电子学报 ›› 2018, Vol. 46 ›› Issue (2): 358-364.DOI: 10.3969/j.issn.0372-2112.2018.02.014

• 学术论文 • 上一篇    下一篇

基于改进经验小波变换的时频分析方法及其在滚动轴承故障诊断中的应用

郑近德, 潘海洋, 戚晓利, 张兴权, 刘庆运   

  1. 安徽工业大学机械工程学院, 安徽马鞍山 243032
  • 收稿日期:2016-07-15 修回日期:2017-09-13 出版日期:2018-02-25
    • 作者简介:
    • 郑近德,男,1986年生于安徽阜阳,博士,现为安徽工业大学机械工程学院副教授.研究方向为动态信号处理,时频分析及机械设备故障诊断,已发表学术论文40余篇.E-mail:lqdlzheng@126.com;潘海洋,男,1989年生于安徽宿州,硕士,现为安徽工业大学机械工程学院助教,湖南大学博士研究生.研究方向为模式识别与机械设备故障诊断.E-mail:pansea@sina.cn
    • 基金资助:
    • 国家自然科学基金 (No.51505002); 国家重点研发计划 (No.2017YFC0805100); 安徽省高校自然科学研究重点项目 (No.KJ2015A080)

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   

  1. School of Mechanical Engineering, Anhui University of Technology, Maanshan, Anhui 243032, China
  • Received:2016-07-15 Revised:2017-09-13 Online:2018-02-25 Published:2018-02-25
    • Supported by:
    • 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)

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.

关键词: 时频分析, 希尔伯特变换, 经验小波变换, 滚动轴承, 故障诊断

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.

Key words: time-frequency analysis, Hilbert transform, empirical wavelet transform, rolling bearing, fault diagnosis

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