Application Research on Deep Convolution Neural Network Based Fault Diagnosis Technology for Traction Converter
Converter is a key component of traction system in electric locomotive. The fault of converter can easily lead to the paralysis of train operation and is one of the most dangerous failures of electric locomotive. In order to avoid poor generalization of feature selection in expert experience and sim...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2021-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.010 |
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| Summary: | Converter is a key component of traction system in electric locomotive. The fault of converter can easily lead to the paralysis of train operation and is one of the most dangerous failures of electric locomotive. In order to avoid poor generalization of feature selection in expert experience and simulation mode in traction converter fault diagnosis, this paper proposes a fault diagnosis method based on deep convolution neural network. Structure parameters in convolutional and pooling layer in Xception model are modified to match the fault data of the traction converter for model training. Experimental results show that the accuracy of Top-1 is 0.842 2 and the accuracy of Top-3 is 0.920 1, which indicates that the proposed method is robust and accurate for fault diagnosis of traction converter, and channels enhancing can provide better generalization for model and realize fault classification. |
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| ISSN: | 2096-5427 |