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).

scholarly journals A Novel Short-Range Prediction Model for Railway Track Irregularity

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Xu ◽  
Rengkui Liu ◽  
Quanxin Sun ◽  
Futian Wang
Keyword(s):  
Prediction Model ◽  
Short Range ◽  
Travel Speed ◽  
Railway Track ◽  
Just In Time ◽  
Railway Network ◽  
Track Geometry ◽  
Short Period ◽  
Track Irregularity ◽  
Transport Volume

In recent years, with axle loads, train loads, transport volume, and travel speed constantly increasing and railway network steadily lengthening, shortcomings of current maintenance strategies are getting to be noticed from an economical and safety perspective. To overcome the shortcomings, permanent-of-way departments throughout the world have given a considerable attention to an ideal maintenance strategy which is to carry out appropriate maintenances just in time on track locations really requiring maintenance. This strategy is simplified as the condition-based maintenance (CBM) which has attracted attentions of engineers of many industries in the recent 70 years. To implement CBM for track irregularity, there are many issues which need to be addressed. One of them focuses on predicting track irregularity of each day in a future short period. In this paper, based on track irregularity evolution characteristics, a Short-Range Prediction Model was developed to this aim and is abbreviated to TI-SRPM. Performance analysis results for TI-SRPM illustrate that track irregularity amplitude predictions on sampling points by TI-SRPM are very close to their measurements by Track Geometry Car.

scholarly journals Developing an Enhanced Short-Range Railroad Track Condition Prediction Model for Optimal Maintenance Scheduling

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Xu ◽  
Chuanjun Jia ◽  
Ye Li ◽  
Quanxin Sun ◽  
Rengkui Liu
Keyword(s):  
Prediction Model ◽  
Short Range ◽  
Prediction Method ◽  
Maintenance Scheduling ◽  
Railroad Track ◽  
Track Geometry ◽  
Track Maintenance ◽  
Track Condition ◽  
Optimal Maintenance ◽  
Process Work

As railroad infrastructure becomes older and older and rail transportation is developing towards higher speed and heavier axle, the risk to safe rail transport and the expenses for railroad maintenance are increasing. The railroad infrastructure deterioration (prediction) model is vital to reducing the risk and the expenses. A short-range track condition prediction method was developed in our previous research on railroad track deterioration analysis. It is intended to provide track maintenance managers with two or three months of track condition in advance to schedule track maintenance activities more smartly. Recent comparison analyses on track geometrical exceptions calculated from track condition measured with track geometry cars and those predicted by the method showed that the method fails to provide reliable condition for some analysis sections. This paper presented the enhancement to the method. One year of track geometry data for the Jiulong-Beijing railroad from track geometry cars was used to conduct error analyses and comparison analyses. Analysis results imply that the enhanced model is robust to make reliable predictions. Our in-process work on applying those predicted conditions for optimal track maintenance scheduling is discussed in brief as well.

Track geometry degradation and maintenance modelling: A review

2016 ◽  
Vol 232 (1) ◽  
pp. 73-102 ◽  
Author(s):  
Iman Soleimanmeigouni ◽  
Alireza Ahmadi ◽  
Uday Kumar
Keyword(s):  
Maintenance Scheduling ◽  
Railway Track ◽  
Railway Transportation ◽  
Degradation Behaviour ◽  
Maintenance Strategies ◽  
Track Geometry ◽  
Track Maintenance ◽  
Railway Infrastructure ◽  
Recovery Models ◽  
Scheduling Models

Increased demand for railway transportation is creating a need for higher train speeds and axle loads. These, in turn, increase the likelihood of track degradation and failures. Modelling the degradation behaviour of track geometry and development of applicable and effective maintenance strategies has become a challenging concern for railway infrastructure managers. During the last three decades, a number of track geometry degradation and maintenance modelling approaches have been developed to predict and improve the railway track geometry condition. In this paper, existing track geometry measures are identified and discussed. Available models for track geometry degradation are reviewed and classified. Tamping recovery models are also reviewed and discussed to identify the issues and challenges of different available methodologies and models. Existing track geometry maintenance models are reviewed and critical observations on each contribution are provided. The most important track maintenance scheduling models are identified and discussed. Finally, the paper provides directions for further research.

scholarly journals Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wei Lili ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Time Series Data ◽  
Arma Model ◽  
Railway Track ◽  
Series Data ◽  
Railway Transportation ◽  
Track Geometry ◽  
Track Irregularity ◽  
Railway Passenger

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.

scholarly journals GELEŽINKELIO KELIO GEOMETRINIŲ PARAMETRŲ DINAMIKOS TYRIMAS TAIKANT KELIO KOKYBĖS INDEKSĄ / RAILWAYS GEOMETRICAL PARAMETERS DYNAMICS INVESTIGATION BY TRACK QUALITY INDEX

2018 ◽  
Vol 10 (0) ◽  
pp. 1-7
Author(s):  
Vytautas Motiejus Bubnelis ◽  
Benas Slepakovas ◽  
Laura Černiauskaitė ◽  
Henrikas Sivilevičius
Keyword(s):  
Quality Index ◽  
Rail Transport ◽  
Geometric Parameters ◽  
Railway Track ◽  
Geometrical Parameters ◽  
Track Geometry ◽  
Track Condition ◽  
Over Time ◽  
Track Quality

Rail transport, in competition with other modes of transport, has to improve the quality of passenger and freight transport. In order to carry passengers and goods quickly, efficiently and safely, it is necessary take maintenance railways so that their geometric parameters do not exceed the tolerances. About real railway track condition, the data is obtained by track geometry recording car, measuring seven geometric parameters dispersion. This paper presents the methodology for determining and estimating the geometric parameters of the track geometry, which shows that the track quality index (TQI) is the sum of the variance of seven geometric parameters. Experimental research on the two-track A (8km) and B (11km) a three-year period (2015-2017) for all 12-month KKI, establish their quality dynamics (change over time). These data indicate that the quality of the analyzed sections A and B was good, but due to the increasing mass (in megatons) of transported loads, there is a tendency to deteriorate. Santrauka Geležinkelių transportas, konkuruodamas su kitomis transporto rūšimis, privalo gerinti keleivių ir krovinių vežimų kokybę. Norint greitai, efektyviai ir saugiai vežti keleivius ir krovinius, būtina taip prižiūrėti geležinkelių kelius, kad jų geometrinių parametrų nuokrypiai neviršytų leidžiamųjų nuokrypių. Apie tikrąją geležinkelių kelio būklę duomenys gaunami kelmačiu išmatavus septynių geometrinių parametrų sklaidą. Šiame darbe pateikta geležinkelio kelio geometrinių parametrų sklaidos nustatymo ir vertinimo metodika, kurioje įrodyta, kad kelio kokybės indeksas (KKI) yra septynių geometrinių parametrų dispersijų suma. Eksperimentiškai ištyrus dviejų geležinkelio kelių A (8 km) ir B (11 km) trijų metų laikotarpiu (nuo 2015 iki 2017 metų) visų 12 mėnesių KKI, nustatyta jų kokybės dinamika (kaita bėgant laikui). Šie duomenys rodo, kad ištyrinėtų A ir B ruožų kelio kokybė buvo gera, bet dėl didėjančios pervežtų krovinių suminės masės (megatonų skaičiaus) turi tendenciją blogėti.

scholarly journals Analyzing Major Track Quality Indices and Introducing a Universally Applicable TQI

2020 ◽  
Vol 10 (23) ◽  
pp. 8490
Author(s):  
Stefan Offenbacher ◽  
Johannes Neuhold ◽  
Peter Veit ◽  
Matthias Landgraf
Keyword(s):  
Measurement Data ◽  
Railway Track ◽  
Quality Indices ◽  
Combine Measurement ◽  
Track Geometry ◽  
University Of Technology ◽  
Track Condition ◽  
Railway Operation ◽  
Selection Of ◽  
Track Quality

Adequate railway track condition is a prerequisite for safe and reliable railway operation. Many track quality indices (TQIs) have been developed with the aim of assessing the track condition holistically. These indices combine measurement signals of some or all relevant geometry parameters with different mathematical models. In this paper, a selection of important TQIs is evaluated. Using measurement data of a five kilometer track section, the indices are calculated and their properties are discussed. This study reveals that all indices exhibit drawbacks to varying degrees. As a consequence, a new index has been developed—the track quality index of Graz University of Technology (TUG_TQI). Its favorable characteristics are presented by means of the above-mentioned test section. The TUG_TQI combines all relevant track geometry parameters, which are normalized beforehand to eliminate over or underrepresentation of different parameters. Thus, the index reliably describes the overall geometrical track quality.

Application of data mining techniques for the investigation of track geometry and stiffness variation

2019 ◽  
Vol 234 (5) ◽  
pp. 439-453 ◽  
Author(s):  
Mohammad Mehrali ◽  
Morteza Esmaeili ◽  
Saeed Mohammadzadeh
Keyword(s):  
Data Mining ◽  
Railway Track ◽  
Quality Indices ◽  
Dominant Wavelength ◽  
Data Mining Techniques ◽  
Track Geometry ◽  
Railway Tracks ◽  
Track Condition ◽  
Track Stiffness ◽  
Track Quality

Railway tracks are one of the most important national assets of many countries. The major part of the annual budget of railway companies concerns repairing, improving, and maintaining railway tracks, which is a challenge for railway managers. The logical method of repair and maintenance should take into account all the economic and technical aspects of the problem and proper management of track maintenance—without knowing the factors and parameters responsible for the track failure—quality control methods, and finally, the choice of the appropriate repair methods. Railway track geometry is the main factor that identifies the track behavior and condition. It is based on measuring the geometric parameters of the track determined by the track quality indices. The existing track quality indices mostly represent the geometrical condition of the railway track superstructure. In the past years, the effects of track bed stiffness on the track condition have been investigated. This paper investigates the railway track condition based on the railway track geometry parameters as well as the vertical track stiffness. A method for continuous measurement of track stiffness along a railway line is described and demonstrated. By measuring the track geometry parameters and stiffness, the superstructure and the substructure condition of the railway track are assessed. In addition, the relation between these data is investigated by using data mining techniques such as classification, decision tree, clustering, and dominant wavelength filtering. It is shown that filtering the data based on the dominant wavelength provides the best correlation between the track geometry in the vertical direction and stiffness.

scholarly journals The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection

2021 ◽  
Vol 11 (11) ◽  
pp. 5244
Author(s):  
Xinchun Zhang ◽  
Ximin Cui ◽  
Bo Huang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Speed ◽  
Measurement Accuracy ◽  
Railway Track ◽  
Filter Method ◽  
Integrated Navigation ◽  
High Speed Railway ◽  
Track Geometry ◽  
Integrated Navigation System

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.

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