多脉冲激励法在轴承滚珠磨损中的状态研究及应用
State Research and Application of Multiple-pulse Excitation Method in Diagnosis of Bearing Ball Wear

机械故障中的轴承故障具有强的非线性特征，其故障特征提取具有一定的困难，针对此问题提出了一种基于多脉冲激励法下的Volterra级数核的故障特征提取方法。通过系统的输入输出信号并建立非线性系统的Volterra级数模型，利用时域和频域的低阶核进行轴承所处状态的对比，进而分析是否处于故障状态。以美国西储大学滚动轴承故障为例来验证该算法，应用小波算法和多脉冲激励法进行对比，得出多脉冲激励法能够较为方便准确地提取轴承的故障特征。
The bearing failure in mechanical system has strong non-linear characteristic, and its fault feature extraction is very difficult. In order to solve this problem, a new fault feature extraction method based on the Volterra series kernel under multi-pulse excitation is proposed. The nonlinear system Volterra series model is established through input and output signals of the system. Using low-order nuclear, and the time domain and frequency domain, the status of the bearing is determined and then analyzed whether the bearing in the fault states. The method is validated by taking the fault of rolling bearing in the University of Western Reserve as an example, the wavelet algorithm and multiple pulse excitation method were compared. It is concluded that the multi-pulse excitation method can extract the bearing fault features conveniently and accurately