The invention discloses a railway vehicle wheel flat monitoring method, which specifically comprises the following steps of: acquiring an axle box vibration acceleration signal and carrying out singular value difference spectrum noise reduction, enveloping the axle box vibration acceleration signal after noise reduction, extracting a low-frequency vibration signal caused by a wheel flat fault, carrying out fast Fourier transform on the extracted low-frequency vibration signal, and carrying out fast Fourier transform on the extracted low-frequency vibration signal. acquiring an amplitude-frequency diagram, calculating to obtain a wheel flat characteristic frequency through a train running speed, comparing and analyzing the amplitude-frequency diagram and the wheel flat characteristic frequency, and diagnosing whether the wheel has a flat fault or not; and extracting amplitude values corresponding to 1-4 times of frequency of flat characteristic frequency and corresponding vehicle speeds from the wheels diagnosed to have flat faults, and inputting the amplitude values and the vehicle speeds into the trained BP neural network model to estimate the flat length of the wheels. The invention provides a method based on combination of singular value differential spectrum noise reduction and the BP neural network, the wheel flat fault can be quickly identified, the flat length is quantitatively estimated, and the method has good practicability.

    本发明公开了一种铁道车辆车轮扁疤监测方法,具体如下:采集轴箱振动加速度信号并做奇异值差分谱降噪,对降噪后的轴箱振动加速度信号包络,提取车轮扁疤故障引起的低频振动信号,对提取的低频振动信号进行快速傅里叶变换,得到幅频图,同时通过列车运行速度,计算得到车轮扁疤特征频率,将幅频图与车轮扁疤特征频率进行对比分析,诊断车轮是否存在扁疤故障;对诊断存在扁疤故障的车轮提取扁疤特征频率1‑4倍频所对应的幅值和对应车速输入到训练后的BP神经网络模型中对车轮扁疤长度进行估算。本发明提出基于奇异值差分谱降噪与BP神经网络相结合的方法,能快速识别车轮扁疤故障,并对扁疤长度进行定量估算,具有良好的实用性。


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    Title :

    Railway vehicle wheel flat monitoring method


    Additional title:

    一种铁道车辆车轮扁疤监测方法


    Contributors:
    LI DAZHU (author) / CHI MAORU (author) / LIANG SHULIN (author) / NIU JIANG (author) / XU WENTIAN (author) / PENG LANG (author)

    Publication date :

    2022-02-18


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    B61L Leiten des Eisenbahnverkehrs , GUIDING RAILWAY TRAFFIC



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