电子学报2018，Vol.46Issue(2)：358-364,7.DOI:10.3969/j.issn.0372-2112.2018.02.014
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
Enhanced Empirical Wavelet Transform Based Time-Frequency Analysis and Its Application to Rolling Bearing Fault Diagnosis
摘要
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 trans-form (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.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Key words
time-frequency analysis/Hilbert transform/empirical wavelet transform/rolling bearing/fault diagnosis分类
信息技术与安全科学引用本文复制引用
郑近德,潘海洋,戚晓利,张兴权,刘庆运..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[J].电子学报,2018,46(2):358-364,7.基金项目
国家自然科学基金(No.51505002) （No.51505002）
国家重点研发计划(No.2017YFC0805100) （No.2017YFC0805100）
安徽省高校自然科学研究重点项目(No.KJ2015A080) （No.KJ2015A080）
