基于AR模型和径向基神经网络的滚动轴承故障诊断

径向基函数(RBF)神经网络是一种三层前馈型非线性神经网络，它具有较强的函数逼近能力和分类能力。根据径向基函数神经网络的优点，在对滚动轴承正常和故障振动信号特征分析的基础上，提出了采用时间序列方法对其建立AR模型，利用AR模型特征参数建立径向基函数神经网络，并用该网络对滚动轴承的故障信号进行了诊断。理论和试验证明了该方法的有效性，且具有较高的诊断精度。

Fault Diagnosis of Rolling Bearing Based on AR model and Radial Basis Function Neural Networks

Radial basis function neural network is a type of three-layer feedforward non-linear network. It has many good properties, such as powerful ability for function approximation, classification. In this paper, in the light of the merit of radial basis function neural network and on the basis of the feature analysis of vibration signal of rolling bearing, AR model is presented by using time series method. Radial basis function neural networks is established based on AR model parameter. In the light of the theory of radial basis function neural networks, fault pattern of rolling bearing is recognized correspondingly. Theory and experiment shows that the recognition of fault pattern of rolling bearing based on AR model and radial basis function neural networks theory is available and its precision is high.