基于多结构数据驱动的车轮扁疤定量识别方法  

Quantitative identification method of wheel flats based on multi-structured data-driven

作　　者：钱新宇 谢清林 陶功权[1] 温泽峰[1] QIAN Xinyu;XIE Qinglin;TAO Gongquan;WEN Zefeng(State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University,Chengdu 610031,China)

机构地区：[1]西南交通大学轨道交通运载系统全国重点实验室,四川成都610031

出　　处：《浙江大学学报(工学版)》2025年第4期688-697,共10页Journal of Zhejiang University:Engineering Science

基　　金：国家自然科学基金资助项目(U21A20167,52475138,52002342);四川省自然科学基金青年科学基金资助项目(2022NSFSC1914).

摘　　要：为了快速、准确检测车轮扁疤,提出以不同结构数据为驱动载体的车轮扁疤定量识别方法.将合成的扁疤车轮数据作为车轮不圆激励输入地铁车辆−轨道刚柔耦合动力学模型,获取不同工况下的轴箱振动响应.对轴箱振动响应进行数据规整,制成不同结构形式的样本集,将它与速度信号融合输入多输入卷积神经网络(MCNN)模型进行训练,探究MCNN模型在不同数据结构输入下的性能差异.结果表明:相较于设置的其他输入数据结构,输入数据结构为时域、频域和时频域组合的MCNN模型识别性能最佳,平均绝对百分比误差与拟合度(R2)分别为1.947%和0.9978,耗时相对较低,单个样本为0.1579 ms.经典模型对比实验、速度信息消融实验和实测数据迁移学习实验的结果表明,输入数据结构为时域、频域和时频域组合的MCNN模型具有工程应用价值.An exploration was conducted into the performance of wheel flat recognition driven by various structured data to achieve efficient and accurate detection of wheel flats,and a new quantitative identification method was proposed.Synthetic wheel flats data were input into the metro vehicle−rail rigid-flexible coupling dynamics model as wheel out-of-roundness excitation to obtain axle box vibration responses under different working conditions.The axle box vibration responses were regulated into sample sets of various structural forms.The sample sets were fused with speed signals and input into a multi-input convolutional neural network(MCNN)model for training,and the differences in performance of MCNN model under different data structure inputs were explored.Results show that compared with other input data structures in the setup,MCNN model recognition performance is best when the input data structure is a combination of time domain,frequency domain and time-frequency domain,with mean absolute percentage error and R-squared coefficient(R2)reaching 1.947%and 0.9978 respectively,and relatively low time-consumption,with 0.1579 ms for a single sample.Results of the classical model comparison experiment,speed information ablation study,and real-data transfer learning experiment show that the MCNN model for engineering applications when the input data structure is a combination of time domain,frequency domain,and time-frequency domain.

关 键 词：车轮扁疤 定量识别 多结构数据样本集 多输入卷积神经网络 轴箱振动加速度 

分 类 号：U231[交通运输工程—道路与铁道工程]