作者：陈春俊1,2, 杨露1,2, 何智颖1, 周林春1
作者单位：1. 西南交通大学机械工程学院，四川 成都 610031;
2. 轨道交通运维技术与装备四川省重点实验室，四川 成都 610031
关键词：高速列车;隧道压力波预测模型;差分自回归滑动平均-BP神经网络组合模型;工况匹配算法;加权K最近邻算法
摘要：
为更精准地进行车内压力波动控制，需要预测高速列车通过隧道时车外隧道压力波的实时变化值。在对列车历史运行重复隧道压力波数据的分析基础上，采用工况匹配（WCM）与加权K最近邻（WKNN）算法从数据库中选取若干与本次工况相接近的运行状态数据，并根据相似程度确定数据权重，构建预测用的历史数据。分别采用差分自回归滑动平均(ARIMA)与BP神经网络（BPNN）模型对隧道压力波进行预测，并将两种预测结果并联考虑，形成ARIMA-BPNN隧道压力波组合预测模型。利用武广客运专线某隧道压力波实测数据进行仿真。仿真结果表明：与WCM-WKNN-ARIMA及WCM-WKNN-BPNN单一预测模型以及WCM-ARIMA-BPNN组合预测模型相比，所建立组合模型能有效对隧道压力波进行预测，且能够取得更高精度的预测结果。
Research on tunnel pressure wave prediction model of high-speed train based on ARIMA-BP neural network
CHEN Chunjun1,2, YANG Lu1,2, HE Zhiying1, ZHOU Linchun1
1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China;
2. Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu 610031, China
Abstract: In order to control the pressure fluctuation inside the vehicle more accurately, it is necessary to predict the real-time variation of the pressure wave outside the tunnel when the high-speed train passes through the tunnel. Based on the analysis of repeated tunnel pressure wave data of train historical operation, several running state data close to this operating condition are selected from the database by using working condition matching (WCM) and weighted K-nearest neighbor (WKNN) algorithm, and the data weight is determined according to the degree of similarity to construct the historical data for prediction. Autoregressive integrated moving average (ARIMA) and back propagation neural network (BPNN) model are used to predict tunnel pressure wave respectively, and the two prediction results are considered in parallel to form an ARIMA-BPNN tunnel pressure wave combination prediction model. The simulation is carried out by using the measured data of pressure wave in a tunnel of Wuhan-Guangzhou passenger dedicated line. The simulation results show that compared with the single prediction model of WCM-WKNN-ARIMA and WCM-WKNN-BPNN and the combined prediction model of WCM-ARIMA-BPNN, the combined model can effectively predict the tunnel pressure wave and obtain more accurate prediction results.
Keywords: high-speed train;prediction model of tunnel pressure wave;ARIMA-BPNN combination model;WCM algorithm;WKNN algorithm
2021, 47(10):80-86 收稿日期: 2020-09-27;收到修改稿日期: 2020-11-24
基金项目: 国家自然科学基金资助项目(51975487)；轨道交通运维技术与装备四川省重点实验室开放基金课题（2019YW003）
作者简介: 陈春俊（1967-），男，四川蒲江县人，教授/博导，博士，研究方向为轨道交通设备性能测试、诊断与控制
参考文献
[1] 李人宪. 高速列车气动影响[M]. 北京: 中国铁道出版社, 2016.
[2] 田红旗. 中国高速轨道交通空气动力学研究进展及发展思考[J]. 中国工程科学, 2015, 17(4): 30-41
[3] 何德华. 高速列车车内空气压力舒适度标准的研究[J]. 铁道机车车辆, 2016, 36(3): 1-6
[4] LIU T H, CHEN M Y, CHEN X D, et al. Field test measurement of the dynamic tightness performance of high-speed trains and study on its influencing factors[J]. Measurement, 2019, 138: 602-613
[5] ZHANG T T, WANG F. The air tightness test method of the high speed train[J]. Applied Mechanics and Materials, 2012, 229-231: 482-486
[6] 李玉洁, 梅元贵. 动车组车辆气密性指标的初步探讨[J]. 铁道机车车辆, 2009, 29(2): 31-35
[7] 王前选, 胡哲龙, 梁习锋, 等. 轨道车辆内部压力与车体气密性、外部压力的关系[J]. 交通运输工程学报, 2018, 18(4): 103-111
[8] 李树典, 周新喜. CRH2型200 km/h动车组车内压力波动控制研究[J]. 机车电传动, 2009(2): 6-7
[9] 罗禄林. 高速列车车内压力保护装置在隧道压力波下的适应性研究[D]. 兰州: 兰州交通大学, 2016.
[10] 王宗昌. 主动式与被动式车内压力保护系统对比分析[J]. 铁道车辆, 2017, 55(6): 30-32
[11] 李新, 陈春俊. 基于模糊PID的高速列车车内压力主被动控制[J]. 中国测试, 2020, 46(1): 105-109
[12] BARON A, MOLTENI P, VIGEVANO L. High-speed trains: Prediction of micro-pressure wave radiation from tunnel portals[J]. Journal of Sound & Vibration, 2006, 296(1-2): 59-72
[13] UYSTEPRUYST D, WILLIAM-LOUIS M, CREUSÉ E, et al. Efficient 3D numerical prediction of the pressure wave generated by high-speed trains entering tunnels[J]. Computers & Fluids, 2011, 47(1): 165-177
[14] WILLIAM-LOUIS M, TOURNIER C. A wave signature based method for the prediction of pressure transients in railway tunnels[J]. Journal of Wind Engineering & Industrial Aerodynamics, 2005, 93(6): 521-531
[15] 陈春俊, 聂锡成, 张洁. 基于ARMA模型的高速列车隧道压力波预测研究[J]. 中国测试, 2013, 39(6): 5-9
[16] 屈国庆. 综合舒适度模拟试验台气压控制系统设计与算法研究[D]. 成都: 西南交通大学, 2016.
[17] JADEVICIUS A, HUSTON S. ARIMA modelling of Lithuanian house price index[J]. International Journal of Housing Markets and Analysis, 2015, 8(1): 135-147
[18] 时晓晴. 基于ARIMA-RBF神经网络的库存预测模型的研究与实现[D]. 南京: 东南大学, 2019.
[19] 许明燕. 基于ARIMA模型和BP神经网络模型的江苏省GDP预测分析[D]. 济南: 山东大学, 2020.
[20] PAN L N, FENG X S, SANG F W, et al. An improved back propagation neural network based on complexity decomposition technology and modified flower pollination optimization for short-term load forecasting[J]. Neural Computing and Applications, 2019, 31(7): 2679-2697
[21] ARIF M, ISHIHARA T, INOOKA H. Incorporation of experience in iterative learning controllers using locally weighted learning[J]. Automatica, 2001, 37(6): 881-888
