经验小波变换在旋转机械故障诊断中的应用  

Application of Empirical Wavelet Transform in Fault Diagnosis of Rotary Mechanisms

作　　者：向玲[1] 李媛媛[1] 

机构地区：[1]华北电力大学机械工程系,河北保定071003

出　　处：《动力工程学报》2015年第12期975-981,共7页Journal of Chinese Society of Power Engineering

基　　金：国家自然科学基金资助项目(51475164);河北省自然科学基金资助项目(E2013502226)

摘　　要：针对旋转机械故障诊断问题,提出了一种基于经验小波变换(EWT)的信号处理方法.该方法通过对信号的傅里叶频谱进行划分,并建立一组小波滤波器组对划分过的频谱进行滤波,得到一组单分量成分,对每个单分量成分进行Hilbert变换即可得到瞬时频率和瞬时幅值;并针对仿真信号和几组典型的实验转子故障信号进行了EWT方法和经验模态分解方法的性能比较研究,以验证该方法的有效性.结果表明:EWT方法能准确地分析机械故障信号,故障特征值明显,可有效应用于旋转机械故障诊断.A signal processing method was proposed for fault diagnosis of rotary mechanisms based on empirical wavelet transform（EWT）.The specific way is to divide the Fourier spectrum of relevant signals,and then to filter the divided spectrum with a set of newly built wavelet filter bank to get a group of single component ingredients,and finally to transform every single component ingredient by Hilbert method so as to obtain the instantaneous frequency and amplitude.To verify the effectiveness of the method,a comparison was made to the performance of EWT and empirical mode decomposition method in diagnosing fault signals coming from both simulation and typical experimental rotors.Results show that the EWT method can accurately analyze the mechanical fault signals and the failure eigenvalues are evident,which may be applied in fault diagnosis of rotary mechanisms.

关 键 词：故障诊断 经验小波变换 经验模态分解 信号分析 

分 类 号：TK288[动力工程及工程热物理—动力机械及工程]