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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| Format: | Article |
| Language: | zho |
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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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| _version_ | 1849224788200914944 |
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| author | LI Chen ZHANG Huiyuan LIU Yong YANG Weifeng |
| author_facet | LI Chen ZHANG Huiyuan LIU Yong YANG Weifeng |
| author_sort | LI Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3693382de7d54a85804bcd23b2cdeada |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-3693382de7d54a85804bcd23b2cdeada2025-08-25T06:53:40ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272021-01-0138606582324172Application Research on Deep Convolution Neural Network Based Fault Diagnosis Technology for Traction ConverterLI ChenZHANG HuiyuanLIU YongYANG WeifengConverter 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.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.010traction converterdeep learningconvolution neural networkfault diagnosis |
| spellingShingle | LI Chen ZHANG Huiyuan LIU Yong YANG Weifeng Application Research on Deep Convolution Neural Network Based Fault Diagnosis Technology for Traction Converter Kongzhi Yu Xinxi Jishu traction converter deep learning convolution neural network fault diagnosis |
| title | Application Research on Deep Convolution Neural Network Based Fault Diagnosis Technology for Traction Converter |
| title_full | Application Research on Deep Convolution Neural Network Based Fault Diagnosis Technology for Traction Converter |
| title_fullStr | Application Research on Deep Convolution Neural Network Based Fault Diagnosis Technology for Traction Converter |
| title_full_unstemmed | Application Research on Deep Convolution Neural Network Based Fault Diagnosis Technology for Traction Converter |
| title_short | Application Research on Deep Convolution Neural Network Based Fault Diagnosis Technology for Traction Converter |
| title_sort | application research on deep convolution neural network based fault diagnosis technology for traction converter |
| topic | traction converter deep learning convolution neural network fault diagnosis |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.010 |
| work_keys_str_mv | AT lichen applicationresearchondeepconvolutionneuralnetworkbasedfaultdiagnosistechnologyfortractionconverter AT zhanghuiyuan applicationresearchondeepconvolutionneuralnetworkbasedfaultdiagnosistechnologyfortractionconverter AT liuyong applicationresearchondeepconvolutionneuralnetworkbasedfaultdiagnosistechnologyfortractionconverter AT yangweifeng applicationresearchondeepconvolutionneuralnetworkbasedfaultdiagnosistechnologyfortractionconverter |