Volterra核函数法在轴承滚珠磨损中的特征提取及应用

Application of the Volterra Kernel Function Method in Feature Extraction of Bearing Ball Wear

针对滚动轴承滚珠磨损故障特征难以提取的问题,提出一种基于多脉冲激励法下的Volterra级数核的求解算法.该方法是一种非线性系统模型的"交叉"诊断法,利用轴承系统输入输出的采样信号,建立Volterra非线性辨识系统模型,并运用多脉冲激励Volterra低阶核求解算法,将得到的低阶核通过时域和频域进行对比来判断轴承当前所处的运行状态.该文以无心车床主轴轴承为例进行实验验证,并与传统的小波分析法对比得出:多脉冲激励法能够方便准确地提取轴承的故障特征,该方法对此类故障的诊断具有一定的借鉴意义.

The fault features are difficult to be extracted from the worn rolling ball bearings. To tackle this problem, an algorithm of the Yolterra series kernel based on the multiple-pulse excitation method was proposed. This method belongs to the cross diagnosis for nonlinear system models, which utilizes the sampled signal input and output of the bearing system to establish the Volterra nonlinear identification system model and applies the Volterra low-order kernel algorithm based on the multiple-pulse excitation method to obtain the low-order kernel, then the low-order kernel will be compared in aspects of the GIRF and the GFRF to estimate the present running state of the bearing system. The major bearing of a centerless lathe was taken for exam- ple to verify this method through experiment. In contrast to the traditional wavelet analysis method, the multiple-pulse excitation method helps extract the fault features of the ball bearing conveniently and exactly. Thus, the proposed method has much significance to the diagnosis of such faults.