scholarly journals Rail Corrugation Detection of High-Speed Railway Using Wheel Dynamic Responses

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Jianbo Li ◽  
Hongmei Shi
Keyword(s):  
High Speed ◽  
Estimation Error ◽  
Estimation Method ◽  
Dynamic Responses ◽  
Support Vector ◽  
Vibration Acceleration ◽  
High Speed Railway ◽  
Rail Corrugation ◽  
Reconstructed Signal ◽  
Wheel Vibration

Rail corrugation often occurs on the high-speed railway, which will affect ride comfort and even the train operation safety in severe condition. Detection of rail corrugation wavelength and depth is absolutely essential for maintenance and safety. A novel method using wheel vibration acceleration is proposed in this paper, in which ensemble empirical mode decomposition (EEMD) is employed to estimate the wavelength, and bispectrum features are extracted to recognize the depth with support vector machine (SVM). Firstly, a vehicle-track coupling model considering the rail corrugation of high-speed railway is established to calculate the wheel vibration acceleration. Secondly, the estimation algorithm of wavelength is studied by analyzing the main frequency with EEMD. The optimal parameters of EEMD are selected according to the orthogonal coefficient of decomposition results and the distribution of the extreme points of signal. The depth detection is transformed to a classification problem with SVM. Bispectrum features, which are extracted from the reconstructed signal using the high-frequency components of wheel vibration acceleration, combining with train speed and corrugation wavelength are input into SVM to recognize the rail corrugation depth. Finally, numerical simulation is carried out to verify the accuracy of the proposed estimation method. The simulation results show that the proposed detection algorithm can accurately identify rail corrugation, the estimation error of rail corrugation wavelength is less than 0.25%, and the classification accuracy of rail corrugation depth is more than 99%.

Estimation of Track Irregularity Based on Genetic Algorithm and Unscented Kalman Filtering

2012 ◽  
Author(s):  
Hongmei Shi ◽  
Zujun Yu
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filtering ◽  
High Speed ◽  
Measurement Data ◽  
Estimation Method ◽  
Coupling Model ◽  
Dynamic Responses ◽  
The Real ◽  
True Value ◽  
Track Irregularity

Track irregularity is the main excitation source of wheel-track interaction. Due to the difference of speed, axle load and suspension parameters between track inspection train and the operating trains, the data acquired from the inspection car cannot completely reflect the real status of track irregularity when the operating trains go through the rail. In this paper, an estimation method of track irregularity is proposed using genetic algorithm and Unscented Kalman Filtering. Firstly, a vehicle-track vertical coupling model is established, in which the high-speed vehicle is assumed as a rigid body with two layers of spring and damping system and the track is viewed as an elastic system with three layers. Then, the static track irregularity is estimated by genetic algorithm using the vibration data of vehicle and dynamic track irregularity which are acquired from the inspection car. And the dynamic responses of vehicle and track can be solved if the static track irregularity is known. So combining with vehicle track coupling model of different operating train, the potential dynamic track irregularity is solved by simulation, which the operating train could goes through. To get a better estimation result, Unscented Kalman Filtering (UKF) algorithm is employed to optimize the dynamic responses of rail using measurement data of vehicle vibration. The simulation results show that the estimated static track irregularity and the vibration responses of vehicle track system can go well with the true value. It can be realized to estimate the real rail status when different trains go through the rail by this method.

Dynamic Responses of Bridge–Tunnel Overlapping Structure for High-Speed Railway Under Different Seismic Excitations

2018 ◽  
Vol 37 (1) ◽  
pp. 43-60
Author(s):  
Guangchen Sun ◽  
Jiayou Xie ◽  
Shan He ◽  
Helin Fu ◽  
Xueliang Jiang ◽  
...  
Keyword(s):  
High Speed ◽  
Dynamic Responses ◽  
Seismic Excitations ◽  
High Speed Railway

Mapping relationship between dynamic responses of high-speed trains and additional bridge deformations

2020 ◽  
pp. 107754632093689
Author(s):  
Hongye Gou ◽  
Chang Liu ◽  
Hui Hua ◽  
Yi Bao ◽  
Qianhui Pu
Keyword(s):  
High Speed ◽  
Coupled Model ◽  
Dynamic Responses ◽  
High Speed Railway ◽  
Railway Bridges ◽  
Running Safety ◽  
High Speed Trains ◽  
Track Bridge ◽  
The Relationship ◽  
Track Deformation

Deformations of high-speed railways accumulate over time and affect the geometry of the track, thus affecting the running safety of trains. This article proposes a new method to map the relationship between dynamic responses of high-speed trains and additional bridge deformations. A train–track–bridge coupled model is established to determine relationship between the dynamic responses (e.g. accelerations and wheel–rail forces) of the high-speed trains and the track deformations caused by bridge pier settlement, girder end rotation, and girder camber. The dynamic responses are correlated with the track deformation. The mapping relationship between bridge deformations and running safety of trains is determined. To satisfy the requirements of safety and riding comfort, the suggested upper thresholds of pier settlement, girder end rotation, and girder camber are 22.6 mm, 0.92‰ rad, and 17.2 mm, respectively. This study provides a method that is convenient for engineers in evaluation and maintenance of high-speed railway bridges.

Analysis of Dynamic Responses of Bridge-Subgrade Transition of High-Speed Railway

2011 ◽  
Vol 90-93 ◽  
pp. 189-196 ◽  
Author(s):  
Chang Wei Yang ◽  
Jian Jing Zhang ◽  
Chuan Bin Zhu
Keyword(s):  
High Speed ◽  
Deflection Angle ◽  
Coupled System ◽  
Dynamic Responses ◽  
Practical Situation ◽  
High Speed Railway ◽  
Ballastless Track ◽  
Track Dynamics ◽  
Coupling Dynamics ◽  
Analysis Models

Referred the vehicle-track coupling dynamics theory [1] and the vertical dynamic analysis models of Bridge-Subgrade transition developed by Zhai [2] ，Wang [3] and others [4]. This article takes account of the interaction between different structural layers in the subgrade system further by using the dynamic ballastless track model and finally establishes a space dynamic numerical model of the vehicle-track-subgrade coupled system. The dynamic response of the coupled system is analyzed when the speed of the train is 350km/h and the transition is filled with graded broken stones mixed with cement of 3%. Results show that the setting forms of Bridge-Subgrade transition have little effect on the dynamic responses, so designers can choose it on account of the practical situation. Due to the location away from abutment about 5m has greater deformation; the stiffness within 5m should be designed alone. Based on the study from vehicle-track dynamics, we suggest that the maximum allowable track deflection angle is 0.9‰ and K30190Mpa within 5m behind the abutment.

Short-Time Fluctuation Characteristic and Combined Forecasting of High-Speed Railway Passenger Flow Based on EEMD

2013 ◽  
Vol 409-410 ◽  
pp. 1071-1074
Author(s):  
Xiu Shan Jiang ◽  
Rui Feng Zhang ◽  
Liang Pan
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Mode Decomposition ◽  
Railway Passenger ◽  
Short Term Forecasting

Take Wuhan-Guangzhou high-speed railway for example. By adopting the empirical mode decomposition (EMD) attempt to analyze mode from the perspective of volatility of high speed railway passenger flow fluctuation signal. Constructed the ensemble empirical mode decomposition-gray support vector machine (EEMD-GSVM) short-term forecasting model which fuse the gray generation and support vector machine with the ensemble empirical mode decomposition (EEMD). Finally, by the accuracy of predicted results, explains the EEMD-GSVM model has the better adaptability.

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