Detection of Weak Fault Signals for EMU Bearings Under Strong Noise

SUN Xin-wei; JI Ai-min; CHEN Xi-hui; LIN Xin-hai; XU Xing

doi:10.12263/DZXB.20201086

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Detection of Weak Fault Signals for EMU Bearings Under Strong Noise

PAPERS | 更新时间：2025-12-08

- Detection of Weak Fault Signals for EMU Bearings Under Strong Noise
- ACTA ELECTRONICA SINICA Vol. 49, Issue 11, Pages: 2217-2224(2021)
- 作者机构：
  
  1.河海大学机电工程学院，江苏常州 213000
  2.中车戚墅堰机车车辆工艺研究所有限公司，江苏常州 213000
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201086
  CLC： TN911.7
- Received：30 September 2020，
  
  Revised：2021-04-01，
  
  Published：25 November 2021
- 稿件说明：
移动端阅览
孙鑫威,纪爱敏,陈曦晖等.强噪声背景下动车组轴承微弱故障信号检测[J].电子学报,2021,49(11):2217-2224.

SUN Xin-wei,JI Ai-min,CHEN Xi-hui,et al.Detection of Weak Fault Signals for EMU Bearings Under Strong Noise[J].ACTA ELECTRONICA SINICA,2021,49(11):2217-2224.
孙鑫威,纪爱敏,陈曦晖等.强噪声背景下动车组轴承微弱故障信号检测[J].电子学报,2021,49(11):2217-2224. DOI： 10.12263/DZXB.20201086.

SUN Xin-wei,JI Ai-min,CHEN Xi-hui,et al.Detection of Weak Fault Signals for EMU Bearings Under Strong Noise[J].ACTA ELECTRONICA SINICA,2021,49(11):2217-2224. DOI： 10.12263/DZXB.20201086.

摘要

动车在高速行驶中，齿轮箱轴承易发生裂纹、点蚀等故障.为了在故障发生的初期检测出微弱的故障频率成分，本文提出了一种基于小波降噪预处理的周期势振动共振的轴承故障诊断方法.利用小波包提取轴承的固有共振频带，重构提取出的信号，滤除其中的强噪声干扰，随后将信号输入周期势振动共振系统，增强了故障特征.同时，本文建立了考虑振动共振系统中高频激励信号幅值的优化模型，并采用蚁群算法实现了其参数的自适应优化，得到输出信号后将其转化到频域分析，从而检测出轴承早期故障.实例分析表明，所提方法的数据处理结果相比单独采用随机共振的结果更精确，误差缩减至0.3%.

Abstract

In the high-speed motion of the motor car

the bearing of the gearbox is prone to cracks

pitting and other failures. In order to detect the weak fault frequency component in the early stage of fault occurrence

a bearing fault diagnosis method based on wavelet de-noising preprocessing and periodic potential vibration resonance feature-enhancing is proposed. Wavelet packet is used to extract the natural resonance frequency band of the bearing

reconstruct the extracted signal

filter out the interference of the strong noise

and then input the signal into the periodic potential vibration resonance system for enhancing the fault characteristics. Meanwhile

an optimization model considering the amplitude of the high-frequency excitation signal in the vibration excitation system is established

and the ant colony algorithm is used to adaptively optimize the parameters. After obtaining the output signal

it is converted into frequency domain analysis to detect early failure of the bearing. The example analysis shows that the error of the proposed method is reduced to 0.3% compared with the result of stochastic resonance.

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