[1]刘玉婷,林建辉.改进的EWT方法在高速列车轴承故障诊断中的应用[J].机车电传动,2020,(04):47-53.[doi:10.13890/j.issn.1000-128x.2020.04.010]
 LIU Yuting,LIN Jianhui.Application of Improved EWT Method in Bearing Fault Diagnosisof High-speed Train[J].Electric Drive for Locomotives,2020,(04):47-53.[doi:10.13890/j.issn.1000-128x.2020.04.010]
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改进的EWT方法在高速列车轴承故障诊断中的应用()
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机车电传动[ISSN:1000-128X/CN:43-1125/U]

卷:
期数:
2020年04期
页码:
47-53
栏目:
试验检测技术
出版日期:
2020-07-10

文章信息/Info

Title:
Application of Improved EWT Method in Bearing Fault Diagnosisof High-speed Train
文章编号:
1000-128X(2020)04-0047-07
作者:
刘玉婷林建辉
(西南交通大学 牵引动力国家重点实验室,四川 成都 610031)
Author(s):
LIU Yuting LIN Jianhui
( State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, Sichuan 610031, China )
关键词:
高速列车经验小波变换频谱趋势Hilbert变换故障诊断滚动轴承仿真
Keywords:
high-speed train EWT spectrum envelope Hilbert transform fault diagnosis rolling bearing simulation
分类号:
U292.91+4;TH133.33
DOI:
10.13890/j.issn.1000-128x.2020.04.010
文献标志码:
A
摘要:
高速列车轴承的故障特征提取较为困难,针对这一问题,在经验小波变换(Empirical Wavelet Transform,EWT)的基础上,提出了一种基于频谱趋势与频带合并的改进EWT方法,并将其应用于高速列车轴承的故障诊断。该方法首先利用经验模态分解,根据IMF分量判断准则,提取故障信号的频谱趋势,从而得到初始的频谱分界点;然后计算各初始频带的故障信息判断指标,得到自适应阈值,判断初始频带的有效性,通过对无效频带的合并完成频谱的重新划分;最后进行经验小波变换,将各频带通过正交滤波器组,对得到的各分量信号进行Hilbert变换,得到轴承的故障特征频率。通过仿真和试验验证,改进后的EWT方法可以准确地提取出轴承故障特征频率的基频和倍频成分,有效地确定轴承故障。
Abstract:
It is difficult to extract fault features of axle box bearings of high-speed train. To solve this problem, an improved empirical wavelet transform based on spectrum trend and frequency band combination was proposed and applied to the fault diagnosis of high-speed train bearings. Firstly, by using empirical mode decomposition, the spectrum trend of the fault signal according to the IMF component judgment criterion was extracted, and the initial spectrum boundary point was obtained. Then, the fault information judgment index of each initial frequency band was calculated, and the adaptive threshold was obtained to determine the initial frequency band, the validity of the spectrum was re-divided by combining the invalid frequency bands. Finally, the empirical wavelet transform was carried out to pass each frequency band through the orthogonal filter bank, and the obtained component signals were subjected to Hilbert transform to obtain the fault characteristic frequency of the bearing. Through simulation and experimental verification, the improved EWT method could accurately extract the fundamental frequency and frequency doubling component of the bearing fault characteristic frequency, and effectively determine the bearing fault.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介:刘玉婷(1997—),女,硕士研究生,主要从事机械设备故障诊断与信号分析。
更新日期/Last Update: 2020-07-10