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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Urban Mass Transit Magazine Press
2025-01-01
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| Series: | Chengshi guidao jiaotong yanjiu |
| Subjects: | |
| Online Access: | https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.2025.01.034.html |
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| Summary: | ［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. |
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| ISSN: | 1007-869X |