基于时频信息融合驱动的车轮扁疤定量识别研究  

Quantitative Detection Method for Wheel Flats Driven by Time-Frequency Information Fusion

作　　者：钱新宇 谢清林 陶功权[1] QIAN Xinyu;XIE Qinglin;TAO Gongquan(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6100311

出　　处：《机械》2024年第12期26-35,共10页Machinery

基　　金：四川省自然科学基金(2022NSFSC1914)。

摘　　要：包络谱分析中,包络时域信号和包络频谱均可有效提取车轮扁疤特征。为结合这两种数据形式的优势,以实现高效准确的车轮扁疤定量识别,提出一种基于时频信息融合驱动的车轮扁疤定量识别方法。首先,通过构建地铁车辆-轨道刚柔耦合动力学模型获取车轮扁疤不同尺度工况下的轴箱振动响应。其次,对轴箱振动响应进行重叠采样和包络谱处理,制成两种不同数据形式的样本集。然后,对比两种不同模型架构下多输入卷积神经网络的识别准确度,着重研究最佳模型架构下多输入卷积神经网络模型的泛化性能和各部分样本集单独输入时的识别性能差异。结果表明:优化分析后模型的综合识别性能最佳,平均绝对百分比误差与拟合度R^(2)分别可达3.812%和0.990;处理随机未知数据时,相对误差不超过7.2%;虽然时效性有所下降,但仍满足在线监测时效性的需求。In the analysis of Envelope Spectrum(ES),both the envelope time-domain signal and the envelope frequency spectrum can effectively extract the features of wheel flats.To combine the advantages of these two data forms,we proposed a quantitative detection method for wheel flats driven by time-frequency information fusion.First,the gearbox vibration response under different scale conditions of wheel flats is obtained through constructing a metro vehicle–track rigid–flexible coupling dynamics model.Second,the gearbox vibration response is processed with overlapping sampling and ES analysis to generate two different types of data samples.Then,the accuracy of the Multi-input Convolutional Neural Network(MCNN)under two different model architectures is compared.The study focuses on analyzing the generalization of the MCNN model under the optimal model architecture and examining the differences in recognition performance when each part of the sample set is input separately.The results show that the model with optimized analysis has the best comprehensive recognition performance,with an average absolute percentage error of 3.812%and a coefficient of determination(R^(2))of 0.990.When dealing with random unknown data,the relative error does not exceed 7.2%.Although there is a decrease in timeliness,it still meets the requirements for online monitoring.

关 键 词：车轮扁疤 定量识别 包络谱 多输入卷积神经网络 

分 类 号：U231[交通运输工程—道路与铁道工程]