摘要
为实现滚动轴承故障周期冲击特征的有效提取,解决经验小波变换Fourier谱分割存在的问题,提出了一种改进的自适应无参经验小波变换方法。首先,利用自适应无参经验小波变换对信号Fourier谱进行自适应分割;然后,利用峭度指标对谱边界进行合并,并重构滤波器组对信号进行分解;最后,选取峭度值最大的分量进行包络解调提取故障特征。仿真和工程应用验证了所提方法的有效性,分析结果表明该方法的性能优于集合经验模态分解和经典经验小波变换。
In order to realize the effective extraction of rolling bearing fault periodic impulse features and solve the problem of Fourier spectral segmentation in empirical wavelet transform,an improved adaptive parameterless empirical wavelet transform method is proposed.First,adaptive segmentation of the Fourier spectrum is performed by adaptive parameterless empirical wavelet transform.Then,the spectral boundaries are combined by using the kurtosis index,and the filter banks are reconstructed to decompose the signal;finally,the component corresponding to the largest kurtosis value is selected to extract fault features by the Hilbert transform.The simulation and engineering application verify the effectiveness of the proposed method.The analysis results show that the performance of the proposed method is better than ensemble empirical model decomposition and the classical empirical wavelet transform.
作者
李继猛
王慧
李铭
姚希峰
LI Ji-meng;WANG Hui;LI Ming;YAO Xi-feng(School of Electrical Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China)
出处
《计量学报》
CSCD
北大核心
2020年第6期710-716,共7页
Acta Metrologica Sinica
基金
国家自然科学基金(51505415)
河北省自然科学基金(E2017203142,F2018203413)。
关键词
计量学
滚动轴承
故障诊断
自适应无参经验小波变换
峭度指标
metrology
rolling bearing
fault diagnosis
adaptive parameterless empirical wavelet transform
kurtosis index
