基于卷积神经网络回归分析和角域同步平均的地铁列车车轮多边形辨识

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Wenjing Sun , Xuan Geng , David J. Thompson , Tengfei Wang , Jinsong Zhou , Jin Zhang
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引用次数: 0

摘要

车轮多面化是车轮失圆的一种形式,近年来已成为城市轨道交通列车上普遍存在的问题。它导致车辆和轨道的动态响应显著增加,振动和噪声水平高,结构疲劳。本文提出了一种利用卷积神经网络(CNN)回归分析识别车轮多边形阶数及其有效值的创新方法。首先,采用角域同步平均(ADSA)方法对轴箱上测量的加速度信号进行处理,有效分离出信号中与车轮多边形化相关的特征信息;为了提取全面的车轮多边形信息,采用了一种特征融合方法,对时域和频域特征进行融合。然后,建立CNN回归模型并进行训练,并利用车辆振动实测数据和现场试验中车轮多边形化实测数据进行验证。对不同的识别方法进行了对比分析,包括对不同的预处理方法和机器学习模型进行了比较,验证了本文提出的方法的有效性。验证结果表明,该方法对高达25阶的车轮多边形具有较高的识别精度。总体平均均方根误差值为2.0 dB。最后,讨论了车轮多边形化条件、轨道刚度和速度波动对辨识精度的影响。结果表明,该方法在不同条件下具有较强的识别能力,在列车运行的复杂情况下具有广泛的适用性和准确性。该研究有助于推进车轮多边形检测领域的发展,为铁路系统的应用提供可靠、有效的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On-board identification of wheel polygonization of metro trains based on convolutional neural network regression analysis and angular-domain synchronous averaging
Wheel polygonization, a form of wheel out-of-roundness, has become a common problem on trains of urban rail transit systems in recent years. It results in a significant increase of the dynamic responses of both the vehicle and the track, high vibration and noise levels, and structural fatigue. This paper proposes an innovative method for identifying wheel polygonization orders and their effective values using convolutional neural network (CNN) regression analysis. First, the acceleration signal measured on the axle box has been processed with the angular-domain synchronous averaging (ADSA) method, effectively separating the characteristic information associated with wheel polygonization within the signal. To extract comprehensive wheel polygonization information, a feature fusion method is employed, integrating features from both the time and frequency domain. Then, a CNN regression model is established and trained, with validation conducted using measured data of vehicle vibration and the wheel polygonization measured during field tests. Comparative analysis with different identification methods is performed, including a comparison of different preprocessing methods and machine learning models, which demonstrates the effectiveness of the proposed method in this study. The verification results show that the proposed method achieves high identification accuracy for wheel polygonization up to the 25th order. The overall average root mean square error value is 2.0  dB. Finally, the influence of wheel polygonization conditions, track stiffness, and speed fluctuation on the identification accuracy is discussed. The results show the proposed method exhibits robust identification capacity under varying conditions, which indicates its wide application and accuracy in complex situations during train service. This research contributes to advancing the field of wheel polygonization detection, offering a reliable and effective solution for application in railway systems.
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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