基于独立分量分析的滚动轴承故障诊断

本文应用独立分量分析(ICA)将滚动轴承系统产生的声信号从传声器获取的声信号中分离出来,然后再采用基于morlet小波变换的包络分析进行再次降噪并获取特征信号,将此特征信号的特征频率与转子频率之比作为已经训练好的线性神经网络的输入向量,以对滚动轴承的运行状态做出判断.实验表明,此方法可靠、有效地诊断出了轴承的状态.

Fault diagnosis for rolling element bearings based on independent component analysis

To overcome the difficulty in extracting the characteristic signals for fault diagnosis of rolling element bearings,the independent component analysis(ICA) was employed to separate the acoustic signals generated by the rolling element bearing system from the mixed acoustic signals collected by microphone.Envelope analysis based on morlet wavelet transform was used to denoise again and extract the fault feature signals.Then,the ratio of the fault feature frequency of the fault feature signals and the frequency of the rotation frequency of the rotor was served as the input parameter of linear neural network to identify the fault pattern of rolling elemental bearings.Experimental result shows that the fault diagnosis approach for rolling element bearings is effective.