Research and Application of Ensemble Empirical Mode Decomposition Principle and 1.5 Dimension Spectrum Method

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Research and Application of Ensemble Empirical Mode Decomposition Principle and 1.5 Dimension Spectrum Method

更新时间：2025-07-09

- Research and Application of Ensemble Empirical Mode Decomposition Principle and 1.5 Dimension Spectrum Method
- Vol. 43, Issue 5, Pages: 94-98(2009)
- 作者机构：
  
  1. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
  2. 北京航天飞行控制中心,北京,100094
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH17
- Online First：10 May 2009，
  
  Published：2009
- 稿件说明：
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Research and Application of Ensemble Empirical Mode Decomposition Principle and 1.5 Dimension Spectrum Method[J]. 2009, 43(5): 94-98.
DOI：

Research and Application of Ensemble Empirical Mode Decomposition Principle and 1.5 Dimension Spectrum Method[J]. 2009, 43(5): 94-98. DOI：

摘要

针对复杂背景下机车走行部齿轮箱齿轮裂纹故障微弱特征的提取问题

提出了总体平均经验模式分解(EEMD)与1.5维谱的故障特征提取方法.首先运用EEMD方法对振动信号进行自适应抗混分解

得到不同频带的基本模式分量(IMF)

然后运用1.5维谱方法对含有故障特征信息的IMF进行后处理.该方法具有避免模式混淆、抑制高斯白噪声、检测非线性耦合特征等特性

并以此来提取故障的微弱特征信息.根据待处理信号的时频特性与EEMD原理

提出了在EEMD方法中加入高斯白噪声的准则

通过信号仿真验证了EEMD方法的抗混分解能力.将EEMD与1.5维谱方法应用于机车走行部齿轮箱的监测诊断中

成功地提取出齿轮箱大齿轮齿根早期的裂纹故障.

Abstract

To extract the gear crack fault weak feature of locomotive running gear box on complex background

a new method of ensemble empirical mode decomposition(EEMD)and 1.5 dimension spectrum for fault feature extraction is proposed. The vibration signal is adaptively anti alias decomposed by EEMD method to get intrinsic mode function(IMF)of different frequency bands

then 1.5 dimension spectrum as a post processing method is adopted to process IMF which contains fault feature information. This method is endowed with characteristics of avoiding model mixing

suppressing Gaussian white noise

detecting the nonlinear coupling feature. Based on time-frequency character of the signal and the principle of EEMD

a criterion of adding Gaussian white noise in EEMD method is proposed

and the anti alias decomposing ability of EEMD method is verified by signal simulation experiment. EEMD and 1.5 dimension spectrum are introduced into monitoring diagnosis of a locomotive running gear box

and the results show that this method enables to successfully extract the early crack fault of the gear tooth root in gearbox.

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references

何正嘉,訾艳阳,张西宁.现代信号处理及工程应用[M]. 西安:西安交通大学出版社,2007:219-245.

WU Zhaohua, HUANG Norden E. A study of the characteristics of white noise using the empirical mode decomposition method [J].Proc R Soc Lond: A, 2004,460:1597-1611.

WU Zhaohua, HUANG Norden E. Ensemble empirical mode decomposition: a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009,1(1):1-41.

苏文斌,温熙森,史维祥.故障诊断中非线性耦合特征提取的研究 [J].西安交通大学学报,1999,33(1):88-92.

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HUANG Norden E, ZHENG Shen, STEVEN R L. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proc R Soc Lond: A, 1998, 454:903-995.

FLANDRIN P, CONCALVÈS G, RILLIN G. Empirical mode decomposition as a filter bank [J]. IEEE Signal Processing, 2004,11(2):112-114

樊养余,陶宝祺,熊克,等.舰船噪声的1.5维谱特征提取 [J].声学学报,2002,27(1):71-76.

FAN Yangyu, TAO Baoqi, XIONG Ke, et al. Feature extracting of ship-radiated noise by 1.5 spectrum [J]. Acta Acustica, 2002,27(1):71-76.

李崇晟, 屈梁生.齿轮早期疲劳裂纹的混沌检测方法 [J].机械工程学报,2005,41(8): 195-198.

LI Chongsheng, QU Liangsheng. Chaotic detection method of gear early-stage fatigue crack [J]. Chinese Journal of Mechanical Engineering, 2005,41(8):195-198.

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