基于BO-CNN-LSTM的轨道车辆车体横向振动状态识别

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中文摘要：轨道车辆实际线路运行中振动状态是评价车辆运行稳定性、乘坐舒适性及安全运营的重要信息,但运行时面临多变工况条件会造成轨道车辆振动状态实时测量困难的问题.针对这一挑战,提出一种融合贝叶斯优化算法(BO)、卷积神经网络(CNN)和长短期记忆网络(LSTM)的车辆振动状态识别新方法.通过对测量得到的振动相关数据进行归一化预处理;利用处理后的数据以转向架横向速度及横摆角速度作为模型的输入,以车体横向加速度及横摆角加速度作为模型的输出,采用CNN学习并提取转向架横向速度及横摆角速度的波形特征,将相应的特征输入LSTM;同时利用BO优化得到LSTM模型的最优超参数配置,进而输出车体横向加速度及横摆角加速度,实现实时状态识别轨道车辆车体横向振动状态.考虑到轨道车辆实际运行的工况特点,采用3个评价指标来分析模型有效性,并与其他经典的方法进行对比分析.研究结果表明,相较于传统的BP神经网络和CNN-LSTM模型,通过BO-CNN-LSTM模型实时识别车体横向振动状态能有效降低误差,准确性最高,并且功率谱密度波形也最为接近.面对不同车速及载荷等工况条件,模型的平均绝对误差和均方根误差分别在0.019和0.029以下,拟合度均在0.99以上,验证了BO-CNN-LSTM模型用于轨道车辆车体横向振动状态识别的有效性.研究结果为轨道车辆运行状态实时识别的研究提供了新思路.

外文标题：State identification of lateral vibration of rail vehicle body based on BO-CNN-LSTM

外文摘要：The vibration state of rail vehicles in actual line operation is important information for evaluating the stability,ride comfort and safe operation of vehicles.However,it is difficult to measure the real-time vibration state of rail vehicles due to the variable operating conditions.To solve this challenge,a new method of vehicle vibration state identification based on Bayesian optimization algorithm (BO),convolutional neural network (CNN) and long short-term memory network (LSTM) is proposed.The measured vibration-related data are preprocessed by normalization.The lateral velocity and yaw velocity of the bogie in the processed data are used as inputs to the model,and the lateral acceleration and yaw acceleration of the vehicle body are taken as outputs of the model.The CNN is utilized to learn and extract the waveform characteristics of lateral velocity and yaw velocity of the bogie,and the corresponding characteristics are input into LSTM.Meanwhile,the BO optimization is adopted to obtain the optimal hyperparameter configuration of the LSTM model.Then,the lateral acceleration and yaw acceleration of the vehicle body are output to achieve the real-time state identification of the lateral vibration state of the rail vehicle body.Considering the characteristics of the actual operating conditions of rail vehicles,three performance indicators are applied to analyze the effectiveness of the model.Moreover,it is compared with other classical methods.The results show that compared with the traditional BP neural network and CNN-LSTM model,the real-time identification of the lateral vibration state of the vehicle body through the BO-CNN-LSTM model can effectively reduce the error,attain the highest accuracy and exhibit spectral density waveforms closest to the true values.In the face of operating conditions such as different vehicle speeds and loads,the average absolute error and root mean square error of the model are below 0.019 and 0.029 respectively.Additionally,the coefficient of determination is above 0.99.This verifies the effectiveness of the BO-CNN-LSTM model for the lateral vibration state identification of the vehicle body.The research results provide new perspectives for the real-time identification of the operational state of rail vehicles.

外文关键词：

rail vehicleslateral vibration of vehicle bodyBayesian optimization algorithmconvolutional neural networklong short-term memory networkstate identification

作者：

周军超、刘乙翰、陈奥、章杰

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作者单位：

四川轻化工大学机械工程学院,四川自贡 643000

长沙理工大学汽车与机械工程学院,湖南长沙 410114

四川警察学院智能警务四川省重点实验室,四川泸州 646000

关键词：

轨道车辆车体横向振动贝叶斯优化算法卷积神经网络长短期记忆网络状态识别

基金：

国家自然科学基金资助项目智能警务四川省重点实验室开放课题资助项目泸州市科技计划资助项目

项目编号：

12002123ZNJW2023KFQN0052023JYJ066

出版年：

2024

DOI：

10.19713/j.cnki.43-1423/u.T20231987

铁道科学与工程学报

中南大学中国铁道学会

铁道科学与工程学报

CSTPCD北大核心EI

影响因子：0.837

ISSN：1672-7029

年,卷(期)：2024.21(9)

参考文献量8