Digital Rail are responsible for producing Safety Cases for the Manchester Metrolink Tram Management System (TMS). With almost 30 sites to cover, we opted to use Microsoft Office tools to streamline the production of the site Safety Cases – each of which has an identical document structure based on EN50129,
The tool was developed in Excel + Word + VBA. We created a template in Word which uses tokens that are replaced by values from an Excel spreadsheet. The tool also integrated with the client Document Management System and DOORS.
The tool also provides management facilities to track progress for document production for each site. We estimate that this approach has saved 20% on project costs and we are currently investigating further uses of this approach on a wide range of projects.
Digital Rail have recently completed a project working with a group of Business and Marketing students from Lancaster University’s Management School.
Working closely with the students over a 6 month period, their project specification was to draw up a plan on how to bring one of Digital Rail’s products to market. The product was a workflow engine tool for analysis, certification and test that we are developing in collaboration with the Austrian Institute of Technology called WeFact.
The students carried out detailed research which included interviewin industry-experts and sending out surveys. At the end of the project the students delivered a Presentation and produced an in-depth Report detailing how Digital Rail can market the product, as well outlining challenges to market, potential product development, and a suggested product business plan.
“Working with Digital Rail on the development of its joint project with the AIT has been an incredibly valuable and enjoyable experience. Howard is an enthusiastic, charismatic, and approachable professional who greatly values the inputs of others around him and gave us real responsibility and opportunities from the very start. We would like to sincerely thank Howard for this incredible opportunity and wish him the very best of success with the project in the future.”
We thoroughly enjoyed working with the students. The project helped us gain an insight into how to bring a niche product such as WeFact to market, and gave the students first-hand experience of developing their skills and expertise working with a real business on a challenging brief.
“It has been a pleasure to work with Howard and Digital Rail. This experience has been an eye opener into a fast growing, important industry that has provided a great opportunity to develop and enhance our skills. We hope that the WEFACT tool finds success in the future and wish the team well.”
Transport is one of the fastest growing contributors to climate change. Rail travel produces lower carbon emissions than travelling by car and offers an efficient alternative for those looking to reduce their carbon footprint. This project will work to understand and increase the efficiencies of railway networks to improve this even further.
This project will utilise Artificial Intelligence (AI), data analysis and modelling techniques to identify and predict problem areas of the UK rail network. These are areas which do not have the resilience and capacity to withstand common problems which lead to train service delays. The associated economic and environmental cost of this is huge, with the need for additional rail services, replacement buses and network maintenance. This project will identify indicators of concern and build a prediction model. This will allow Digital Rail Ltd to work with rail networks to increase efficiencies and direct resources to the most appropriate areas.
This project would suit a graduate with a background in engineering or computer science who is capable of coding and developing advanced APIs.
This Masters by Research is a collaborative research project between Lancaster University, with supervision by Dr Paul Rayson, and Digital Rail Ltd.
Digital Rail is a business that develops products for railway safety and resilience using advanced technologies in machine learning. It owns intellectual property and patents on several products such as Machine Vision and Big Data analytics. Digital Rail Ltd also provides services and training to railway clients including Independent Safety Assessment (ISA).
To apply for this opportunity please email graduate-applications@cgeinnovation.org with:
This project is part funded by the European Regional development Fund and is subject to confirmation of funding. For further information about the Centre for Global Eco-Innovation, please see their website.
Midnight Tuesday 19th June 2018
Start: October 2018
Digital Rail partnered with CRITICAL Software to conduct the independence safety assessment of the vehicles, track system and interfaces of the Shanghai Metro Line 8 Phase III Automated People Mover.
These vehicles will provide safe transportation in airports and dense urban areas all around the world. They are fully automated, able to cope with the bands of transporting millions of passengers each year.
Our assessment covered project plans, specifications, design tests and supplied products, as well as installation, commissioning and operations of the vehicles, the infrastructure and interfaces communication with other systems. Our work helped to achieve high safety levels in line with international standards.
Digital Rail have 3 main groups for research, development and deployment: Data Fusion Group, Autonomous Vision Group and Intelligent Systems Engineering Group.
These research groups are generating world class research and platforms for data analytics. We have various licenses and patent applications which we are always looking to discuss and expand.
Big Data Analytics on railway data for safety and performance optimisation. This will use mostly open source analytic tools.
The idea is to develop a platform for data fusion which is user-friendly, web-based, fully supported with annual licenses available. Applications will initially include rail vehicle diesels engine monitoring, driver behaviour monitoring and accident data.
Clients currently include Siemens and TfGM.
Team: Dr Peter Garraghan Lancaster University, Dr Howard Parkinson DRL, Dr Rhian Davies DRL, Damian Borowiec DRL, Dr Gary Bamford Advisory, James Bamford Advisory. Eddy Crompton DRL.
This group will primarily research, develop and market the Platform Train Interface (PTI) autonomous vision system. The AVG business plan will explain how this will happen. This will require further significant investment and patent costs. We have prototype at TRL 3 and a patent application that is successfully registered.
Team: Prof Angelov Lancaster University, Dr Howard Parkinson DRL, Dr Rhian Davies DRL, Dr Chris Johnson MMU, Marie Nolan & Nick King Lancaster University, Dr Gary Bamford Advisory.
The task is to develop a platform for systems engineering including requirements process and models. This will be based around the WEFACT workflow management from the Austrian Institute of Technology (AIT). This is being undertaken in collaboration with the AIT.
The platform will also include optional plug-in Natural Language Processing (NLP) applications that bring intelligence and machine learning into systems engineering. The platform will be suitable for railway, nuclear, automotive and aerospace applications. The ISEG business plan explains how this will happen.
Team: Dr Paul Rayson Lancaster University, Clive Osman DRL, Eddy Crompton DRL, Dr Howard Parkinson DRL, Willibald Kern AIT, Dr Christoph Schnitter AIT, Dr Gary Bamford Advisory, New MSc by RES Person DRL.
Digital Rail have worked with Lancaster University on a project under which our intelligent computer vision system is optimising safety and vehicle dwell times. The project is based upon patented technology developed at Lancaster University. Years of research by Professor Plamen Angelov and Dr Gruff Morris have allowed the intelligent vision system to be computationally efficient to reduce data dimensionality in detecting both static and moving objects by autonomously performing platform train interface (PTI) monitoring after being taught what both a good and errant platform situation looks like.
The Vision system:
Passengers (customers)
Drivers
Train and Station Operators
Between 16-19th April 2018 Digital Rail will be presenting a Poster on ‘Opportunities for resilient rail system development using natural language processing’ at the 7th European Transport Research Arena Conference in Vienna. The Transport Research Arena 2018 will include a wide spectrum of research and innovation activities spanning basic research to application-oriented engineering, social, technical and economic aspects as well as policies and standards. The Conference covers all modes of transport: rail, road, waterborne, aviation, and cross-modal.
You can read our paper which examines a natural language and machine learning approach to assessing railway hazard logs here.
Find out more about the full Conference programme on their website: www.traconference.eu
In September 2018 we will be running a Special Session on ‘Using Big data to Increase Railway Resilience’ at the Fourth International Conference on Railway Technology: Research, Development and Maintenance in Barcelona, Incorporating: The Eighth International Symposium on Speed-up and Sustainable Technology for Railway and Maglev Systems.
The conference will be exploring themes ranging from Infrastructure, Strategies and Economics, Planning and Operations, and Signalling and Communication, amongst others.
Digital Rail are working on a project that seeks to use advanced Natural Language Processing (NLP) to identify non-compliance against railway standards during the development of railway system. Non-compliance will cause a lack of resilience which will be linked to likely train service delays and the associated carbon toll. The toll is caused by having to lay on additional rail services, buses, maintenance, and so on. The carbon toll of a system will therefore be known during the design phase which will be major step forward in understanding how clean our railways are likely to be.
It has been shown that modern electric railway transportation is environmentally friendly when compared to normal occupancy car transportation and carbon usage in the railway is mainly in operations. For a given concept of operation, the environmental impact of the system and the resilience are ”baked-in” in the downward part of the V lifecycle as shown in the Figure 1 below.
Figure 1. Development V Lifecycle
This part of the V Lifecycle includes requirements management and risk assessment (for operations) that we will monitor via a machine learning approach using NLP. The baseline is compliance with the standard in terms of resilience and any non-compliances will be identified by the NLP tool acting as a watchdog and identifying low quality activities. Risk assessment will identify events caused by lack of resilience (mainly safety and reliability) in operations and establish a relationship that will be mapped to carbon via a consequence model as depicted in Figure 2.
Figure 2. Linkage between Resilience and Environmental Impact
NLP is a technique that is not widely used in the rail industry and there is an opportunity to apply it to the critical systems engineering areas such as requirements, hazard log development and management. NLP is being applied more widely in other domains such as social media and advertising.
The environmental impact that is caused by lack of resilience includes: laying on additional train services, bussing, maintenance etc. This does not included the wider consequence to the public of lost working hours. Environmental Life Cycle Analysis (ELCA) during other phases is not in our scope.
The output of the project will be prototype NLP application programming interface (API) tools that can be plugged into a lifecycle management tool as depicted in Figure 3 below. This will enable resilience to be enhanced and the carbon impact to be understood more fully during railway operations.
Figure 3. Intelligent High Resilience Low Carbon System Development Model
It has been shown that modern electric railway transportation is environmentally friendly when compared to normal occupancy car transportation. It has also been shown that the most of the environmental impact and carbon usage of a rail system is during operations [2]. Our tools will track standard compliance that ensures the resilience of the rail system. and understand how any lack of resilience will impact carbon expenditure by linking the two with an environmental model based upon service outages.
The environmental impact that is caused by lack of resilience includes: laying on additional train services, bussing, extra maintenance activities include car, van, plant usage, having to have redundant assets, passengers using other means, and the eventual decline of rail. This does not included the wider consequence to the public of lost working hours.
When train and tram wheels brake unevenly the result is often wheel flats. Resulting wheel slides cost the industry millions of pounds in engineering costs and delays each year.
UK Railway Group Standard GM/RT2466 requires that wheel flats larger than 60mm on vehicles operating at speeds up to and including 200km/h (125mph) have to be returned to depots immediately – at greatly reduced speeds. For 40-60mm flats, a vehicle has to be returned to the depot within 24 hours of discovery of the fault.
Meanwhile, in the UK it is estimated that the issue of slippery rails and reduced adhesion caused by falling autumn leaves cost the industry GBP60m (EUR67.5m) a year.
Wheel slides are directly impacted by a multitude of events, including service performance, weather conditions, time of year, vehicle condition, track quality, track cleaning approaches, leaf-fall data, level crossing proximity, driving policies and more. The issue has also resulted in several serious rail safety incidents in recent years.
A Typical Wheel Flat and Sanding
Although it is recognised that there are many potential causes for wheel slide, and data needs to be acquired from a wide range of sources, the current approach to analysis is to simply use each individual data source, essentially in isolation. However if all the sources were examined in unison, using the power of Big Data analytics, it is probable that the industry would not only identify the major contributions more effectively and efficiently, but it would also probably make important discoveries of problem areas that would otherwise remain hidden.
Through ‘Knowledge Discovery from Data’ (KDD), there is a potential saving of millions. Over the last few years there has been significant anticipation associated with the use of Big Data techniques for the analysis of rail-related data; however the major expectations have yet to be fully realised and we are currently performing an analysis of real-time train data to prove the technique.
For this project we have used in-service data that includes GPS positioning, braking and power application, dwell times, information on wheel slide, sanding application, speed, acceleration and more. It predicts when a train or tram is most at risk from sliding, when the driver needs to be warned and when sand needs to be applied to stop wheel slides. By reducing the number of alerts and optimising the use of sand, the rail system will be subject to reduced delays and damage.
We are currently looking to apply the techniques that have been used in the prediction of solar flares to ‘multi-variable’ analysis of rail problems. We are working closely with a team from Georgia State University (GSU), one of the leaders in the emerging field of data science, who have recently made significant advances in big data analytics related to prediction of solar flares that we believe can be directly applied to complex rail problems. The GSU techniques are based on the combination of decision trees and deep neural networks feeding off multiple data streams.
This multi-data stream approach to prediction fits well with our ELBowTie© risk analysis methodology. Over the coming months we will be setting up analysis of real time train data to prove the technique. Siemens Train care in Manchester are responsible for maintaining the Siemens Desiro diesel multiple Unit fleet .
For this project there will be supplied in service data from the train including train position (from GPS) braking and power data, station dwell data, wheel slide data, sanding application data, speed, acceleration and so on. We will use this data to attempt to predict when the train is most at risk from sliding and when the driver needs to be warned and when sand needs to be supplied to stop wheel slides. By reducing the number of alerts and optimising the use of sand, the system will be less subject to delays and damage from wheel flats.
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