基于时变自回归模型与神经网络的滚动轴承故障智能诊断

Fault Intelligent Diagnosis for Rolling Bearings Based on Time Varying Autoregressive Model and Neural Network

使用时变自回归建模分析方法建立滚动轴承振动信号特征提取模型,基于基函数算法求解该模型的时变参数,并采用AIC准则确定模型阶数。在利用上述参数化模型对轴承振动信号进行特征提取的基础上,构建BP神经网络,有效地实现了轴承故障的智能诊断。

The feature extraction model for vibration signal of rolling bearings is established by using time varying autoregressive modeling method. The time varying parameters for the model is solved based on basis function arithmetic, and the model order is determined by using AIC rule. On the basis of above - mentioned parameterized model for feature extraction, a BP neural network is built, and the intelligent diagnosis for fault of rolling bearings is effectively realized.