基于小波包参数模型的滚动轴承智能故障诊断

针对平稳自回归模型无法准确描述滚动轴承振动信号的非平稳性,提出一种结合小波包分解与自回归模型的故障特征提取方法,以提取能准确反映轴承运行状态的特征向量。首先,通过小波包变换对滚动轴承运行时产生的非平稳振动信号进行分解,得到一系列刻画原始信号特征的系数;然后,利用自相关算法对各系数建立自回归模型,并将自回归模型的参数作为特征向量;最后,采用支持向量机分类器对提取的特征向量进行故障分类,从而实现滚动轴承的智能故障诊断。仿真结果表明该方法的有效性。

Intelligent Fault Diagnosis of Rolling Bearing Based on Wavelet Packet Autoregressive Model

Since the non-stationary of vibration signals cannot be fully described by the stationary autoregression model,a feature extraction approach based on wavelet packet decomposition(WPD) and autoregressive(AR) model is proposed,and then the feature vectors are extracted to accurately reflect the running state of rolling bearing.Firstly,the non-stationary signals generated by rolling bearing vibration are decomposed into some coefficients by wavelet packet transformation.Then,the coefficients are modeled as AR model and the parameters of AR model are used as the feature vectors.Finally,fault patterns are recognized by the feature vectors using support vector machine(SVM) classifier,consequently the intelligent fault diagnosis is realized.The simulation results show the effectiveness of the proposed method.