An intelligent fault diagnosis system of rolling bearing

State monitoring and fault diagnosing of rolling bearing by analyzing vibration signal is one of the major problems which need to be solved in engineering. On the basis of the feature analysis of vibration signal of rolling bearing, the AR model is established to reduce the dimension of the Euclidean space. The pattern of characteristic space and fault space is presented. Radial basis function neural networks is employed based on the AR model parameters. In the light of the theory of the RBF networks, the fault pattern is recognized correspondingly. The intelligent fault diagnosis system of rolling bearing is achieved using Matlab. Theory and experiment show that the system is available and precise.