基于新小波阈值的轴承故障诊断方法

Bearing Fault Diagnosis Method Based on New Wavelet Threshold

对于滚动轴承的故障分类方法抗干扰性差、准确率低等问题,文中提出一种新小波阈值的滚动轴承故障特征提取方法.通过计算每层分解后数据与原始数据的相关系数,改变调节因子,在不同分解层中选取阈值和阈值函数,达到寻求最优信噪比的目的.以型号为6205-2RS的滚动轴承故障信号为例进行仿真实验,对降噪后的频谱以及LS-SVM诊断结果对比可以看出,新阈值的降噪效果要明显好于另外几种方法.

The fault classification method of rolling bearings has problems such as poor anti-interference and low accuracy.Therefore,a bearing fault feature extraction method based on new wavelet threshold is proposed in this paper.Firstly,the correlation coefficient between the decomposed data of each layer and the original data is calculated.Secondly,the adjustment factor is changed according to the correlation coefficient,and the threshold and the threshold function are selected in different decomposition layers.Finally,the goal of finding the optimal signal-to-noise ratio is achieved.The rolling bearing fault signal of model 6205-2RS is used as an example for troubleshooting.By comparing the spectrum and LS-SVM diagnosis results after noise reduction,it can be seen that the noise reduction effect of the new threshold is significantly better than that of other methods.