基于改进小波阈值和Teager能量算子的轴承故障诊断

Rolling Bearing Fault Diagnosis Based on Improved Wavelet Threshold De-noising Method and Teager Energy Operator

针对背景噪声下滚动轴承故障特征难以提取的问题以及传统小波阈值降噪算法中阈值难以确定的不足,文章提出了改进的小波阈值算法对滚动轴承的故障信号进行降噪预处理。同时,为了能有效地提取出滚动轴承的故障特征,引入了在检测信号冲击特征具有独特优势的Teager能量算子增强故障特征。仿真信号和实测信号处理结果表明,改进后的小波阈值算法能很好滤除故障信号中的噪声,且Teager能量算子能明显增强故障特征。该算法充分结合了两者的优点,有很好的工程应用前景。

In order to extract the fault characteristics of rolling bearing against the background noise and overcome the shortage of threshold which is difficult to determine in traditional wavelet threshold denoising algorithm,in this pa- per, an improved wavelet threshold algorithm was proposed to reduce the noise in fault signal of rolling bearing. In or- der to extract effectively fault features of rolling bearings, at the same time,Teager energy operator was introduced to solve this problem. Simulation and test show that the improved wavelet threshold algorithm can filter the noise in the fault signal very well and the Teager energy operator can obviously enhance the fault characteristics. This algorithm combines the advantages of both,and has a good engineering application prospects.