Estimation of Vehicle Dynamic Response from Track Irregularity Using Deep Learning Techniques

To improve the quality of track maintenance work, it is a desire to estimate vehicle dynamic behavior from track geometry irregularities. This paper proposes a deep learning model to predict vehicle responses (e.g., vertical wheel-rail forces, wheel unloading rate, and car body vertical acceleration...

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Bibliographic Details
Published in:Shock and vibration 2022-12, Vol.2022, p.1-9
Main Authors: Yang, Fei, Hao, Xiaoli, Zhang, Hang, Jiang, Jie, Fan, Zhixing, Wei, Zilong
Format: Article
Language:English
Subjects:
Acceleration
Accuracy
Artificial neural networks
Datasets
Deep learning
Dynamic response
Geometry
High speed rail
Irregularities
Machine learning
Methods
Multibody systems
Neural networks
Predictions
Simulation
Simulation models
Software
Vertical forces
Waveforms
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Summary:To improve the quality of track maintenance work, it is a desire to estimate vehicle dynamic behavior from track geometry irregularities. This paper proposes a deep learning model to predict vehicle responses (e.g., vertical wheel-rail forces, wheel unloading rate, and car body vertical acceleration) using deep learning techniques. In the proposed CA-CNN-MUSE model, convolutional neural networks (CNNs) are used to learn features of track irregularities, and multiscale self-attention mechanisms (MUSE) are employed to capture the long-term and short-term trends of sequences. Coordinate attention (CA) is introduced into CNN to focus on important interchannel relationships and important spatial mileage points. The experiments were performed on a multibody simulation model of the vehicle system and the measured data of the actual high-speed line. The results show that the CA-CNN-MUSE has high prediction accuracy for vertical vehicle responses and fast computation speed. The predicted time-domain waveforms and power spectral densities (PSDs) agree well with the actual vehicle responses. The main features of the lateral vehicle responses can also be captured by the proposed method, yet the results are not as good as the vertical ones.
ISSN:1070-9622
1875-9203
DOI:10.1155/2022/2136464