基于小波包去噪的循环自相关方法在轴承故障诊断中的应用

The Application of the Denoising Wavelet Packet Based Cyclic Autocorrelation Method in Diagnosing the Rolling Bearing Faults

针对在轴承故障诊断中提取到的信号(包含噪声干扰)及表现出的复杂调制特性,采用小波包方法对信号进行降噪处理,以提高数据的准确度和精度,采用循环自相关的方法进行信号的解调处理,有效的提取故障特征频率。结果表明,用仿真信号将小波包降噪结合循环自相关方法应用于滚动轴承的内、外圈及滚动体的故障诊断,可以有效地提取出轴承的故障特征频率。

Because noise comes along with the signals acquired from bearing fault diagnosis,which shows complicated characteristics of modulation,the wavelet packet method is used to suppress the noise,so as to improve the accuracy and precision of the data. The cyclic autocorrelation method is adopted to demodulate the signal,and to extract the characteristic frequency of the fault effectively. The results show that when the method of combining denoising wavelet packet and cyclic autocorrelation by simulated signals is used in diagnosing faults in the inner and outer rings of the rolling bearing and the rolling parts,the characteristic frequency of the faults of the rolling bearing can be extracted effectively.