scholarly journals A Roadmap for Sustainable Smart Track—Wireless Continuous Monitoring of Railway Track Condition

2021 ◽  
Vol 13 (13) ◽  
pp. 7456
Author(s):  
J. Riley Edwards ◽  
Kirill A. Mechitov ◽  
Ian Germoglio Barbosa ◽  
Arthur de O. Lima ◽  
Billie F. Spencer ◽  
...  
Keyword(s):  
Conceptual Design ◽  
Ieee 802.15.4 ◽  
Railway Track ◽  
Public And Private ◽  
Expert Opinions ◽  
Track Condition ◽  
Train Operation ◽  
Accident Data ◽  
Monitoring Technologies ◽  
4G Lte

Ensuring safe train operation, minimizing service interruptions, and optimizing maintenance procedures are primary railway industry focus areas. To support these goals, a multi-disciplinary team of researchers at the University of Illinois at Urbana-Champaign proposed a wireless, continuous, and accurate methodology to monitor track conditions. This project, referred to as “Smart Track”, included the development of a conceptual design plan for efficient and effective implementation of smart monitoring technologies. The project began by establishing guiding research questions, and revising those questions based on track-caused accident data obtained from the Federal Railroad Administration (FRA) and expert opinions from rail experts in the public and private sectors. Next, the research team combined these findings and developed metrics for assigning risk and priorities to various track assets and inspection needs. In parallel, the project team conducted a survey of available wireless technologies for intra-site and site-to-cloud communications. These capabilities were mapped to instrumentation types and requirements (e.g., strain gauges, accelerometers) to ensure compatibility in terms of energy consumption, bandwidth, and communications range. Results identified the rail, crosstie and support, ballast and sub-structure, bridge deck and support, and special trackwork as priority locations for instrumentation. Additionally, IEEE 802.15.4 was found to be the most appropriate cellular communication system within field sites and 4G LTE cellular was determined to be the wireless technology best suited for field site-to-cloud communication. The conceptual design presented in this paper is the first step in achieving the broader goal of Smart Track; to improve the rail industry’s ability to answer critical safety and maintenance-related questions related to the track infrastructure by monitoring and predicting track health.

Rail Track Maintenance Planning: An Assessment Model

2000 ◽  
Vol 1713 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Scott A. Simson ◽  
Luis Ferreira ◽  
Martin H. Murray
Keyword(s):  
Net Present Value ◽  
Assessment Model ◽  
Operating Costs ◽  
Railway Track ◽  
Base Case ◽  
Maintenance Planning ◽  
Track Maintenance ◽  
Track Condition ◽  
Train Operation ◽  
Maintenance Work

In Australian rail freight operations, railway track maintenance makes up between 25 and 35 percent of total train operating costs. Models have shown that track maintenance costs can be reduced by 5 to 10 percent through improved planning. The Track Maintenance Planning Model (TMPM) has been developed to deal with the track maintenance planning function in the medium to long term. In contrast to traditional models, which mainly use expert systems, TMPM simulates the impacts of railway track conditions and related maintenance work by using an existing track degradation model. Track condition data from that model are used to determine whether safety-related speed restrictions are needed and what immediate maintenance work may be required for safe train operation. TMPM outputs the net present value of the financial benefits of undertaking a given maintenance strategy compared with a base-case maintenance scenario. This approach has an advantage over current models in investigating what-if scenarios. The track engineer can assess the possible benefits of reduced operating costs from upgrading track infrastructure or from improving maintenance equipment. Track maintenance and train operating costs also can be simulated over time. The results of applying the model to a test track section using several different maintenance strategies are presented.

scholarly journals Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

2021 ◽  
Vol 11 (11) ◽  
pp. 4756
Author(s):  
Gaoran Guo ◽  
Xuhao Cui ◽  
Bowen Du
Keyword(s):  
Random Forest ◽  
High Speed ◽  
Recognition Rate ◽  
Operation Time ◽  
Deformation Monitoring ◽  
Vibration Monitoring ◽  
Railway Track ◽  
Ballastless Track ◽  
Train Operation ◽  
Track Slab

High-speed railways (HSRs) are established all over the world owing to their advantages of high speed, ride comfort, and low vibration and noise. A ballastless track slab is a crucial part of the HSR, and its working condition directly affects the safe operation of the train. With increasing train operation time, track slabs suffer from various defects such as track slab warping and arching as well as interlayer disengagement defect. These defects will eventually lead to the deformation of track slabs and thus jeopardize safe train operation. Therefore, it is important to monitor the condition of ballastless track slabs and identify their defects. This paper proposes a method for monitoring track slab deformation using fiber optic sensing technology and an intelligent method for identifying track slab deformation using the random-forest model. The results show that track-side monitoring can effectively capture the vibration signals caused by train vibration, track slab deformation, noise, and environmental vibration. The proposed intelligent algorithm can identify track slab deformation effectively, and the recognition rate can reach 96.09%. This paper provides new methods for track slab deformation monitoring and intelligent identification.

Managing Government Agency Collaboration through Social Networks

2011 ◽  
pp. 1054-1064
Author(s):  
Salvatore Parise
Keyword(s):  
Social Networks ◽  
Employee Communication ◽  
Work Processes ◽  
Public And Private ◽  
Knowledge Based ◽  
Expert Opinions ◽  
Key Stakeholders ◽  
Working Together ◽  
Market Needs ◽  
Job Levels

Public and private-based organizations are increasingly relying on collaboration—the coordination of two or more individuals, groups or companies working together to achieve a common goal or to create mutual value—to meet customer and market needs. Collaboration requires “rich” employee communication mechanisms that involve both people finding and interacting with subject-matter experts inside and outside their organization as well as people tapping into and incorporating structured information (e.g., the latest market research reports) and “unstructured knowledge” (e.g., expert opinions discussed at conferences) as part of their work projects. Today’s collaboration needs require networks of employees, often with different areas of expertise, organizational affiliations, job levels, or company tenure, to coordinate in near real-time to perform knowledge-based work. Organizations with a focus on the acquisition, interpretation, and sharing of intelligence information can benefit by understanding the barriers to collaboration and how fostering social networks among employees and key stakeholders results in more effective collaboration. This article provides an illustrated example involving a government intelligence agency of how social network analysis can be used to understand social networks. A framework composed of three components, collaborative IT tools, talent management and networked work processes, to enable and apply social networks is also introduced.

scholarly journals Railway Track Condition Assessment at Network Level by Frequency Domain Analysis of GPR Data

2018 ◽  
Vol 10 (4) ◽  
pp. 559 ◽  
Author(s):  
Simona Fontul ◽  
André Paixão ◽  
Mercedes Solla ◽  
Lara Pajewski
Keyword(s):  
Frequency Domain ◽  
Domain Analysis ◽  
Condition Assessment ◽  
Frequency Domain Analysis ◽  
Railway Track ◽  
Track Condition

Railway track condition indicators from ground penetrating radar

2003 ◽  
Vol 36 (3) ◽  
pp. 157-167 ◽  
Author(s):  
Theodore R. Sussmann ◽  
Ernest T. Selig ◽  
James P. Hyslip
Keyword(s):  
Ground Penetrating Radar ◽  
Railway Track ◽  
Condition Indicators ◽  
Track Condition ◽  
Ground Penetrating

scholarly journals An adaptive opportunistic maintenance model based on railway track condition prediction

2016 ◽  
Vol 49 (28) ◽  
pp. 120-125 ◽  
Author(s):  
Christophe Letot ◽  
Iman Soleimanmeigouni ◽  
Alireza Ahmadi ◽  
Pierre Dehombreux
Keyword(s):  
Railway Track ◽  
Opportunistic Maintenance ◽  
Model Based ◽  
Track Condition ◽  
Maintenance Model

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 Investigation of Dynamic Factor, Rail Stress and Track Foundation Modulus of Ballasted Railway Track for Different Bed Conditions Using Numerical Methods

2021 ◽  
Vol 1200 (1) ◽  
pp. 012018
Author(s):  
Shanmugasekar Thenappan
Keyword(s):  
Fatigue Strength ◽  
Numerical Methods ◽  
High Speed ◽  
Test Method ◽  
Railway Track ◽  
Design Parameters ◽  
Dynamic Factor ◽  
Bending Stress ◽  
Track Condition ◽  
Track Stiffness

Abstract The track stiffness is the primary function of roadbeds thickness and subgrade characteristics. For this purpose, numerical scale track finite element technique representing the ballasted track with multi layered substructure founded on subgrade was simulated. The track deflection, stress was abstracted in static and dynamic conditions. The track significant design parameters: Foundation modulus, rail fatigue strength, rail bending stress and stress on subgrade levels were evaluated by using improved current track design numerical methods and compared against field test results which were carried out on part of MG Double track high speed main line (1600 km). Mathematical equations were developed to correlate the variables; ballast thickness, settlement, track stiffness, rail bending stress and rail fatigue strength on varying subgrade soil modulus. Incorporation of this parametric study will improve and optimise the conventional track design and maintenance standard. A simple improved track design was introduced by using single track stiffness parameter from conventional plate bearing test (PBT) on Force Displacement (FD) conventional curve method. The improved method with deriving equivalent track stiffness from rail pad and track substructure tested C value are accurate and simple. The current test method to determine the track stiffness in live track condition is expensive and unsafe with operational requirements. This PBT is simple, cost saving on labour, safe and without applying live train load.

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