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

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.

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.