-stationarity, thereby enhancing the Transformer model's feature extraction capabilities. Following this, the Transformer model undertakes inversion estimation training on the identified track slab vibration acceleration sub-modes and rail vibration acceleration data. Finally, the model's utility is demonstrated through inversion estimation trials using actual vibra-tion acceleration data from the track slabs and rails of a specific intercity high-speed railway. Field tests on high-speed rail reveal that, compared to a standalone Transformer model, the VMD-T model con-siderably improves in terms of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the determination coefficient (R2_score) by approximately 20％, 11％, and 48.1％ respectively, indi-cating a stronger capability in feature learning and inversion estimation. This study preliminarily achieves non-contact estimation monitoring of rail vertical vibration acceleration magnitudes.