改进的EWT方法在高速列车轴承故障诊断中的应用

刘玉婷; 林建辉

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

试验检测技术 | 更新时间：2021-12-10

- 改进的EWT方法在高速列车轴承故障诊断中的应用
- Application of Improved EWT Method in Bearing Fault Diagnosis of High-speed Train
- 机车电传动 2020年第4期页码：47-53
- 作者机构：
  
  1.西南交通大学　牵引动力国家重点实验室，四川　成都　610031
- 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。
- 基金信息：
- DOI：10.13890/j.issn.1000-128x.2020.04.010
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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,(4):47-53.

Yuting LIU, Jianhui LIN. Application of Improved EWT Method in Bearing Fault Diagnosis of High-speed Train[J]. Electric Drive for Locomotives, 2020,(4):47-53.
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,(4):47-53. DOI： 10.13890/j.issn.1000-128x.2020.04.010.

Yuting LIU, Jianhui LIN. Application of Improved EWT Method in Bearing Fault Diagnosis of High-speed Train[J]. Electric Drive for Locomotives, 2020,(4):47-53. DOI： 10.13890/j.issn.1000-128x.2020.04.010.

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.

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 trainEWTspectrum envelopeHilbert transformfault diagnosisrolling bearingsimulation

references

CHOE H, WAN Y L, CHAN A K. Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals:comparison of FFT, CWT, and DWT features[J]. Proceedings of SPIE - The International Society for Optical Engineering, 1997, 3078: 480-496. DOI: 10.1117/12.271772http://doi.org/10.1117/12.271772.

吴斌, 冯长建, 罗跃纲, 等. 滚动轴承故障的振动信号诊断方法[J]. 机械设计与制造, 2009 (11): 178-179.

王宏宇. 非平稳随机信号分析与处理[M]. 北京: 国防工业出版社, 1999.

GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.

GILLES J, TRAN G, OSHER S. 2D empirical transforms, wavelets, ridgelets and curvelets revisited[J]. SIAM Journal on Imaging Sciences, 2014, 7(1): 157-186.

GILLES J, HEAL K. A parameterless scale-space approach to find meaningful modes in histograms- Application to image and spectrum segmentation[J]. International Journal of Wavelets, Multiresolution and Information Processing, 2014, 12(6): 1450044.

陈隽, 徐幼麟. 经验模分解在信号趋势项提取中的应用[J]. 振动、测试与诊断, 2005, 25(2): 101-104.

MOGHTADERI A, FLANDRIN P, BORGNAT P. Trend filtering via empirical mode decompositions[J]. Computational Statistics & Data Analysis, 2013, 58(1): 114-126.

李秀坤, 李婷婷, 马涛. 微弱信号强干扰分离方法研究[J]. 振动与冲击, 2011, 30(3): 225-227.

PAN J, CHEN J L, ZI Y Y, et al. Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment[J]. Mechanical Systems and Signal Processing, 2016, 72/73: 160-183.

王昌明, 张征, 李峰, 等. 改进经验小波变换的齿轮箱故障诊断新方法与应用[J]. 噪声与振动控制, 2018, 38(5): 167-172.

陈东月. 基于改进的CEEMD及IMF价值评价的高速列车齿轮箱故障诊断研究[D]. 成都: 西南交通大学, 2018.

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