摘要
为了实现经验小波变换中Fourier谱的自适应分割,提出了一种基于能量的尺度空间经验小波变换(Energy Scale Space Empirical Wavelet Transform,ESEWT)方法,并将此方法应用于轴承故障诊断。首先使用尺度空间的方法对傅里叶谱进行自适应划分,得到各频带分界点;接着根据各频带能量筛选频带分界点,使其保留能量大于均值的频带,合并小于均值的相邻频带;然后在得到有效的频带分界点后,设计小波滤波器组,得到分量信号;最后对各分量信号进行Hilbert变换,提取轴承的故障特征频率。通过实验验证,ESEWT方法能够减少频带分界点,在一定程度上改善了频带破裂现象,并且能够精确提取出轴承故障特征频率,凸显了故障频率及其谐波成分,能有效的识别轴承故障。
In order to fulfill the adaptive segmentation of Fourier spectrum in empirical wavelet,a method called Energy Scale Space Empirical Wavelet Transform(ESEWT)is proposed and applied to bearing fault diagnosis. Firstly,use the method of scale space to classify the Fourier spectrum and get the different frequency points. Then,select the cutoff points of frequency bands according to the energy. Retain the frequency bands whose energy is greater than the mean value and combine the adjacent frequency bands less than the average value. After getting the effective cutoff points of frequency bands,we design the wavelet filter bank to get component signals. Finally,Hilbert transform is carried out for each component signal to extract the fault characteristic frequency of the bearing. Through experimental verification,ESEWT method can reduce the cutoff points of frequency bands,to some extent,it can improve the band-break phenomenon. At the same time,ESEWT method can accurately extract the bearing fault characteristic frequency,highlight the fault frequency and its harmonic components,effectively identify the bearing failure.
作者
栗蕴琦
林建辉
李倩
LI Yun-qi;LIN Jian-hui;LI Qian(Southwest Jiaotong University,State-Key Laboratory of Traction Power,Sichuan Chengdu610031,China)
出处
《机械设计与制造》
北大核心
2020年第5期83-87,共5页
Machinery Design & Manufacture
基金
国家自然科学基金(51475387)。
关键词
经验小波变换
尺度空间
能量
轴承故障诊断
Empirical Wavelet Transform
Scale Space
Energy
Bearing Fault Diagnosis
