基于相对动态误差的轴承故障特征参数提取北大核心CSCD

轴承故障信号中存在有关故障的异常信息,对维护机械安全有着重大意义。轴承故障信号经小波包分解之后,故障的异常信息主要体现在分解频段的动态误差上,而各个频段的动态误差一般由标准差能量熵和标准差均值来描述。为了凸显轴承故障的区分特征,通过轴承故障尺寸去刻度动态误差,利用相应的轴承故障特征参数提取相对动态误差,是有效的处理方法。基于此思路,本文针对小波包分解后不同频段分量的标准差,计算其能量熵以及均值。然后把对应频段的标准差能量熵和标准差均值相加作为特征参数,在同一尺度下定性分析。同时把轴承信号不同频段的特征参数相加后的数值与轴承故障尺寸相比,通过产生的相对动态误差进行定量分析,最终实现对轴承故障的有效区分。实验结果表明,本文所提方法对轴承故障有很好的区分效果。

Extraction of bearing fault characteristic parameters based on relative dynamic error

There is abnormal information about the fault in the bearing fault sig nal,which is significant to maintain mechanical safety.After the bearing fault signal is decomposed by wavelet packe t,the abnormal information of the fault is mainly reflected in the dynamic error of the decomposed frequency band.The dyna mic error of each frequency band is generally described by the energy entropy of standard deviation and the mean val ue of standard deviation.In order to highlight the distinguishing characteristics of bearing faults,the dynamic erro r is demarcated by the bearing fault size.It is an effective method to extract the relative dynamic error by using the correspon ding bearing fault characteristic parameters. Based on this idea,this paper calculates the energy entropy and mean through th e standard deviation of different frequency band components after wavelet packet decomposition.Then,the energy entropy of the standard deviation and the mean value of the standard deviation of the corresponding frequency band are added as the characteristic parameters for qualitative analysis at the same scale.At the same time,the value obtained by adding the characteristic parameters of the different frequency bands of the bearing signal is compared with the bearing fau lt size.Through the quantitative analysis of the relative dynamic error,the effective distinction of bearing faults is fi nally realized.The experimental results show that the method proposed in this paper has a good effect on distinguishing beari ng faults.