OEEMD与Teager能量算子结合的轴承故障诊断

针对滚动轴承发生局部故障时振动信号中微弱周期性冲击的特征提取问题,提出参数优化集合经验模式分解(optimal ensemble empirical mode decomposition,简称OEEMD)与Teager能量算子解调结合的滚动轴承故障诊断方法。首先,针对集合经验模式分解(ensemble empirical mode decomposition,简称EEMD)过程中两个关键参数k(加入白噪声的幅值系数)和m(集合平均次数)的准确选取问题,通过引入相关系数、相关均方根误差和信噪比分析,给出一种可自适应确定这两个参数取值的OEEMD方法,通过OEEMD将冲击从滚动轴承振动信号中分离出来;其次,采用Teager能量算子对其进行包络解调,计算出瞬时幅值后再对瞬时幅值进行包络谱分析,以获取冲击的特征频率,从而对滚动轴承故障进行准确诊断。仿真信号分析和应用实例验证了该方法的有效性。

Fault Diagnosis of Bearings Combining OEEMD with Teager Energy Operator Demodulation

To extract the weak periodic impulse characteristics of vibration signal measured from the rolling bearing when local defects occur, a fault diagnosis method of rolling bearings combining an optimal ensemble empirical mode decomposition (OEEMD) with Teager energy operator is proposed. The OEEMD method is presented to determine two critical parameters k (the amplitude coefficient of the added white noise) and m (the number of ensemble trials)of the ensemble empirical mode decomposition (EEMD). It combines the correlation coefficient, relative root-mean-square of the error (RRMSE) and signal-to-noise ratio to guide the selection of the two critical parameters. The impulse is separated from the vibration signals of a rolling bearing by OEEMD. Then, the instantaneous amplitude is estimated by Teager energy operator and the characteristic frequency of the impulse component can be acquired from the envelope spectrum of the instantaneous amplitude. In the end, the rolling bearing faults are diagnosed. Both the simulation and experimental data prove The effectiveness of the proposed method.