摘要
针对滚动轴承故障诊断,综合运用小波分析和Hilbert-Huang变换,提出了一种用于滚动轴承故障特征提取的时频综合分析方法。该方法首先对滚动轴承故障信号进行小波分解,得到故障产生的共振频带,并进行解调,然后用Hilbert-Huang变换对所得到的解调信号进行EMD分解(经验模态分解),得出各个本征模态函数IMF。最后对各IMF信号进行包络谱分析,从包络谱上可以清晰地观察出滚动轴承的故障特征频率。本文进行了实例分析,结果充分表明了该时频综合分析方法较单一的分析方法更加能突出故障特征。
Aiming at the rolling element bearing fault diagnosis, we propose a time-frequency comprehensive analysis method for roiling bearing fault feature extraction. It is a comprehensive application of wavelet analysis and Hilbert-Huang Transform （HHT）. First, wavelet decomposition of rolling element bearing fault signal is carried out to obtain the resonance frequency band, which is the demodulated by Hilbert transform. After that, EMD （Empirical Mode Decomposition） is used to decompose the demodulated signal, and each intrinsic mode function （IMF） is obtained. Finally, spectrum analysis is carried out to each IMF signal, and the feature frequencies of rolling element beating faults can be clearly observed. Examples are given to verify the new methods, and the result shows that this time-frequency comprehensive analysis method has stronger robustness and excellent accuracy.
出处
《机械科学与技术》
CSCD
北大核心
2009年第4期517-521,共5页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(50705042)
航空科学基金项目(2007ZB52022)资助
