摘要
滚动轴承的故障信号是非平稳的、多分量的调制信号,特别是故障早期,由于调制源弱,早期故障信号微弱且受周围设备的噪声干扰,导致故障特征难以识别。采用自相关分析和局域均值分解(LMD)方法提取故障特征。首先采用自相关分析提取信号中的周期成分,消除噪声的干扰,然后利用局域均值分解方法将多分量的调制信号分解为若干个PF分量之和,再结合共振解调技术对PF分量进行包络分析以提取故障特征频率。实验证明了方法的有效性。
Rolling bearing fault vibration signals were nonstationary and multi--component modu- lated signals, Especially in the early stages of the fault, fault features were difficult to identify because early fault signals were weak due to the weak modulation source and contained strong noise. The auto- correlation analysis and LMD method was proposed herein, it can extract fault features effectively. First, the noises were eliminated by using autocorrelation analysis, the periodic components in the sig- nals were extracted. Second, multi--component modulation signals were decomposed into a number of product functions（PFs）. Last,applying demodulated resonance technique to each PF component,the fault feature frequency was extracted through envelopment analysis. The experimental results demon- strate the effectiveness of the method.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2014年第2期186-191,共6页
China Mechanical Engineering
基金
国家自然科学基金资助项目(51176028)
吉林省自然科学基金资助项目(201115179)
