Enhanced Empirical Wavelet Transform Based Time-Frequency Analysis and Its Application to Rolling Bearing Fault Diagnosis

ZHENG Jin-de; PAN Hai-yang; QI Xiao-li; ZHANG Xing-quan; LIU Qing-yun

doi:10.3969/j.issn.0372-2112.2018.02.014

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Enhanced Empirical Wavelet Transform Based Time-Frequency Analysis and Its Application to Rolling Bearing Fault Diagnosis

更新时间：2025-07-16

- Enhanced Empirical Wavelet Transform Based Time-Frequency Analysis and Its Application to Rolling Bearing Fault Diagnosis
- Acta Electronica Sinica Vol. 46, Issue 2, Pages: 358-364(2018)
- 作者机构：
  
  安徽工业大学机械工程学院,安徽,马鞍山,243032
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.51505002);National Key Research and Development Program of China (No.2017YFC0805100);Key Program of Natural Science Research Projrct of Colleges and Universities of Anhui Province (No.KJ2015A080)
- DOI：10.3969/j.issn.0372-2112.2018.02.014
  CLC： TN911.7TH165.3
- Published Online：25 February 2018，
  
  Published：2018
- 稿件说明：
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ZHENG Jin-de, PAN Hai-yang, QI Xiao-li, et al. Enhanced Empirical Wavelet Transform Based Time-Frequency Analysis and Its Application to Rolling Bearing Fault Diagnosis[J]. Acta Electronica Sinica, 2018, 46(2): 358-364.
DOI：

ZHENG Jin-de, PAN Hai-yang, QI Xiao-li, et al. Enhanced Empirical Wavelet Transform Based Time-Frequency Analysis and Its Application to Rolling Bearing Fault Diagnosis[J]. Acta Electronica Sinica, 2018, 46(2): 358-364. DOI： 10.3969/j.issn.0372-2112.2018.02.014.

摘要

经验小波变换是最近提出的非平稳信号分析方法，针对其不足，提出了一种改进的经验小波变换方法；同时结合瞬时频率新定义，提出了一种非平稳信号时频分析新方法.该方法首先通过改进的经验小波变换将一个复杂的非平稳信号自适应地分解为若干个具有紧支集频谱的内禀模态函数之和；再通过对每个内禀模态函数进行解调，得到原始信号的时频分布.将提出的方法应用于滚动轴承试验数据分析，并将其与希尔伯特黄变换进行了对比，结果表明，论文提出的方法能够有效地诊断滚动轴承故障，且诊断效果优于希尔伯特黄变换方法.

Abstract

Empirical wavelet transform is a recently proposed method for non-stationary signal analysis. In view of its shortcomings

an enhanced empirical wavelet transform (EEWT) is proposed in this paper. Meanwhile

combining the new definition of instantaneous frequency

a new time-frequency analysis method for non-stationary signal is put forward. Firstly

EEWT is used to decompose a non-stationary signal into a number of intrinsic mode functions (IMFs) that have compact support set spectrum. Secondly

the time-frequency distribution of original signal can be obtained by demodulating each IMF Also

the proposed method is applied to analyze experiment data of rolling bearing by comparing with Hilbert-Huang transform (HHT) and the results show that the proposed method can effectively diagnose the faults of rolling bearings and get a better effect than that of HHT.

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