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Strategic Study of CAE >> 2004, Volume 6, Issue 2

Fault Pattern Recognition of Rolling Bearing Based on Radial Basis Function Neural Networks

College of Machinery and Engineering , Jilin University , Changchun 130025 , China

Funding project:吉林省教育委员会基金资助项目(吉教合字99第10号) Received: 2003-08-21 Revised: 2003-10-23 Available online: 2004-02-20

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Abstract

Radial basis function neural network is a type of three — layer feedforward network. It has many good properties, such as powerful ability for function approximation, classification and learning rapidly. 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 parameters. In the light of the theory of radial basis function neural networks, fault pattern of rolling bearing is recognized correspondingly. Theory and experiment show that the recognition of fault pattern of rolling bearing based on radial basis function neural networks theory is available and its precision is high.

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