Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
As a key component of rail vehicle operation, the running condition of the wheelset significantly affects the operational safety of track vehicles. The wheel diameter, flange thickness, and flange height are key dimensional parameters of the wheelset, which directly influence the correct position of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/21/11935 |
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| author | Duo Ye Jing Wen Shubin Zheng Qianwen Zhong Wanrong Pei Hongde Jia Chuanping Zhou Youping Gong |
| author_facet | Duo Ye Jing Wen Shubin Zheng Qianwen Zhong Wanrong Pei Hongde Jia Chuanping Zhou Youping Gong |
| author_sort | Duo Ye |
| collection | DOAJ |
| description | As a key component of rail vehicle operation, the running condition of the wheelset significantly affects the operational safety of track vehicles. The wheel diameter, flange thickness, and flange height are key dimensional parameters of the wheelset, which directly influence the correct position of wheelsets on the track, and the train needs to be continuously monitored during the passenger operation. A prediction model for the key parameters of the wheelset is established based on LSTM (long short-term memory) neural network, and real measured data of wheelsets from the Shanghai Metro vehicles are selected. The predicted results of the model are compared and analyzed, and the results show that the LSTM-based prediction model for key parameters of wheelsets performs well, with the mean absolute percentage errors (MAPEs) for wheel diameter, flange thickness, and flange height being 0.08%, 0.42%, and 0.44%, respectively, for the left wheel and 0.07%, 0.35%, and 0.44%, respectively, for the right wheel. The prediction model for the train wheelset parameters established in this paper has a good prediction accuracy. By predicting the key parameters of the wheelset, the faults and causes of the wheelset can be found and determined, which is helpful for engineers to overhaul the wheelset faults, make maintenance plans, and perform preventive maintenance. |
| first_indexed | 2024-03-11T11:34:08Z |
| format | Article |
| id | doaj.art-effddfd4f89d435192f2bb26648ded50 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T11:34:08Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-effddfd4f89d435192f2bb26648ded502023-11-10T14:59:13ZengMDPI AGApplied Sciences2076-34172023-10-0113211193510.3390/app132111935Prediction of Key Parameters of Wheelset Based on LSTM Neural NetworkDuo Ye0Jing Wen1Shubin Zheng2Qianwen Zhong3Wanrong Pei4Hongde Jia5Chuanping Zhou6Youping Gong7School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Zhejiang University, Hangzhou 310030, ChinaAs a key component of rail vehicle operation, the running condition of the wheelset significantly affects the operational safety of track vehicles. The wheel diameter, flange thickness, and flange height are key dimensional parameters of the wheelset, which directly influence the correct position of wheelsets on the track, and the train needs to be continuously monitored during the passenger operation. A prediction model for the key parameters of the wheelset is established based on LSTM (long short-term memory) neural network, and real measured data of wheelsets from the Shanghai Metro vehicles are selected. The predicted results of the model are compared and analyzed, and the results show that the LSTM-based prediction model for key parameters of wheelsets performs well, with the mean absolute percentage errors (MAPEs) for wheel diameter, flange thickness, and flange height being 0.08%, 0.42%, and 0.44%, respectively, for the left wheel and 0.07%, 0.35%, and 0.44%, respectively, for the right wheel. The prediction model for the train wheelset parameters established in this paper has a good prediction accuracy. By predicting the key parameters of the wheelset, the faults and causes of the wheelset can be found and determined, which is helpful for engineers to overhaul the wheelset faults, make maintenance plans, and perform preventive maintenance.https://www.mdpi.com/2076-3417/13/21/11935LSTM neural networkwheelset wearparameter predictiondeep learningreprofiling plan |
| spellingShingle | Duo Ye Jing Wen Shubin Zheng Qianwen Zhong Wanrong Pei Hongde Jia Chuanping Zhou Youping Gong Prediction of Key Parameters of Wheelset Based on LSTM Neural Network Applied Sciences LSTM neural network wheelset wear parameter prediction deep learning reprofiling plan |
| title | Prediction of Key Parameters of Wheelset Based on LSTM Neural Network |
| title_full | Prediction of Key Parameters of Wheelset Based on LSTM Neural Network |
| title_fullStr | Prediction of Key Parameters of Wheelset Based on LSTM Neural Network |
| title_full_unstemmed | Prediction of Key Parameters of Wheelset Based on LSTM Neural Network |
| title_short | Prediction of Key Parameters of Wheelset Based on LSTM Neural Network |
| title_sort | prediction of key parameters of wheelset based on lstm neural network |
| topic | LSTM neural network wheelset wear parameter prediction deep learning reprofiling plan |
| url | https://www.mdpi.com/2076-3417/13/21/11935 |
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