基于小波包分析的滚动轴承的故障诊断方法研究

用基于非平稳信号的分析方法,研究滚动轴承的故障诊断模型与算法。在充分分析故障机理及特点的前提下,重点开展对滚动轴承故障振动信号的小波包分析的研究工作,提取出反映故障模式的有效故障特征。并基于所获取的故障特征向量,建立BP神经网络分类器,实现对滚动轴承典型故障的识别与诊断。

Fault diagnosis of roller bearings based on the wavelet packet analysis

We used the analysis methods which are based on non-stationary signal analysis methods to study the model and algorithm of the roller bearing fault diagnosis.In the full analysis of failure mechanisms and characteristics of the premise,we focused on the wavelet packet analysis of the vibration signal of rolling bearing fault to extract the effective fault characteristics which could reflect the failure modes.We established the BP neural network classifier based on the fault eigenvectors which we have obtained to achieve recognition and diagnosis of the typical failures of rolling bearings.