由于滚动轴承声发射信号在各频段的能量分布与轴承的故障类型相关，可以利用谐波小波包将不同故障滚动轴承的声发射信号分解到不同频段，进而将各频段的能量组成特征向量输入BP神经网络，通过神经网络判别滚动轴承的故障类型。利用神经网络对滚动轴承进行故障识别时，对谐波小波包和Daubechies小波包进行了比较。实验结果证明对于滚动轴承声发射信号的故障模式识别，将谐波小波包分解和BP神经网络相结合的方法可以获得良好的效果。

The energy content of each frequency sub-band of the acoustic emission(AE) signal after decomposition is related to the type of the roller bearing defect, AE signals measured from the bearing test rig were decomposed into a number of frequency sub-bands by using harmonic wavelet packet, and energy features associated with each sub-band were selected. The energy features were then used as inputs to a back-propagation neural network classifiers for identifing the bearing’s fault. In the bearing fault recognition, harmonic wavelet packet was compared with daubechies wavelet packet. The experimental results indicate that the proposed fault diagnosis method is effective and can be used for roller bearing fault recognition.