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

doi:10.3785/j.issn.1008-973X.2025.04.004

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (4): 688-697 DOI: 10.3785/j.issn.1008-973X.2025.04.004

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

Xinyu QIAN(

),Qinglin XIE,Gongquan TAO*(

),Zefeng WEN

State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, China

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Abstract

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 0.157 9 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.

Key words： wheel flats quantitative identification multi-structure data sample set multi-input convolutional neural network axlebox vibration acceleration

Received: 19 February 2024 Published: 25 April 2025

CLC:

U 231

Fund: 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）.

Corresponding Authors: Gongquan TAO E-mail: xyqian@my.swjtu.edu.cn;taogongquan@swjtu.edu.cn

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Cite this article:

Xinyu QIAN,Qinglin XIE,Gongquan TAO,Zefeng WEN. Quantitative identification method of wheel flats based on multi-structured data-driven. Journal of ZheJiang University (Engineering Science), 2025, 59(4): 688-697.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.04.004 OR https://www.zjujournals.com/eng/Y2025/V59/I4/688

基于多结构数据驱动的车轮扁疤定量识别方法

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0.157 9 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.

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

Fig.1 Flowchart of proposed method for quantitative identification of wheel flats

Tab.1 Sizes of simulated wheel flats

Fig.2 Synthesized data of wheel flats

Tab.2 Main structural parameters of simulation model

Fig.3 Measurement of wheel out-of-roundness

Fig.4 Vertical vibration acceleration of axle box

Fig.5 Vibration acceleration of axle box at two cases (time domain samples)

Fig.6 Vibration acceleration of axle box at two cases (frequency domain samples)

Fig.7 Example of two-channel frequency domain sample

Fig.8 Vibration acceleration of axle box at two cases (time-frequency domain samples)

Fig.9 Schematic structure of multi-input convolutional neural network model

Tab.3 Structural parameters of multi-input convolutional neural network model

Tab.4 Comparison of recognition performance with different input

Fig.10 Comparison of recognition performance of different models

Fig.11 Comparison of model recognition performance with or without speed information

Fig.12 Measurement of localized wheel defects

Fig.13 Measurement of axlebox vibration acceleration signals

Fig.14 Migration learning results at major speed level


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