摘要
谐波小波具有良好的盒形频谱特性,可以将非平稳振动信号既不交叠又无遗漏地分解到相互独立的频带上。将谐波小波作为滤波器,可以将特定频段的成分与信号的其他频率成分分离,进行重构后就能够提取出强背景噪声干扰下的特定信号频段成分,实现谐波小波滤波。然后,对故障敏感段信号进行HHT边际谱分析,并以边际谱的最大峰值作为特征向量判断轴承的工作状态和故障类型。
The harmonic wavelet has a fine box-like spectral characteristics,which could decompose non-stationary vibration signal to independent frequency bands without any overlapping or leak.The harmonic wavelet is used as a fil-ter,the specific frequency band components is separated with other frequency components of signal.Then the specific signal frequency band component under strong background noise is abstracted after the reconstruction,the harmonic wavelet filtering is achieved.The Hilbert-Huang Transformation （HHT）marginal spectrum analysis is applied to the fault sensitive period of signal,and the maximal peak value of marginal spectrum is used as the characteristic vector to judge working state and fault type of bearings.
出处
《轴承》
北大核心
2014年第9期44-47,共4页
Bearing
基金
国家杰出青年科学基金项目(61225019)
关键词
滚动轴承
故障诊断
谐波小波滤波
边际谱
HHT
rolling bearing
fault diagnosis
harmonic wavelet filtering
HHT
marginal spectrum
