Deep Learning for Track Quality Evaluation of High-Speed Railway Based on Vehicle-Body Vibration Prediction

Document identifier:
Access full text here:10.1109/ACCESS.2019.2960537
Keyword: Engineering and Technology, Civil Engineering, Other Civil Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Annan samhällsbyggnadsteknik, Track quality evaluation, Track geometry, Vehicle-body vibration, Convolutional neural network (CNN), Long short-term memory (LSTM), CNN-LSTM, Drift och underhållsteknik, Operation and Maintenance
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 11 Sustainable cities and communities
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Track quality evaluation is fundamental for track maintenance. Around the world, track geometry standards are established to evaluate track quality. However, these standards may not be capable of detecting some abnormal track geometry conditions that can cause considerable vehicle-body vibration. And people gradually realized that track quality evaluation should be based not only on track geometry but also on vehicle performance. Vehicle-body vibration prediction is beneficial for locating potential track geometry defects, and the predicted accelerations can be used as an auxiliary index for assessing track quality. For this purpose, this paper gives a method to predict vehicle-body vibration based on deep learning, which represents one of the newest areas in artificial intelligence. By integrating convolutional neural network (CNN) and long short-term memory (LSTM), a CNN-LSTM model is proposed to make accurate and point-wise prediction. To achieve the optimal performance and explore the internal mechanism of the model, structural configurations and inner states are extensively studied. CNN-LSTM can take advantage of the powerful feature extraction capacity of CNN and LSTM, and outperforms the fully-connected neural network and the plain LSTM on the experimental data of a high-speed railway. In detail, CNN-LSTM has superior performance in predicting vertical vehicle-body vibration below 10 Hz and lateral vehicle-body vibration below 1 Hz. Moreover, analysis shows that the predicted vehicle-body acceleration can act as a performance-based evaluation index of track quality.


Shuai Ma

School of Civil Engineering, Beijing Jiaotong University, Beijing, China
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Liang Gao

School of Civil Engineering, Beijing Jiaotong University, Beijing, China
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Xiubo Liu

Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, China
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Jing Lin

Luleå tekniska universitet; Drift, underhåll och akustik
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