基于时序-RBF神经网络的齿轮故障诊断方法
Due to incipient fault features of gear being not obvious,a method based on time series analysis and radial basis function neural networks is proposed.First the vibratory signals in normal and fault states have been analyzed by time series analysis respectively,so state features can be extracted eff...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2008-01-01
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| Series: | Jixie chuandong |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2008.04.017 |
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| Summary: | Due to incipient fault features of gear being not obvious,a method based on time series analysis and radial basis function neural networks is proposed.First the vibratory signals in normal and fault states have been analyzed by time series analysis respectively,so state features can be extracted effectively by the time series model’s autoregressive coefficients.Then the autoregressive coefficients make up the eigenvectors which are taken as inputs for neural networks training.Consequently the identification and diagnosis of gears in different working conditions,such as normal,crack,gear tooth broken,and partial pitting etc.have been accomplished.The diagnosis result shows that the method based on time series analysis and RBF neural network is feasible for multiple or early fault classification. |
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| ISSN: | 1004-2539 |