A neural network driving curve generation method for the heavy-haul train

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

External organisations

  • Beijing Jiaotong University
  • School of Electronics

Abstract

The heavy-haul train has a series of characteristics, such as the locomotive traction properties, the longer length of train, and the nonlinear train pipe pressure during train braking. When the train is running on a continuous long and steep downgrade railway line, the safety of the train is ensured by cycle braking, which puts high demands on the driving skills of the driver. In this article, a driving curve generation method for the heavy-haul train based on a neural network is proposed. First, in order to describe the nonlinear characteristics of train braking, the neural network model is constructed and trained by practical driving data. In the neural network model, various nonlinear neurons are interconnected to work for information processing and transmission. The target value of train braking pressure reduction and release time is achieved by modeling the braking process. The equation of train motion is computed to obtain the driving curve. Finally, in four typical operation scenarios, comparing the curve data generated by the method with corresponding practical data of the Shuohuang heavy-haul railway line, the results show that the method is effective.

Details

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalAdvances in Mechanical Engineering
Volume8
Issue number5
Early online date4 May 2016
Publication statusE-pub ahead of print - 4 May 2016

Keywords

  • Driving curve, Heavy-haul train, Modeling, Neural network, Operation scenarios

ASJC Scopus subject areas