Design and Verification of Simulation Data Generator for Rail Transit Switch Machine Oriented to Intelligent Operation and Maintenance
［Objective］ Difficulty in easily obtaining fault data of various rail transit equipment leads to insufficient data to support the research on machine intelligence algorithms such as fault diagnosis and prediction. In order to meet the urgent need of intelligent rail transit operation and maintenance...
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| Format: | Article |
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Urban Mass Transit Magazine Press
2025-01-01
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| Series: | Chengshi guidao jiaotong yanjiu |
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| Online Access: | https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.2025.01.034.html |
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| author | ZOU Jinbai WEI Shiyan LIU Jiang SHA Quan WU Jie JI Guoyi |
| author_facet | ZOU Jinbai WEI Shiyan LIU Jiang SHA Quan WU Jie JI Guoyi |
| author_sort | ZOU Jinbai |
| collection | DOAJ |
| description | ［Objective］ Difficulty in easily obtaining fault data of various rail transit equipment leads to insufficient data to support the research on machine intelligence algorithms such as fault diagnosis and prediction. In order to meet the urgent need of intelligent rail transit operation and maintenance for a large amount of training data, it is necessary to design and verify the simulation data generator (hereinafter abbreviated as SD generator) of rail transit switch machine. ［Method］ The characteristics of S700K type switch machine power curves under normal operation and gradual fault conditions are analyzed, and causes of the faults are discussed. By comparing two simulation data generation methods, a switch machine SD generator is designed based on the Borderline-Smote algorithm. Through building a platform for SD generator, and using the time series features of learning the power data by LSTM (long short-term memory) prediction model, three characteristics of the generated gradual fault power data such as the crest factor, standard deviation, and variance are tested. ［Result & Conclusion］ The LSTM prediction model trained on the power data generated by SD generator can predict the power change trend of the S700K type switch machine. The root mean square errors of crest factor, standard deviation, and variance calculated by the LSTM prediction model and the periodic replication method are 0.335 5, 0.023 9, and 0.024 1 respectively. The relatively small errors prove the authenticity and feasibility of SD generator. |
| format | Article |
| id | doaj-art-a8dfafd00ecc4f209beeec02ed65bc51 |
| institution | Kabale University |
| issn | 1007-869X |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Urban Mass Transit Magazine Press |
| record_format | Article |
| series | Chengshi guidao jiaotong yanjiu |
| spelling | doaj-art-a8dfafd00ecc4f209beeec02ed65bc512025-01-13T08:04:42ZzhoUrban Mass Transit Magazine PressChengshi guidao jiaotong yanjiu1007-869X2025-01-0128118819210.16037/j.1007-869x.2025.01.034Design and Verification of Simulation Data Generator for Rail Transit Switch Machine Oriented to Intelligent Operation and MaintenanceZOU Jinbai0WEI Shiyan1LIU Jiang2SHA Quan3WU Jie4JI Guoyi5School of Railway Transportation, Shanghai Institute of Technology, 201400, Shanghai, ChinaSchool of Railway Transportation, Shanghai Institute of Technology, 201400, Shanghai, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, 100044, Beijing, ChinaSchool of Railway Transportation, Shanghai Institute of Technology, 201400, Shanghai, ChinaTelecom & Signaling Branch, Shanghai Metro Maintenance Support Co., Ltd., 200235, Shanghai, ChinaSchool of Railway Transportation, Shanghai Institute of Technology, 201400, Shanghai, China［Objective］ Difficulty in easily obtaining fault data of various rail transit equipment leads to insufficient data to support the research on machine intelligence algorithms such as fault diagnosis and prediction. In order to meet the urgent need of intelligent rail transit operation and maintenance for a large amount of training data, it is necessary to design and verify the simulation data generator (hereinafter abbreviated as SD generator) of rail transit switch machine. ［Method］ The characteristics of S700K type switch machine power curves under normal operation and gradual fault conditions are analyzed, and causes of the faults are discussed. By comparing two simulation data generation methods, a switch machine SD generator is designed based on the Borderline-Smote algorithm. Through building a platform for SD generator, and using the time series features of learning the power data by LSTM (long short-term memory) prediction model, three characteristics of the generated gradual fault power data such as the crest factor, standard deviation, and variance are tested. ［Result & Conclusion］ The LSTM prediction model trained on the power data generated by SD generator can predict the power change trend of the S700K type switch machine. The root mean square errors of crest factor, standard deviation, and variance calculated by the LSTM prediction model and the periodic replication method are 0.335 5, 0.023 9, and 0.024 1 respectively. The relatively small errors prove the authenticity and feasibility of SD generator.https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.2025.01.034.htmlrail transitswitch machinesimulation data generatorintelligent operation and maintenance |
| spellingShingle | ZOU Jinbai WEI Shiyan LIU Jiang SHA Quan WU Jie JI Guoyi Design and Verification of Simulation Data Generator for Rail Transit Switch Machine Oriented to Intelligent Operation and Maintenance Chengshi guidao jiaotong yanjiu rail transit switch machine simulation data generator intelligent operation and maintenance |
| title | Design and Verification of Simulation Data Generator for Rail Transit Switch Machine Oriented to Intelligent Operation and Maintenance |
| title_full | Design and Verification of Simulation Data Generator for Rail Transit Switch Machine Oriented to Intelligent Operation and Maintenance |
| title_fullStr | Design and Verification of Simulation Data Generator for Rail Transit Switch Machine Oriented to Intelligent Operation and Maintenance |
| title_full_unstemmed | Design and Verification of Simulation Data Generator for Rail Transit Switch Machine Oriented to Intelligent Operation and Maintenance |
| title_short | Design and Verification of Simulation Data Generator for Rail Transit Switch Machine Oriented to Intelligent Operation and Maintenance |
| title_sort | design and verification of simulation data generator for rail transit switch machine oriented to intelligent operation and maintenance |
| topic | rail transit switch machine simulation data generator intelligent operation and maintenance |
| url | https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.2025.01.034.html |
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