Hu Zhongshuo, Yang Jianwei, Yao Dechen, Wang Jinhai, Bai Yongliang
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.
Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.
Entropy (Basel). 2021 May 25;23(6):660. doi: 10.3390/e23060660.
In the signal processing of real subway vehicles, impacts between wheelsets and rail joint gaps have significant negative effects on the spectrum. This introduces great difficulties for the fault diagnosis of gearboxes. To solve this problem, this paper proposes an adaptive time-domain signal segmentation method that envelopes the original signal using a cubic spline interpolation. The peak values of the rail joint gap impacts are extracted to realize the adaptive segmentation of gearbox fault signals when the vehicle was moving at a uniform speed. A long-time and unsteady signal affected by wheel-rail impacts is segmented into multiple short-term, steady-state signals, which can suppress the high amplitude of the shock response signal. Finally, on this basis, multiple short-term sample signals are analyzed by time- and frequency-domain analyses and compared with the nonfaulty results. The results showed that the method can efficiently suppress the high-amplitude components of subway gearbox vibration signals and effectively extract the characteristics of weak faults due to uniform wear of the gearbox in the time and frequency domains. This provides reference value for the gearbox fault diagnosis in engineering practice.
在实际地铁车辆的信号处理中，轮对与钢轨接头间隙之间的冲击对频谱有显著的负面影响。这给齿轮箱的故障诊断带来了很大困难。为解决这一问题，本文提出一种自适应时域信号分割方法，该方法利用三次样条插值对原始信号进行包络。提取钢轨接头间隙冲击的峰值，以实现车辆匀速行驶时齿轮箱故障信号的自适应分割。将受轮轨冲击影响的长时间不稳定信号分割成多个短期稳态信号，可抑制冲击响应信号的高幅值。最后，在此基础上，通过时域和频域分析对多个短期样本信号进行分析，并与无故障结果进行比较。结果表明，该方法能有效抑制地铁齿轮箱振动信号的高幅值成分，并能在时域和频域中有效提取由于齿轮箱均匀磨损导致的微弱故障特征。这为工程实践中的齿轮箱故障诊断提供了参考价值。
