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Article

Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle

1
School of Industrial Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
2
Urban Transit Research Group, Advanced Railroad Vehicle Division, Korea Railroad Research Institute, Uiwan-si 16105, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Steven Chatterton and Lang Xu
Machines 2021, 9(7), 130; https://doi.org/10.3390/machines9070130
Received: 17 May 2021 / Revised: 23 June 2021 / Accepted: 26 June 2021 / Published: 29 June 2021
Train running safety is considered one of the key criteria for advanced highway trains and bogies. While a number of existing research studies have focused on its measurement and monitoring, this study proposes a new and effective train running a safety prediction framework. The wheel derail coefficient, wheel rate of load reduction, and wheel lateral pressure are considered the decision variables for the safety framework. Data for actual measured rail conditions and vibration-based signals are used as the input data. However, advanced trains and bogies are influenced more by their inertial structures and mechanisms than by railway conditions and external environments. In order to reflect their inertial influences, past data of output variables are used as recurrent data. The proposed framework shares advantages of a general deep neural network and a recurrent neural network. To prove the effectiveness of the proposed hybrid deep-learning framework, numerical analyses using an actual measured train-railway model and transit simulation are conducted and compared with the existing deep learning architectures. View Full-Text
Keywords: train running safety; hybrid deep learning; railway vehicle; vibration analysis train running safety; hybrid deep learning; railway vehicle; vibration analysis
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MDPI and ACS Style

Lee, H.; Han, S.-Y.; Park, K.; Lee, H.; Kwon, T. Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle. Machines 2021, 9, 130. https://doi.org/10.3390/machines9070130

AMA Style

Lee H, Han S-Y, Park K, Lee H, Kwon T. Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle. Machines. 2021; 9(7):130. https://doi.org/10.3390/machines9070130

Chicago/Turabian Style

Lee, Hyunsoo, Seok-Youn Han, Keejun Park, Hoyoung Lee, and Taesoo Kwon. 2021. "Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle" Machines 9, no. 7: 130. https://doi.org/10.3390/machines9070130

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