基于集合经验模式分解和交叉能量算子的滚动轴承故障诊断

Fault diagnosis of rolling element bearing based on ensemble empirical mode decomposition and cross energy operator

  • 摘要: 振动信号的周期性冲击及其重复频率是滚动轴承故障诊断的关键.本文提出了一种基于集合经验模式分解和交叉能量算子提取滚动轴承故障特征的方法.首先,应用集合经验模式分解方法将振动信号分解为本征模式函数以满足交叉能量算子对信号单分量的要求.然后根据相关程度和峭度从本征模式函数中选取敏感分量,计算敏感分量和原始信号的瞬时交叉能量及其傅里叶频谱.最后根据交叉能量的频谱结构和特征频率识别轴承故障.通过分析滚动轴承故障仿真信号和实验测试信号,诊断了滚动轴承元件故障,验证了该方法的有效性.

     

    Abstract: Periodic impulses in vibration signals and its repeating frequency are the key factors for diagnosing rolling element bearing faults. A new method based on ensemble empirical mode decomposition(EEMD) and cross energy operator was proposed to extract the characteristic frequency of bearing fault. Firstly,the signal was decomposed into intrinsic mode function(IMF) by means of EEMD to satisfy the mono-component requirement by the cross energy operator. Next,the sensitive IMF was selected according to correlation and kurtosis,and instantaneous cross energy between the IMF and the original signal and its Fourier spectrum were calculated. Finally,the bearing faults were diagnosed by matching the repeating frequency of fault-induced periodic impulses with the fault characteristic frequency. By analyzing both a simulated faulty bearing vibration signal and the experimental data of bearing faults,the bearing faults were diagnosed and the effectiveness of the proposed method was validated.

     

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