摘要
为准确预测高速铁路无砟轨道不平顺发展趋势,结合改进粒子群优化算法(IPSO)和长短期记忆网络(LSTM)搭建轨道质量指数(TQI)预测模型(IPSO-LSTM),将轨检车获取的各项轨道不平顺检测数据经过异常值剔除和降噪等预处理,形成TQI时间序列数据,利用标准化处理后的TQI样本开展模型训练和不平顺预测分析,并与其他常用预测方法进行对比。研究结果表明:长短期记忆网络具有记忆历史信息的功能,能较好地预测非线性时间序列的发展趋势。采用IPSO可解决LSTM中隐含层神经元个数和学习速率等超参数难以选取的问题,增强了模型预测性能。针对某高速铁路K5+000~K7+000区段长达4年的轨道不平顺检测数据,IPSO-LSTM模型对TQI的预测精度最高,自回归积分滑动平均模型(ARIMA)次之,BP神经网络和灰色模型相差不大。IPSO-LSTM的平均相对误差和均方根误差分别为0.035和0.135,与ARIMA,BP神经网络和灰色模型相比,其平均相对误差降低22%~45%,均方根误差降低26%~45%,验证了IPSO-LSTM模型用于无砟轨道不平顺预测的有效性。IPSO-LSTM预测模型有望为了解和掌握高铁无砟轨道质量发展提供一种新的技术支撑。
To accurately predict the development trend of slab track irregularity for high-speed railways,a track quality index(TQI) prediction model(IPSO-LSTM) was built to improve particle swarm optimization(IPSO)and long short-term memory network(LSTM).The track irregularity detection data obtained by the track inspection vehicles were preprocessed by outlier elimination and noise reduction to generate time series data of TQI.Then the standardized TQI samples were used to carry out model training and irregularity prediction.Comparisons with other commonly used prediction methods were made.The results show that long short-term memory network has the function of memorizing historical information and can predict the development trend of nonlinear time series well.The difficulty faced by LSTM in hyperparameter selection,such as the number of hidden layer neurons and learning rate is solved.The model prediction performance is enhanced by adopting IPSO.For the track irregularity data at K5+000 to K7+000 section of a high-speed railway for 4 years,the IPSOLSTM model has the highest prediction accuracy for TQI,followed by the autoregressive integral moving average model(ARIMA).And the BP neural network is not much different from the grey model.The average relative error and root mean square error of IPSO-LSTM are 0.035 and 0.135,respectively.Compared with ARIMA,BP neural network and gray model,the average relative error is reduced by 22%~45%,and the root mean square error is reduced by 26%~45% for IPSO-LSTM.It verifies the validity of IPSO-LSTM model for predicting the irregularity of slab tracks.The IPSO-LSTM model is expected to provide a new technical support for controlling the development of slab track quality for high-speed railways.
作者
杜威
任娟娟
许雪山
曾学勤
何庆
DU Wei;REN Juanjuan;XU Xueshan;ZENG Xueqin;HE Qing(MOE Key Laboratory of High-speed Railway Engineering,Southwest Jiaotong University,Chengdu 610031,China;School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China;School of Traffic and Transportation Engineering,Changsha University of Science and Technology,Changsha 410114,China)
出处
《铁道科学与工程学报》
EI
CAS
CSCD
北大核心
2023年第3期753-761,共9页
Journal of Railway Science and Engineering
基金
国家重点研发计划项目(2021YFF0502100)
国家自然科学基金资助项目(52022085)
高速铁路无砟轨道设计与维护四川省青年科技创新研究团队项目(2022JDTD0015)。
关键词
无砟轨道
轨道质量指数
不平顺预测
长短期记忆网络
改进粒子群优化算法
slab track
track quality index
irregularity prediction
long and short-term memory network
improved particle swarm optimization
作者简介
通信作者:任娟娟(1983-),女,山西霍州人,教授,博士,从事高速铁路无砟轨道结构设计理论与损伤机理研究,E−mail:jj.ren@swjtu.edu.cn。
