【作者】 冯志鹏； 陈衍娟； 马飞； 刘立； 郝如江； 褚福磊；

【Author】 FENG Zhipeng~1,CHEN Yanjuan~1,MA Fei~1,LIU Li~1,HAO Rujiang~2,CHU Fulei~3 1.School of Mechanical Engineering,University of Science and Technology Beijing,Beijing 100083,P.R.China 2.Department of Mechanical Engineering,Shijiazhuang Railway Institute,Shijiazhuang 050043,P.R.China 3.Department of Precision Instruments and Mechanology,Tsinghua University,Beijing 100084,P.R.China

【机构】 北京科技大学机械工程学院； 石家庄铁道大学机械工程学院； 清华大学精密仪器与机械学系；

【摘要】 周期性冲击及其重复频率是识别滚动轴承故障的重要依据。传统的包络谱分析需要优化选择滤波参数,过程复杂。本文提出了基于集合经验模式分解(EEMD)的包络谱分析方法,用于提取滚动轴承故障的特征频率。首先应用EEMD方法将信号分解为单分量成分,然后对包含主要冲击特征的第一个本质模式函数(IMF)进行包络谱分析,识别故障引起的周期性冲击的重复频率,从而诊断故障。这种方法不涉及滤波参数选择,在实际应用中具有更好的适应性。通过实验信号分析,验证了该方法的有效性。更多还原

【Abstract】 Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the localized damage of rolling element bearings.Traditional envelope spectrum is effective in fault diagnosis of rolling element bearings,but it needs optimization of filter parameters which is complicated.A new method based on ensemble empirical mode decomposition(EEMD) and envelope spectral analysis is proposed to extract the characteristic frequency of bearing element fault.The signal is firstly decomposed into mono-components by means of EEMD,then the obtained first intrinsic mode function is analyzed by means of envelope spectrum to identify the repeating frequency of fault induced periodic impulses and thereby to diagnose bearing faults.Its effectiveness in extracting the characteristic frequency of bearing faults,and especially its performance in identifying the symptoms of weak faults,are validated by the experimental signal analyses of seeded fault experiments.更多还原