A neural network driving curve generation method for the heavy-haul train
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 ensure...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2016-05-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/1687814016647883 |
| _version_ | 1818275061122990080 |
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| author | Youneng Huang Litian Tan Lei Chen Tao Tang |
| author_facet | Youneng Huang Litian Tan Lei Chen Tao Tang |
| author_sort | Youneng Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T22:23:45Z |
| format | Article |
| id | doaj.art-14f3e1d020a84b5aa2fa0886ccb54e6b |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-12T22:23:45Z |
| publishDate | 2016-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-14f3e1d020a84b5aa2fa0886ccb54e6b2022-12-22T00:09:50ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402016-05-01810.1177/168781401664788310.1177_1687814016647883A neural network driving curve generation method for the heavy-haul trainYouneng Huang0Litian Tan1Lei Chen2Tao Tang3School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, UKState Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing, ChinaThe 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.https://doi.org/10.1177/1687814016647883 |
| spellingShingle | Youneng Huang Litian Tan Lei Chen Tao Tang A neural network driving curve generation method for the heavy-haul train Advances in Mechanical Engineering |
| title | A neural network driving curve generation method for the heavy-haul train |
| title_full | A neural network driving curve generation method for the heavy-haul train |
| title_fullStr | A neural network driving curve generation method for the heavy-haul train |
| title_full_unstemmed | A neural network driving curve generation method for the heavy-haul train |
| title_short | A neural network driving curve generation method for the heavy-haul train |
| title_sort | neural network driving curve generation method for the heavy haul train |
| url | https://doi.org/10.1177/1687814016647883 |
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