Railway track quality assessment method based on vehicle system identification

Luber, Bernd

doi:10.1007/s00502-009-0635-3

Cited by 12 publications

(5 citation statements)

References 2 publications

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“…Some researches characterize the behavior of the vehicletrack system as linear transfer functions and estimate parameters based on system identifcation theory [7][8][9]. However, system identifcation theory can be applied to only the linear system and only constant speed conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Hao

Zhang

et al. 2022

Shock and Vibration

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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.

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Section: Introductionmentioning

confidence: 99%

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

Yang

Hao

Zhang

et al. 2022

Shock and Vibration

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“…In addition, the methodology of neural network was also employed by Sadeghi et al (2012) to establish the correlations between the track structural conditions and the data obtained from automated inspections. Luber (2009) proposed a method for track geometry assessment by calculating a vehicle response on the basis of system identifications. Sadeghi et al (2009Sadeghi et al ( , 2010 suggested incorporating rail cant into the development of track geometry indexes and proposed a coefficient to each geometry parameter based on its role in the overall quality condition of the track.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Assessing Railway Track Quality Based on Ensemble Empirical Mode Decomposition

Li¹,

Lei²,

Gao³

2016

International Conference on Transportation and Development 2016

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The quality of railway track geometry has a critical influence on the safety and comfort of train operations. In order to assess track geometry quality more effectively, the factor of track geometry wavelength should be considered. In this paper, a novel approach of assessment of track geometry quality based on intrinsic mode function (IMF), which derived from the measured data of track geometry by their characteristic wavelength scales, is presented. For this purpose, the frequency spectrum and the energy of each IMF of track geometry are calculated via fast Fourier transform method. The energy coefficient of each IMF of track geometry is defined as the ratio of each energy of IMFs to the total energy of the track geometry, and then the new parameter index is obtained with the corresponding calculation. In comparison with Track Quality Index (TQI) which is the main management method of track geometry in China, the new approach has been proved to be a general form of TQI. Finally, the track geometry data collected by track recording vehicles from Hefei-Wuhan high speed railway is studied using the new parameter index. The statistic analyzing results indicate that the new parameter index is the development and extension of Track Quality Index and can assess the track quality accurately and effectively.

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“…VIV exists mostly and commonly in the high-speed railway (HSR) system. In all HSR systems, the track irregularity-induced vibration certainly serves as the main source and has attracted lots of attention in both engineering and scientific committees [1,2].…”

Section: Introductionmentioning

confidence: 99%

Track Irregularity Assessment in High-Speed Rail by Incorporating Carriage-Body Acceleration with Transfer Function

Niu

Liu

Zhou

2020

Mathematical Problems in Engineering

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The determination of the precise track irregularity with unfavorable wavelength, which shall induce vehicle’s violent vibration in terms of the vehicle’s speeds, still challenges the researchers. This study proposes a feasible study of assessing the track irregularity by using the transfer function and the measured carriage-body acceleration by combining the ARX model with state space model. The ARX model and state space model are constructed using system identification to obtain the transfer relation between the track irregularity and the carriage-body acceleration, respectively. The model’s parameters are estimated by the measured data from the high-speed China Railway Comprehensive Inspection Train (CRCIT). The correlation value between the predicted and measured carriage-body acceleration shows that both models can effectively represent the transfer characteristics between the track irregularity and the carriage-body acceleration. Furthermore, the models can help assess the proportion of the vibration caused by track irregularity with the specific wavelengths and determine the track irregularity with unfavorable wavelength.

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Railway track quality assessment method based on vehicle system identification

Cited by 12 publications

References 2 publications

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

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

A Novel Approach to Assessing Railway Track Quality Based on Ensemble Empirical Mode Decomposition

Track Irregularity Assessment in High-Speed Rail by Incorporating Carriage-Body Acceleration with Transfer Function

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