A multivariate statistical representation of railway track irregularities using ARMA models

2021 ◽  
pp. 1-17
Author(s):  
J. N. Costa ◽  
J. Ambrósio ◽  
D. Frey ◽  
A. R. Andrade
Keyword(s):  
Railway Track ◽  
Arma Models ◽  
Multivariate Statistical ◽  
Statistical Representation ◽  
Track Irregularities

scholarly journals Advanced Stochastic Modeling of Railway Track Irregularities

2013 ◽  
Vol 5 ◽  
pp. 401637 ◽  
Author(s):  
Mengyi Zhu ◽  
Xiaohui Cheng ◽  
Lixin Miao ◽  
Xinya Sun ◽  
Shuai Wang
Keyword(s):  
Stochastic Modeling ◽  
Railway Track ◽  
Track Irregularities

A Novel Algorithm for Predicting Track Irregularities of Unit Track Sections

2010 ◽  
Author(s):  
R. K. Liu ◽  
P. Xu ◽  
Q. X. Sun
Keyword(s):  
Short Range ◽  
Computer Software ◽  
Railway Track ◽  
Railway Transportation ◽  
Waveform Data ◽  
Track Geometry ◽  
Short Period ◽  
Track Condition ◽  
Inspection And Maintenance ◽  
Track Irregularities

During train runs, the interaction between train wheels and the rail track underneath makes track geometry change, which in turn results in all kinds of track irregularities. After the 6th train speed raise of China in 2007, railway transportation has shown three main new features: speed-raised, heavy-loading and high-density. Under these features, changes in railway track irregularities of China have also presented some new characteristics: higher deterioration rates of track irregularities and more frequent occurrences of track exceptions. To ensure the train operational safety and increase the transportation service quality, the preventive inspection and maintenance of railway track facilities have been put forward once again by railway maintenance departments of China. A precondition for the preventive inspection and maintenance is about how to accurately evaluate and predict the future track condition according to the historical track inspection data. In this paper, based on the characteristics of track irregularity changes and in accordance with the calculus thinking, we have developed a short-range prediction model called SRPM. The model uses track waveform data generated by the track geometry car (TGC) to predict track irregularities of a unit track section with the length of 100m for each day in a future short period of time. An algorithm for using SRPM to predict track irregularities has also been designed. According to the designed algorithm, using ORACLE database and computer program languages, we have programmed a computer software named P-SRPM. We then used P-SRPM to deal with 25 sets of TGC-generated track waveform data from the up going track of the Beijing-Shanghai railway (Jing-Hu railway) administrated by Jinan Railway Bureau (JRB) and predicted track irregularities of unit sections in the railway track segment. Finally, errors in these predictions were analyzed in both temporal and spatial dimensions. From the error analysis results, we come to the conclusion that SRPM can fairly accurately make short-range predictions for track irregularities of each unit section in the JRB-administrated Jing-Hu railway track (up going).

Markov-based model for the prediction of railway track irregularities

2013 ◽  
Vol 229 (2) ◽  
pp. 150-159 ◽  
Author(s):  
Lei Bai ◽  
Rengkui Liu ◽  
Quanxin Sun ◽  
Futian Wang ◽  
Peng Xu
Keyword(s):  
Railway Track ◽  
Track Irregularities

Numerical Simulation of Railway Track Irregularities Based on Stochastic Expansion Method of Standard Orthogonal Bases

2012 ◽  
Vol 178-181 ◽  
pp. 1373-1378 ◽  
Author(s):  
Heng Bin Zheng ◽  
Quan Sheng Yan ◽  
Jun Liang Hu ◽  
Zhou Chen
Keyword(s):  
Numerical Simulation ◽  
Stochastic Process ◽  
Random Variables ◽  
Expansion Method ◽  
Simulation Method ◽  
New Method ◽  
Railway Track ◽  
Independent Random Variables ◽  
Orthogonal Bases ◽  
Track Irregularities

A new simulation method of stochastic process was accepted in the field of simulation for the track irregularities. This method adopted the Hartley orthogonal bases as the standard orthogonal bases. On the basis of the expansion method of stochastic process, and under the condition of ensuring accuracy, it could capture main probabilistic characters of a stochastic process with only a few independent random variables. Through the numerical simulation of the example, it testified the validity and effectiveness of the new method.

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