基于AR模型和径向基神经网络的滚动轴承故障诊断
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 fea...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2004-01-01
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| Series: | Jixie chuandong |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2004.05.003 |
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| Summary: | 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. |
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| ISSN: | 1004-2539 |