故障特征提取是大型机械设备状态监测和故障诊断领域的核心问题。对四类典型汽轮机轴瓦振动故障信号,分别进行小波分解和各类型振动细节信号频率特征提取,提取出可以完全表征汽轮机轴瓦振动特点的频率特征。研究表明,小波分析在汽轮机轴瓦振动故障诊断中能较好地提取频率特征。 Fault feature extraction is the core issue in the field of condition monitoring and fault diagnosis of large mechanical equipment. Based on the vibration fault signals of the four typical steam turbine bearings, the wavelet decomposition and the frequency characteristics of vibration signals are extracted respectively, and the frequency characteristics of vibration characteristics of the turbine bearing bush are fully extracted. The research shows that the wavelet analysis can extract the frequency characteristics well in the vibration fault diagnosis of the turbine bearing.