经验小波变换-同步提取及其在滚动轴承故障诊断中的应用

Empirical wavelet transformsynchroextracting transform and its applications in fault diagnosis of rolling bearing

为了准确诊断轴承故障并探究故障信号的时变特性,提出了一种基于同步提取变换(Synchroextracting Transform,SET)和经验小波变换(Empirical Wavelet Transform,EWT)的轴承故障诊断方法。对故障信号进行经验小波变换分解,把分解得到的若干个经验模态进行同步提取变换,将所有模态的SET结果叠加即可得到EWTSET的时频结果。仿真表明,提出的方法比传统的SET方法有优势,能够有效解决传统SET方法在处理瞬时频率较近的模态信号时易出现瞬时频率特征模糊的问题。把所提出的方法应用到不同损伤程度的轴承故障诊断中,实验验证了提出的方法能有效地诊断出轴承故障与损伤程度,能清晰地表示故障信号的时变特征。

In order to accurately diagnose bearing faults and explore the time-varying characteristics of the fault signal,a bearing fault diagnosis method based on Synchroextracting Transform and Empirical Wavelet Transform(EWT-SET)is proposed.In the proposed method,the fault signal is decomposed by EWT,and the obtained empirical modes are used by SET.The SET of all modes are superimposed to obtain the time-frequency distributions of fault signal.The simulation shows that the proposed method is superior to the traditional SET method,and can solve the problem of the ambiguity of the instantaneous frequency trajectory occurred in the SET when the frequencies of the modal signals are very close to each other.The proposed method is applied to the fault diagnosis of rolling bearing with different degrees of damage.The experiments show that the proposed method can effectively diagnose the type of rolling bearing faults and the degree of damage,and can clearly represent the time-varying characteristics of fault signals.