基于LMD和MED的滚动轴承故障特征提取方法

Fault feature extraction method for rolling element bearings based on LMD and MED

机械系统所拾取的振动信号包含着许多复杂的信息成分,微弱故障信号的提取往往会受到这些成分的影响,故障识别非常困难,尤其是滚动体故障识别,往往比内圈和外圈故障识别更困难。提出局域均值分解(local mean decomposition,LMD)与最小熵反褶积(minimum entropy deconvolution,MED)结合的方式,提取强噪声、强确定性成分下微弱故障信号的特征。先用LMD对信号做预处理,自适应地分解为若干个乘积函数(product function,PF)分量,再对前4个PF分量做MED处理以放大故障脉冲特征,最后对MED处理后的信号进行包络分析。通过对强噪声背景下滚动轴承滚动体的故障实例分析,该方法得到的输出频谱故障特征频率处峰值与200 Hz内所有峰值均值的比值较原信号的增加了96.4%,同时信噪比提高了18.3%,成功地提取了故障特征,取得了良好的效果,该研究可为强噪声环境下轴承故障识别和诊断提供参考。

The vibration signals collected from mechanical systems consist of cyclic impulse response, deterministic component and noise. The rolling bearing＇s fault features are usually so weak that they are overwhelmed by these components,leading difficulty for fault diagnosis. Compared with the inner race and outer race defects of rolling bearing, recognizing the rolling element defects are much more challenging. Therefore, the key problem of fault diagnosis of rolling bears is to exactly extract the weak fault features from a strong noisy background. In this paper, we developed a method based on the minimum entropy deconvolution（MED） and local mean decomposition（LMD） for diagnosing fault features. First, the LMD was used to decompose the original signals into a set of production functions（PFs） adaptively. Each PF was a product of an amplitude envelope signal and a frequency-modulated signal. By doing so, we aimed to obtaining different components embedded in the original signal. These included the cyclic impulse response, deterministic component and noise. However, the cyclic impulse responses were always submerged by noises and they were helpless to make a decision of fault. The MED filter was adopted to search for an optimum set of filter coefficients that recover the output signal（of an inverse filter） with the maximum value of kurtosis. The MED filter was capable of deconvolution of the periodic impulsive excitations from a mixture of response signals and thus enhanced the impulses arising from spalls and cracks in rolling bearings. The MED filter can also be used to remove most noises. Therefore, the former four PFs were further processed by the MED to enhance the fault impulse information. At last,the signal after processed by the LMD and MED was analyzed by envelop analysis. Through this envelop spectrum,the fault features were ultimately extracted. Experimental investigation of 6205-2RS JEM SKF bearings with rolling element defects was performed. The vibration data were obtained from a test rig for simulating various bearing faults in an electrical engineering lab of the Case Western Reserve University. Single point defects were introduced to the test bearings by the electro-discharge machining with the diameters of 0.177 8 mm. The faulty bearings were installed in the drive end, but the accelerometers were placed at the fan end, so the noise was very strong. Using our LMD-MED method, the fault features were successfully extracted. We concluded based on the experiment that the fep index, which indicates the ratio of the peak value at the fault characteristic frequency versus the mean value of the spectrum in 200 Hz band, was increased by 96.4% compared with the original signal. At the same time, the signal-to-noise ratio（SNR） was raised by approximately 18.3% after the signal processing by the LMD and MED. The experiment results proved that the method was effective to detect and extract the fault features of rolling bearings with strong background noises. Besides, these showed that the method based on the minimum entropy deconvolution and local mean decomposition can also provide a very useful reference for fault diagnosis of rolling bears.