摘要
机械故障的声发射信号中往往掺杂着各种干扰和噪声,为解决这一问题,提出了小波变换、集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)和马氏距离相结合的滚动轴承故障诊断方法;首次将马氏距离引入到轴承声发射信号的故障诊断中。该方法首先对故障轴承的声发射信号进行小波去噪处理,再对去噪后的信号进行EEMD分解,将其分解为多个固有模式函数(简称IMF)。其次采用马氏距离的方法消除EEMD分解结果中的虚假分量,提取能够反映轴承故障特征的IMF分量,突出高频共振成分。最后,通过瞬时Teager能量的Fourier频谱识别轴承故障的特征频率。仿真信号和滚动轴承外圈声发射信号的实例分析表明:此方法能很好地去除混杂在轴承声发射信号中的噪声,准确地识别出轴承故障的部位。
The acoustic emission signal of mechanical faults is usually mixed with various kinds of interference andnoise. In this article, a method of fault diagnosis of roller bearings was proposed using wavelet transform and EEMD-mahalanobisdistance. First of all, the original acoustic emission signals were disposed by wavelet-denoising and decomposed intoseveral stationary intrinsic mode functions （IMF） by EEMD. Then, the false IMFs of EEMD were eliminated by mahalanobisdistance method so that the IMF components which could reflect the characteristics of bearing faults could be extracted.Finally, the Fourier spectrum of the transient Teager energy was used to recognize the characteristic frequencies of the bearingfaults. Comparison of simulation signal with the measurement emission signal of the bearing with outer race faults showthat the method can effectively remove the noise in the fault mixed signals, and identify the location of the bearing fault accurately.
出处
《噪声与振动控制》
CSCD
2015年第1期235-239,共5页
Noise and Vibration Control
基金
航空科学基金(2012ZB54007)
中航工业产学研专项(cxy2012sh17)
关键词
振动与波
故障诊断
小波变换
集合经验模态分解
Teager能量谱分析
vibration and wave
fault diagnosis
wavelet transform
ensemble empirical mode decomposition（EEMD）
Teager energy spectrum analysis
