International Conference on Railway Engineering (ICRE 2016)
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- Location: Brussels, Belgium
- Conference date: 12-13 May 2016
- ISBN: 978-1-78561-292-3
- Conference number: CP703
- The following topics are dealt with: safety; infrastructure; ICT; optimisation and railway condition monitoring.
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- Author(s): P. Rowlands
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In a sector growing at an unprecedented rate and historically lagging behind in its adoption of technology, how is Atkins addressing today's challenges by embracing tomorrow's technologies? Atkins Transportation division is developing a number of software tools to assist the design of railway electrification overhead line equipment. The interlinked suite of programmes, known collectively as TADPOLE (Tools Aiding the Design and Production of Overhead Line Equipment), uses automation and common data sources to meet current technical and programme demands in the UK marketplace. Additionally, Atkins was joint lead partner with Laing O'Rourke in the Digitally Enabling Electrification (DEE) Research Project, along with dhp11 and Imperial College London. The project was funded by Innovate UK to research applying Digital Engineering in the Railway Industry. The two year project, which is part of the Government innovation strategy for digitising the railway, focused on railway electrification. The team has developed approaches that increase the efficiency and validity of data through all stages of the asset lifecycle. By exploiting digital technologies the project team has demonstrated how the industry can achieve and benefit from an integrated BIM compliant environment through common methods of exchanging and structuring data throughout the lifecycle of an OLE asset. - Author(s): Q. Fu and J.M. Easton
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Weather impacts on rail performance is complex. In this paper, we present an empirical data analysis of train delays, which were particularly attributed to wind, within the Anglia Route of Great Britain's railway network. This investigation involves a variety of data sources that are relevant to those railway incidents, including their detailed records, historic weather and trackside vegetation conditions and so on. Our work briefly explores how those data could be exploited in order to gain a deeper insight into the occurrence of the incidents, given the correlations between various factors, such as wind speed and tree density surrounding the incident sites. The survey findings will serve to underlie further analyses by using more advanced statistical approaches, for which specific recommendations are also made. - Author(s): S. Ahamed
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There has been an ever-increasing demands on the rail transport network lately, with a vast majority of customers having access to mobile communication and the internet. As such, there is a need for a transport network which is more intelligent and provides seamless connectivity to meet the demand. Visible Light Communication (VLC) - A technology which has matured recently, has the potential to provide a wide range of benefits to the railway industry. The paper outlines the basic components in this system, review the state of the art and discuss some of the challenges & possibilities and the broad spectrum of potential applications in railway environment this new wireless technique opens up.
Digital electrification design
How does existing data improve decision making? A case study of wind-related incidents on rail network
Visible Light Communication in Railways
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- Author(s): S. Kimkeran and G. Buckett
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Aurizon Network has embarked on a project to produce a comprehensive Network Asset Management System (NAMS). The process has included compiling a single database from numerous legacy systems, then validating the assets comprising each of the corridors using the survey tools and software developed by Omnicom Engineering Limited. The paper will describe the methodology adopted to compile a database of assets that form the single source of truth which is a fundamental component of the SAP based system. OmniSurveyor3D is an award winning system that has been used since 1998 to enable infrastructure owners to map, manage and maintain large complex transport networks such as Road and Rail. As well as providing accurate geospatial and linear asset data, the end user software has a comprehensive set of tools that allows customers to extract additional benefits from the data. Data from the system can be exported in an open industry format that can be integrated in third party systems. The integration of very high resolution laser data enables accurate measurements ±1mm to be taken as well as automatic clearance assessment. - Author(s): R.G. Nyberg
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The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.
Investing in productive, efficient infrastructure: out with the old, in with the new
A machine learning approach for recognising woody plants on railway trackbeds
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- Author(s): H. Douglas ; C. Roberts ; S. Hillmansen
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As it expands, the rail industry is concerned not only with maintaining service quality and managing increased demand, but with improving energy efficiency. Whilst it is important that future passenger infrastructures, rolling stock and operations are designed sustainably, existing systems need to reduce their environmental impacts to meet international efficiency targets. A number of energy-saving solutions have been developed to achieve this, often showing excellent results in simulation and for selected case studies. However, operators face a difficult decision when choosing solutions to implement, as these results give limited information on transferability. It may be obvious that a particular solution is more suited for high speed rail than a metro, given the differences between these systems. Yet it could just as easily be the case that a solution which works for one metro is not effective for another, due to certain factors. Through the application of a previously developed method, these factors are investigated for different metro systems. The impact of route, vehicle and service characteristics on the application of energy saving solutions is evaluated, allowing the characteristics with greatest influence to be determined. From this information, recommendations are made as to which solutions are most appropriate for the different systems investigated. - Author(s): C. Pislaru ; A. Abobghala ; Y. Munir
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This paper presents the analysis of two control methods used to increase the kinematic performance of two-axle bogie when the rail vehicle travels on curved track with constant speed. A holistic model for the bogie and wheelsets is developed using the multibody system approach. The mathematical model is implemented in MATLAB environment using Simulink toolbox. Then the bogie model is included in a closed loop and PID controller tuning is performed using Control Systems toolbox from MATLAB environment. The simulated values for lateral and yaw angular displacement of wheelsets are compared with those obtained by applying the Model Reference Adaptive Control (MRAC) method. The results of the sensitivity analysis for rise time, peak overshoot and settling time with respect to travel speed for rail vehicle show that MRAC method improves the time domain closed-loop performance. The proposed MRAC method applied to the bogie system within the rail vehicle multibody model represents an important contribution to the development of rail transportation systems where rolling stock is equipped with motion control systems with superior efficiency and reduced energy consumption. - Author(s): M. Blumenfeld ; F. Schmid ; C. Roberts
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The emergence of the megalopolis has created social, economic and environmental burdens that result in significant challenges for the sustainability of urban systems. More specifically, transport in growing megalopoleis has become an important issue, known to contribute significant social and environmental impacts in urban areas. However, traditional scientific approaches do not appear to cope with the complexity of these systems or to alter the trajectory of current trends towards desirable outcomes. Considering the increasing urgency for a more sustainable future, we contend that a holistic view should be combined with a normative problem-solving approach. Based on this, we propose a novel methodology that combines the back-casting method with a systems engineering approach in order to develop a design process to generate innovative and robust solutions so as to address the uncertainty of future scenarios. We apply this design process to the task of resolving the systemic paradox of coverage, speed and capacity in metro systems. The case study leads to a solution that increases average door-to-door speeds by up to 55% and capacity by 30% when compared to conventional operations and thus meets the sustainability goals of megalopoleis.
Optimising energy saving in metro systems through characteristic evaluation
Increasing the Kinematic Performance of the Rail Vehicles by Using Adaptive Control Methods for Steering Bogies
A systems approach to developing future urban systems: creating a metro that fits the megalopolis
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- Author(s): S. Newcombe and G. Tucker
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`Train monitoring train' and `infrastructure monitoring infrastructure' Remote Condition Monitoring (RCM) systems are well established in the rail industry and have demonstrated significant benefits. However, it can be more efficient to use cross-industry systems i.e. to monitor some aspects of train condition from the line-side and some aspects of the condition of the infrastructure from passing trains. There has not been a wide uptake of these systems and previous RSSB research suggests that this is due to difficulties in setting up the technical, commercial and data sharing arrangements between different industry partners. RSSB project T1010 has addressed these issues by creating a template contract that can be used when negotiating the commercial arrangements for the introduction and continued operation of cross-industry RCM systems. This is supported by a business case tool that helps to identify the most efficient use of RCM and the associated benefits for each party. A draft data-sharing architecture has also been developed. These tools should make it easier to introduce new cross-industry RCM systems. - Author(s): B. Sambo ; A. Bevan ; C. Pislaru
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The paper presents the development of an intelligent image processing algorithm capable of detecting fatigue defects from images of the rail surface. The links between the defect detection algorithm and 3D models for rail crack propagation are investigated, considering the influence of input parameters (materials, vehicle characteristics, loading conditions). The dynamic behaviour at the wheel-rail interface resulting in contact forces responsible for stressing and straining the rail material are imported from vehicle dynamics simulations. The integration of the simulated results from vehicle dynamics, contact and fracture mechanics models offer more reliable estimation of the stress intensity factors (SIF). Also the sensitivity analysis related to materials, vehicle characteristics, and loading conditions will provide further understanding of the factors that influence crack propagation in rails such as shear stresses, hydraulic pressure, fluid entrapment and squeeze film effect. This novel application of image processing for the detection of rail surface rolling contact fatigue (RCF) damage and automatic incorporation in a crack growth model represents an important contribution to the development of modern techniques for non-destructive rail inspection. This will result in improved planning/scheduling of future rail maintenance (e.g. rail grinding, renewal), less disruptions and reduced track maintenance costs in rail industry.
Enabling greater use of cross-industry remote condition monitoring
A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model
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- Author(s): H.J. Parkinson ; G. Bamford ; B. Kandola
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The focus of this paper is on the challenges encountered in safety assessments around the use of multiple data sources in the form of Big Data Analytics. The Grayrigg rail accident is analysed to investigate the type of analytic algorithms that would be needed to extract enhanced safety knowledge out of the available data sources. The current state of play in machine learning is explored and an overview of all the available analytical approaches is given, ways forward are presented and assumptions discussed to determine the attributes and requirements for combinations of data. This was done in order to assess their applicability to the proposed ELBowTie approach. The overall objective is to use this information to develop a prototype machine learning algorithm with a view to training the algorithm to recognise heightened risk situations on the rail infrastructure. - Author(s): S. Ghosh ; P. Dasgupta ; C. Mandal ; A. Katiyar
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The safe functioning of a train control system is critically dependent on the validity of the movement authorities issued to a train by the track-side radio control blocks (RBC). In order to guarantee that the movement authorities are safe, the RBC needs to consult the interlocking logic. The RBC and the interlocking logic has to be configured with data specific to the yard of operation. This paper aims to validate the data using formal methods. Specifically we present an approach for proving that the trains maintain safe distance between them when they follow the movement authorities issued by the RBC in a yard. We use UPPAAL [13] for modelling the train control system system for a small yard including the continuous dynamics of trains. The UPPAAL model-checker is used to verify the safety of all train movements permitted by the model. We demonstrate that errors in the logic can be detected by the proposed method in feasible time. As opposed to previous approaches using theorem proving techniques, our method lends itself easily to automation, which is thereby more acceptable to railway engineers.
The Development of an Enhanced Bowtie Railway Safety Assessment Tool using a Big Data Analytics Approach
Formal verification of movement authorities in automatic train control systems