Noise and Vibration Control ›› 2022, Vol. 42 ›› Issue (2): 108-113.

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Fault feature extraction method of gear root crack based on stochastic resonance and graph fourier transform theory

  

  • Received:2021-03-11 Revised:2021-06-25 Online:2022-04-18 Published:2022-04-18
  • Contact: liu xinchang

基于随机共振及图谱理论的齿轮故障特征提取

刘新厂1,孙琦   

  • 通讯作者: 刘新厂

Abstract: Abstract:. The working environment of locomotive gearbox is very bad, and the gear is easy to be damaged. Gear box root crack damage detection is an effective way to ensure the safe operation of the train. In this paper, a fault extraction method based on stochastic resonance and spectrum theory is proposed. Firstly, the complex Morlet wavelet comb filter is used to demodulate the original signal; Then, the spectrum theory is used to process the simulation signal and extract the impact signal components contained in the signal; Finally, the stochastic resonance method is used to process the extracted impulse signal to eliminate the noise interference and enhance the impulse component in the signal. The simulation data are processed by using the proposed method, which proves the effectiveness of the proposed method.

摘要: 机车齿轮箱齿轮工作环境恶劣极易出现齿轮损伤。齿轮箱齿根裂纹损伤检测是保证列车安全运行的有效措施。本文提出了一种基于随机共振以及图谱理论相结合的齿根裂纹故障提取方法。基于随机共振以及图谱理论的故障提取方法具体过程如下,首先,运用复Morlet小波梳状滤波器对原始信号进行解调处理;然后,运用图谱理论方法对仿真信号进行处理提取信号中含有的冲击信号成分;最后利用随机共振方法对提取的冲击信号进行处理,达到消除信号中噪声干扰增强信号中冲击成分的目的。运用本文提出方法对仿真数据进行处理,证明了本文提出方法的有效性。

关键词: 机车齿轮箱, 齿根裂纹, 随机共振, 图谱理论