Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN)
As a commonly used mode of transportation in people’s daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting t...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/1547428 |
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| _version_ | 1832545233538121728 |
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| author | Weijie Tao Xiaowei Li Zheng Li |
| author_facet | Weijie Tao Xiaowei Li Zheng Li |
| author_sort | Weijie Tao |
| collection | DOAJ |
| description | As a commonly used mode of transportation in people’s daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting track circuit faults still relies on the experience of on-site personnel. In order to improve the efficiency and accuracy of fault diagnosis, we propose to establish an intelligent fault diagnosis system. Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). The LSTM network is established on the basis of fault data and used for ZPW-2000A track circuit fault diagnosis. However, the use of a single LSTM network has a high error rate in the common fault diagnosis of track circuits. Therefore, this paper proposes a feature extraction method based on the UNet network. This method is used to extract the features of the original data and then input them into the LSTM network for fault diagnosis. Through experiments with on-site fault data, it has been verified that this method can accurately classify seven common track circuit faults. Finally, the superiority of the method is verified by comparing it with other commonly used fault classification methods. |
| format | Article |
| id | doaj-art-5dbc6c885e924d77b3d14ecbaa807d24 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-5dbc6c885e924d77b3d14ecbaa807d242025-02-03T07:26:21ZengWileyJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/1547428Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN)Weijie Tao0Xiaowei Li1Zheng Li2School of Rail TransportationSchool of Rail TransportationTraffic Information Engineering and ControlAs a commonly used mode of transportation in people’s daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting track circuit faults still relies on the experience of on-site personnel. In order to improve the efficiency and accuracy of fault diagnosis, we propose to establish an intelligent fault diagnosis system. Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). The LSTM network is established on the basis of fault data and used for ZPW-2000A track circuit fault diagnosis. However, the use of a single LSTM network has a high error rate in the common fault diagnosis of track circuits. Therefore, this paper proposes a feature extraction method based on the UNet network. This method is used to extract the features of the original data and then input them into the LSTM network for fault diagnosis. Through experiments with on-site fault data, it has been verified that this method can accurately classify seven common track circuit faults. Finally, the superiority of the method is verified by comparing it with other commonly used fault classification methods.http://dx.doi.org/10.1155/2024/1547428 |
| spellingShingle | Weijie Tao Xiaowei Li Zheng Li Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN) Journal of Electrical and Computer Engineering |
| title | Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN) |
| title_full | Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN) |
| title_fullStr | Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN) |
| title_full_unstemmed | Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN) |
| title_short | Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN) |
| title_sort | track circuits fault diagnosis method based on the unet lstm network uln |
| url | http://dx.doi.org/10.1155/2024/1547428 |
| work_keys_str_mv | AT weijietao trackcircuitsfaultdiagnosismethodbasedontheunetlstmnetworkuln AT xiaoweili trackcircuitsfaultdiagnosismethodbasedontheunetlstmnetworkuln AT zhengli trackcircuitsfaultdiagnosismethodbasedontheunetlstmnetworkuln |