基于振动信号瞬时分量抽取的偏心故障特征提取方法

Eccentricity Fault Feature Extraction Method Based on Instantaneous Component Extraction of Vibration Signal

电机的气隙偏心会引起气隙磁场的不均匀,产生的不平衡磁拉力导致电机异常振动。为了准确提取出电机偏心故障特征频率,在短时傅里叶变换基础上提出一种新的瞬态分量抽取方法。方法可以抽取故障信号中的瞬时分量,且具有比一般时频分析更好的信号重构效果,突出了偏心故障的特征频率,以此对故障进行检测。试验结果表明,方法的抗干扰能力比较强,有着良好的故障特征提取效果,能够准确识别出电机故障。

The eccentricity of the air gap of the motor will cause the uneven magnetic field of the air gap,and the resulting unbalanced magnetic pull will cause the motor to vibrate abnormally.In order to accurately extract the eccentricity fault characteristic frequency of the motor,a new transient component extraction method was proposed based on the short-time Fourier transform.The method can extract the instantaneous component in the fault signal.Moreover,the signal reconstruction effect was better than that of the general time-frequency analysis.The characteristic frequency of eccentric faults was highlighted to detect faults.The test results show that the method has strong anti-interference ability with good fault feature extraction effect,and can accurately identify motor faults.