滚动轴承的故障诊断

摘要

For the non-stationary and modulation features of rolling bearing's fault signals, a method based on wavelet analysis is employed. The signals including fault information are decomposed and reconstructed by wavelet analysis method. Then, demodulation and fine spectral analysis of the signals are carried out by using Hilbert transform. The characteristic frequencies of the fault signals are extracted, and the fault patterns of the rolling bearings can be recognized. It is found that the wavelet analysis and Hilbert transform are effective in identifying the local defects of rolling bearings.%针对滚动轴承故障信号的非平稳和调制特点,使用小波分解,对包含故障信息的信号进行分解、重构.应用Hilbert变换进行解调和细化频谱分析,提取了故障特征频率,判断出滚动轴承故障模式.小波分解和Hilbert变换结合对滚动轴承局部损伤故障的检测是有效的.