基于小波包和支持向量机的滚动轴承故障模式识别

为了解决对故障轴承的特征提取和故障特征准确分类问题,提出了应用小波包变换和支持向量机相结合进行滚动轴承故障诊断的方法.小波包变换具有良好的时-频局部化特征,非常适于对瞬态或时变信号进行特征提取.而支持向量机可完成模式识别和非线性回归.利用上述原理根据轴承振动信号的频域变化特征,采用小波包变换对其提取频域能量特征向量,然后利用建立的支持向量机多故障分类器完成滚动轴承故障模式的识别.试验结果表明,支持向量机可以有效、准确地识别轴承的故障模式,为轴承故障诊断向智能化发展提供了新的途径.

Fault Pattern Recognition of Rolling Bearing Based on Wavelet Packet and Support Vector Machine

The method of fault recognition of rolling bearings based on wavelet packet transform and support vector machine was presented, in order to solve the feature extracting and feature classifying of fault bearings diagnosis. Wavelet packet transform, as a new technique of signal processing, possesses excellent characteristic of time -frequency localization and is suitable for analyzing the time -varying or transient signals. Support vector machine is capable of pattern recognition and nonlinear regression. According to the frequency domain feature of vibration signal of ball bearing, energy eigenvector of frequency domain was extracted using wavelet packet transform method. Fault pattern of rolling bearing was recognized using support vector machine multiple fault classifier. Theory and experiment shows that this method is available to recognize the fault pattern accurately and provides a new approach to intelligent fault diagnosis.