Simultaneous schedule of trains and track maintenance according to stochastic blockage time

Author(s):  
Mostafa Bababeik ◽  
Mohammad Farjadamin ◽  
Navid Khademi ◽  
Amir-Hossein Fani
Keyword(s):  
Author(s):  
Mahdieh Sedghi ◽  
Osmo Kauppila ◽  
Bjarne Bergquist ◽  
Erik Vanhatalo ◽  
Murat Kulahci

Author(s):  
Zai-Wei Li ◽  
Xiao-Zhou Liu ◽  
Hong-Yao Lu ◽  
Yue-Lei He

The deformation of longitudinally coupled prefabricated slab track (LCPST) due to high temperature may lead to a reduction in ride comfort and safety in high-speed rail (HSR) operation. It is thus critical to understand and track the development of such defects. This study develops an online monitoring system to analyze LCPST deformation at different slab depths under various temperatures. The trackside system, powered by solar energy with STM8L core that is ultra-low in energy consumption, is used to collect data of LCPST deformation and temperature level uninterruptedly. With canonical correlation analysis, it is found that LCPST deformation presents similar periodic variation to yearly temperature fluctuation and large longitudinal force may be generated as heat accumulates in summer, thereby causing track defects. Then the distribution of temperature and deformation data is categorized based on fuzzy c-means clustering. Through the distribution analysis, it is suggested that slab inspection can be shortened to 6 hours, i.e. from 10:00 am to 4:00 pm, reducing 14.3% track inspection workload from the current practice. The price of workload reduction is only a 2% chance of missed detection of slab deformation. The finding of this research can be used to enhance LCPST monitoring efficiency and reduce interruption to HSR operation, which is an essential step in promoting reliable and cost-effective track service.


2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Xu ◽  
Chuanjun Jia ◽  
Ye Li ◽  
Quanxin Sun ◽  
Rengkui Liu

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.


2021 ◽  
Vol 11 (3) ◽  
pp. 1071
Author(s):  
Davide Bombarda ◽  
Giorgio Matteo Vitetta ◽  
Giovanni Ferrante

Rail tracks undergo massive stresses that can affect their structural integrity and produce rail breakage. The last phenomenon represents a serious concern for railway management authorities, since it may cause derailments and, consequently, losses of rolling stock material and lives. Therefore, the activities of track maintenance and inspection are of paramount importance. In recent years, the use of various technologies for monitoring rails and the detection of their defects has been investigated; however, despite the important progresses in this field, substantial research efforts are still required to achieve higher scanning speeds and improve the reliability of diagnostic procedures. It is expected that, in the near future, an important role in track maintenance and inspection will be played by the ultrasonic guided wave technology. In this manuscript, its use in rail track monitoring is investigated in detail; moreover, both of the main strategies investigated in the technical literature are taken into consideration. The first strategy consists of the installation of the monitoring instrumentation on board a moving test vehicle that scans the track below while running. The second strategy, instead, is based on distributing the instrumentation throughout the entire rail network, so that continuous monitoring in quasi-real-time can be obtained. In our analysis of the proposed solutions, the prototypes and the employed methods are described.


Author(s):  
Joseph W. Palese ◽  
Sergio DiVentura ◽  
Ken Hill ◽  
Peter Maurice

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.


1986 ◽  
Vol 80 (1) ◽  
pp. 307-310
Author(s):  
DH NUTBROWN ◽  
P COYSTEN ◽  
AIB PEASE

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