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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
- 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.
- Subjects :
- Mechanical engineering and machinery
TJ1-1570
Subjects
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