Research on the air brake system of heavy haul trains based on neural network

Abstract

To achieve precise prediction of the air pressure changes in the critical components of the air brake system, including brake pipes (BP), auxiliary reservoirs (AR), and brake cylinders (BC), this paper proposed a neural network-based air brake model developed from the brake application and release experimental data with different brake applications (50, 70, 100, and 150 kPa) conducted on a heavy haul train (HHT) test platform. The physical structures of the air brake system and the working mechanisms of a neural network are introduced, and the neural network-based air brake model scheme is presented by analysing the variation of air pressure within the air brake system under typical braking conditions (50 kPa). Besides, by comparing with other models, the superiority of the proposed model is verified, and the proposed model achieves higher computational efficiency, which increased by about 2900 times compared to the fluid dynamic model under the same conditions. Then, the model is applied to the calculation of the longitudinal dynamics of a 10,000-ton HHT, and the results demonstrate that the proposed model could serve as a promising tool for solving the braking excitation problem of longitudinal dynamics of trains and the study of intelligent braking control of trains.

KEYWORDS:

• Heavy haul train
• air brake system
• machine learning
• neural network
• numerical simulation

Acknowledgements

The authors would like to thank the Research Institute of Rail Transit and Electric Traction Technology and the Institute for Orbital Transportation Aerodynamics and Collision Safety Technology Research for providing equipment and materials to this project, and Zhuzhou CRRC Times Electric Co., Ltd for the cooperation and support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: the Hunan Provincial Science and Technology Talent Recruitment Project [grant number2022TJ-N14] and the National Natural Science Foundation of China [grant number 52072414].