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Analysis and prediction of double-carriage train wheel wear based on SIMPACK and neural networks

Analysis and prediction of double-carriage train wheel wear based on SIMPACK and neural networks

Authors :
Shuwen Wang
Hao Guo
Siyuan Zhang
David Barton
Peter Brooks
Source :
Advances in Mechanical Engineering, Vol 14 (2022)
Publication Year :
2022
Publisher :
SAGE Publishing, 2022.

Abstract

Wheel and rail wear seriously affects the safety and reliability of train operations. In this study single-carriage and double-carriage models considering the connecting unit of a high-speed train are developed to investigate the normal forces, lateral forces, and lateral displacements of wheelsets. Based on the results from these models, the Archard wear model is employed to predict the wheel wear. In addition, based on the daily measured data, a nonlinear autoregulatory (NAR) model and a wavelet neural network (WNN) model are developed to predict the wheel wear over a longer time period. The simulation results show that, compared with the single-carriage model, the normal forces, lateral forces, and lateral displacements of the wheelsets close to the connecting unit in the double-carriage model increase to a certain extent dependent on the speed. The wheel wear predictions show that the wheel wear on the wheelsets near the connecting unit is slightly larger than on the wheelsets far from the connecting unit. Based on the mean square error, the NAR model has somewhat better performance in the wheel wear prediction than the WNN model. The research results represent an important contribution to the maintenance and safe operation of high-speed trains.

Details

Language :
English
ISSN :
16878140 and 16878132
Volume :
14
Database :
Directory of Open Access Journals
Journal :
Advances in Mechanical Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.7747b7c63584fe29c7bad88c96bce88
Document Type :
article
Full Text :
https://doi.org/10.1177/16878132221078491