基于小波包能量法的滚动轴承故障诊断

Vibrating Diagnosis of Rolling Bearings Based on Wavelet Packet Energy Feature

阐述了故障轴承振动与信号的关系,小波包的原理以及BP神经网络的工作原理和实现过程,并以滚动轴承故障诊断为例,提取了小波包节点能量作为振动信号特征参数,并训练BP神经网络,对故障模式进行识别。结果表明,如果神经网络设计合理,训练适当,则具有很强的故障识别能力。说明利用小波包能量法和BP神经网络进行滚动轴承振动诊断是可行、有效的。

The steps of method of rolling bearings fault diagnosis were summarized based on wavelet packet energy feature and BP neural network, and the principle of wavelet packet and the connection between vibration of fault rolling bearings and signal were illuminated. On this basis, wavelet packet energy feature was extracted to construct characteristic vector, and a rolling bearings fault diagnosis experiment was designed to verify this method. The conclusion indicated that BP neural network has possessed good capability of identification with reasonable design and proper training. The results showed that it is feasible to implement fault vibrating diagnosis of rolling bearings with wavelet packet energy feature and BP neural network.