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.

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).

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 Railroad Track Deterioration Characteristics Based Track Measurement Data Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Peng Xu ◽  
Reng-Kui Liu ◽  
Feng Wang ◽  
Fu-Tian Wang ◽  
Quan-Xin Sun
Keyword(s):  
Data Mining ◽  
Measurement Data ◽  
Accurate Information ◽  
Mining Method ◽  
Railroad Track ◽  
Track Maintenance ◽  
Data Mining Method ◽  
Track Condition ◽  
Deterioration Characteristics ◽  
Rail Traffic

Accurate information on future railroad track condition is essential to optimally schedule track Maintenance & Renewal activities in order to minimize influences of the activities on rail traffic under constraints of limited budgets and maintaining allowable condition tracks. In this paper, a track measurement data mining method is presented to this aim. It is developed on the basis of track deterioration characteristics. Actual track measurement data is used to analyze errors in track condition predictions by the method. The analysis results show that the proposed method can mine accurate track deterioration rates from historical track measurement data and thus accurately provides future track condition two or three months in advance.

A Novel and Reliable Track Condition Prediction Model for Condition Based Track Maintenance

2013 ◽  
Author(s):  
Peng Xu ◽  
Rengkui Liu ◽  
Quanxin Sun ◽  
Reginald R. Souleyrette ◽  
Jerry G. Rose
Keyword(s):  
Power Systems ◽  
Prediction Model ◽  
High Speed ◽  
Management Practices ◽  
Prediction Models ◽  
Practical Knowledge ◽  
Facility Management ◽  
Railway Transportation ◽  
Track Maintenance ◽  
Track Condition

Recent railway transportation developments throughout the world have demonstrated two main trends, high speed and heavy haul. Both of these have resulted in increased wheel loads due to increased dynamic forces and/or higher weights. It is well known that increased wheel loads result in faster deterioration of the track structure. Consequently, maintenance-of-way departments inspect more frequently to ensure safety and comfort for passengers and reduce the risk of damage to freight. An alternative to more frequent inspections is a track maintenance strategy known as condition based maintenance (CBM). CBM has received considerable attention in other industries such as truck fleet management and power systems facility management. Practices in these fields show that CBM can not only reduce interruption of service but also enhance system reliability. What is more, CBM can also reduce life-cycle costs. Within railroading, CBM is used to schedule preventive rail grinding, but, CBM has not yet found widespread implementation in the maintenance of other track components. The key to effective implementation of CBM is reliable forecasts of future conditions based on prediction models. In this paper, a novel track condition prediction model is presented which may serve as a basis for condition based track maintenance. The model is built on practical knowledge of track condition deterioration. Typically, the model can predict track condition (including isolated geometry exceptions and condition of unit track sections) two to three months in advance, depending on tonnage/frequency of trains. To validate the model, track inspection data were collected from the Jinan bureau of China Railroads. Some analysis of the results of track condition predictions is also presented.

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.

A Heuristic Approach to the Railroad Track Maintenance Scheduling Problem

2011 ◽  
Vol 26 (2) ◽  
pp. 129-145 ◽  
Author(s):  
Fan Peng ◽  
Seungmo Kang ◽  
Xiaopeng Li ◽  
Yanfeng Ouyang ◽  
Kamalesh Somani ◽  
...  
Keyword(s):  
Heuristic Approach ◽  
Maintenance Scheduling ◽  
Scheduling Problem ◽  
Railroad Track ◽  
Track Maintenance

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Optimizing Tamper Efficiency Through the Integration of Inertial Based Track Geometry Measurement

2017 ◽  
Author(s):  
Joseph W. Palese ◽  
Sergio DiVentura ◽  
Ken Hill ◽  
Peter Maurice
Keyword(s):  
Time Perspective ◽  
Cloud Service ◽  
New Method ◽  
Maximum Efficiency ◽  
Track Geometry ◽  
Track Maintenance ◽  
Measurement Systems ◽  
Recorded Data ◽  
Available Technology ◽  
Geometry Measurement

Maintaining track geometry is key to the safe and efficient operations of a railroad. Failure to properly maintain geometry can lead to costly track structure failures or even more costly derailments. Currently, there exists a number of different methods for measuring track geometry and then if required, maintaining the track to return track geometry to specified levels of acceptance. Because of this need to have proper track geometry, tampers are one of the most common pieces of maintenance equipment in a railroad operation’s fleet. It is therefore paramount from both a cost and track time perspective to gain maximum efficiency from any one particular tamper. Track geometry is typically measured through a variety of contact and non-contact measurement systems which can mount on a variety of different platforms. With respect to a tamper, a push buggy projector system is typically used to measure track geometry, utilizing the tamper body as the basis for the reference system, Track geometry can be measured utilizing this technology during a prerecording run. Then, the software onboard the tamper analyzes the recorded data to determine the best fit and calculate throws that achieve a better track alignment, particularly in curves. During the tamping operation, the tamper buggy system and frame adjust the track. Due to its design, track geometry measurements can only be made at low speed (roughly 4mph) which can severely affect the efficiency of the tamper. To help decrease pre maintenance inspection times, an inertial based track geometry measurement system has been developed and integrated into the tamper’s operating software. This system can mount directly to the frame of a tamper and operate at hy-rail to very low speeds. Measurements made can be fed directly into the tamper control system to guide where and how track geometry adjustments need to be made. In addition, the capability to collect data during travel mode without the buggies extended allows for the collection of data at any time. Thus, data can be recorded when traveling back and forth to a stabling location, before and/or after grinding. This allows for synchronization of data at a later time to utilize for adjusting the track. Also, data can be collected post-work to allow for the comparison of pre and post geometry to allow for the determination of the effectiveness of a given tamping operation. Tampers equipped with this track geometry system facilitate the foundation for an enterprise solution. Data that is measured and collected can be sent to a cloud service, in real time that will provide exception reports, health status, and rail health trend analyses. Utilizing the available technology further optimizes response time in track maintenance. This paper will introduce this new method of mounting and completely integrating an inertial based track geometry system onto a tamper. In addition, studies will be presented which confirm the ability of this system to replicate the tamper’s projection based track geometry system. Finally, a comprehensive study on efficiency gains will be presented directly comparing a standard method of maintaining a segment via a tamper to this new method of using onboard inertial track geometry measurement.

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