基于滚动轴承振动信号分布特点和小波分析的优点,提出以小波能量作为诊断标准来提取滚动轴承的故障特征。而实际中所采集到的振动信号却含有其他机械振动或故障能量。为了解决这一问题,提高滚动轴承的故障特征提取精度,提出一种基于独立分量和小波能量的滚动轴承故障特征提取。试验证明了该法的有效性和具有很广的应用潜力。 Based on the vibration signal distribution characteristics of rolling bearings and the advantages of wavelet analysis, the fault characteristics of rolling bearings are proposed based on the wavelet energy as a diagnostic criterion. In fact, the vibration signal collected contains other mechanical vibration or fault energy. In order to solve this problem and improve the fault feature extraction accuracy of rolling bearing, a fault feature extraction of rolling bearing based on independent component and wavelet energy is proposed. The test proves the validity of the method and has a wide range of potential applications.