Research on strong and weak evaluation method of air brake for intelligent driving heavy haul train

WANG Feikuan; JIANG Jie; ZHANG Zhengfang; LUO Yuan

doi:10.13890/j.issn.1000-128X.2022.05.016

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Research on strong and weak evaluation method of air brake for intelligent driving heavy haul train

Electric Traction and Control | 更新时间：2024-08-02

- Research on strong and weak evaluation method of air brake for intelligent driving heavy haul train
- Electric drive for locomotives Issue 5, Pages: 109-115(2022)
- 作者机构：
  
  1.国家能源集团包神铁路集团公司，内蒙古包头　　014010
  2.株洲中车时代电气股份有限公司，湖南株洲;412001
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2022.05.016
  CLC： U292.92⁺1
- Published：10 September 2022，
  
  Received：11 October 2021，
  
  Revised：10 August 2022，
- 稿件说明：
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WANG Feikuan, JIANG Jie, ZHANG Zhengfang, et al. Research on strong and weak evaluation method of air brake for intelligent driving heavy haul train. [J]. Electric drive for locomotives (5):109-115(2022)
DOI：

WANG Feikuan, JIANG Jie, ZHANG Zhengfang, et al. Research on strong and weak evaluation method of air brake for intelligent driving heavy haul train. [J]. Electric drive for locomotives (5):109-115(2022) DOI： 10.13890/j.issn.1000-128X.2022.05.016.

摘要

“3+0”编组的重载智能驾驶列车在循环空气制动控制过程中，由于空气制动力的大小受到线路条件、车辆载荷、闸瓦摩擦特性、管路差异性和空气制动间歇工作特性等多种因素影响，难以对列车进行准确规划和精准控制，甚至存在行车安全隐患。针对这一问题，文章提出一种基于改进粒子群-支持向量机（Improved Particle Swarm Optimization-Support Vector Machine

IPSO-SVM）的空气制动力强弱预测方法，采用IPSO对SVM的参数进行最优搜索，将影响空气制动力大小的主要因素作为SVM的输入，对空气制动力强弱进行评估，并基于神朔铁路现场实测数据进行了分析验证，验证结果显示文章所提出方法的预测精度可达90%以上，证明了该方法的合理性和有效性，验证结果表明该方法具有良好的实际工程应用价值。

Abstract

In the process of circulating air brake control of intelligent driving heavy haul train with the formation of 3+0

because the magnitude of air braking force is affected by various factors such as railway line conditions

vehicle load

friction characteristics of brake shoes

differences in pipelines

and intermittent working characteristics of air brakes

it is difficult to accurately plan and control trains

even causing potential safety hazards. To solve this problem

this paper proposed a prediction method for the strong and weak of air braking force based on IPSO-SVM (Improved Particle Swarm Optimization-Support Vector Machine). IPSO was used to optimize the parameters of SVM. The main factors affecting the magnitude of air braking force were used as the input of SVM to evaluate the strong and weak of air braking force

and the analysis and verification were carried out based on the measured data of Shenmu-Shuozhou railway. The results show that the prediction accuracy of the proposed method can reach more than 90%

which verifies the method is rational and effective

having good and practical value in engineering application.

关键词

重载列车智能驾驶空气制动改进粒子群支持向量机

Keywords

heavy haul trainintelligent drivingair brakingIPSOSVM

references

肖家博, 尚敬. 货运机车自动驾驶系统研究与设计[J]. 控制与信息技术, 2018(6): 38-43.

XIAO Jiabo, SHANG Jing. Research and design of freight locomotives automatic train operation system[J]. Control and Information Technology, 2018(6): 38-43.

刘鹏飞, 王开云, 张大伟. 牵引及制动操纵对重载机车轮轨动力作用的影响[J]. 中国铁道科学, 2017, 38(2): 96-104.

LIU Pengfei, WANG Kaiyun, ZHANG Dawei. Influence of traction and braking operation on wheel-rail dynamic interaction for heavy haul locomotive[J]. China Railway Science, 2017, 38(2): 96-104.

PIECHOWIAK T. Verification of pneumatic railway brake models[J]. Vehicle System Dynamics, 2010, 48(3): 283-299.

DU Kaibing, WANG Zhanchao, ZHANG Zhengfang. State observation and parameter identification for autonomous heavy haul train[C]//IEEE. 2020 IEEE Vehicle Power and Propulsion Conference (VPPC). Gijon: IEEE, 2020: 1-5.

王悉. 基于机器学习的重载列车智能驾驶方法研究[D]. 北京: 北京交通大学, 2017.

WANG Xi. Machine learning based intelligent operation methods for heavy haul train[D]. Beijing: Beijing Jiaotong University, 2017.

宋健, 魏伟. 重载列车纵向动力学仿真模型的有效性研究[J]. 大连交通大学学报, 2019, 40(3): 23-29.

SONG Jian, WEI Wei. Validity of simulation model of heavy haul train longitudinal dynamics[J]. Journal of Dalian Jiaotong University, 2019, 40(3): 23-29.

张中央. 列车牵引计算[M]. 北京: 中国铁道出版社, 2006.

ZHANG Zhongyang. Train traction calculation[M]. Beijing: China Railway Publishing House, 2006.

吴文江, 陈其工, 高文根. 基于PSO优化参数的最小二乘支持向量机短期负荷预测[J]. 重庆理工大学学报(自然科学版), 2016, 30(3): 112-115.

WU Wenjiang, CHEN Qigong, GAO Wengen. Parameter selection for LSSVM based on PSO to short-term power load forecasting[J]. Journal of Chongqing University of Technology (Natural Science), 2016, 30(3): 112-115.

杜晓敏, 王立德, 李召召, 等. 基于波形特征提取和FA-Grid SVM的MVB故障诊断[J]. 机车电传动, 2020(2): 71-74.

DU Xiaomin, WANG Lide, LI Zhaozhao, et al. MVB fault diagnosis based on waveform feature extraction and FA-Grid SVM[J]. Electric Drive for Locomotives, 2020(2): 71-74.

林国汉, 章兢, 刘朝华, 等. 改进粒子群算法优化扩展卡尔曼滤波器电机转速估计[J]. 计算机应用研究, 2013, 30(7): 2003-2006.

LIN Guohan, ZHANG Jing, LIU Chaohua, et al. Speed estimation of motor using improved particle swarm algorithm optimized extended Kalman filter[J]. Application Research of Computers, 2013, 30(7): 2003-2006.

王振武, 孙佳骏, 尹成峰. 改进粒子群算法优化的支持向量机及其应用[J]. 哈尔滨工程大学学报, 2016, 37(12): 1728-1733.

WANG Zhenwu, SUN Jiajun, YIN Chengfeng. A support vector machine based on an improved particle swarm optimization algorithm and its application[J]. Journal of Harbin Engineering University, 2016, 37(12): 1728-1733.

冯征. 重载货车空气制动系统建模及仿真[D]. 成都: 西南交通大学, 2016.

FENG Zheng. Modeling and simulation of air brake system of heavy truck[D]. Chengdu: Southwest Jiaotong University, 2016.

何志浩, 樊嘉慧, 梁晖. 半实物列车制动系统仿真平台结构设计及空气制动模型解析[J]. 城市轨道交通研究, 2021, 24(6): 24-28.

HE Zhihao, FAN Jiahui, LIANG Hui. Structure design and aerodynamic model analysis of semi-physical train braking system simulation platform[J]. Urban Mass Transit, 2021, 24(6): 24-28.

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