There is abnormal information about the fault in the bearing fault signal,which is significant to maintain mechanical safety.After the bearing fault signal is decomposed by wavelet packet,the abnormal information of the fault is mainly reflected in the dynamic error of the decomposed frequency band.The dynamic error of each frequency band is generally described by the energy entropy of standard deviation and the mean value of standard deviation.In order to highlight the distinguishing characteristics of bearing faults,the dynamic error is demarcated by the bearing fault size.It is an effective method to extract the relative dynamic error by using the corresponding bearing fault characteristic parameters.Based on this idea,this paper calculates the energy entropy and mean through the 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 fault size.Through the quantitative analysis of the relative dynamic error,the effective distinction of bearing faults is finally realized.The experimental results show that the method proposed in this paper has a good effect on distinguishing bearing faults.