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Two New Appointments for Digital Rail®

Damian Borowiec

Digital Rail Ltd are delighted to announce the  appointments of:

Dr Rhian Davies

We are pleased to say that Dr Rhian Davies has joined Digital Rail Ltd, as a Data Scientist . Rhian has worked as a data scientist for the last 3 years having graduated with a PhD in Statistics from Lancaster University from where she also obtained a first class degree Maths and Statistics.

Dr Rhian Davies

Dr Rhian Davies

Damian Borowiec

Damian is a computer scientist graduate from Lancaster University. Damian won the Chancellors medal for exceptional performance as an undergraduate, a highly prestigious award.

 

Damian Borowiec

Damian Borowiec

They will both we working on a data analytics project which is seeking to optimise train maintenance by scheduling the maintenance each night based upon the health of the train. We are happy about these appointments as the railway has to attract new young talent and soon!

Natural Language Search Analytics

Using Natural Language Processing to Empower Data Democratization

•Enables non-technical users with the freedom to create reports and dashboards by their own.
•Increases productivity and the time spent on creating reports and dashboards.
•Empowers users with data-driven decision.

Streamlining Safety Case document production on large projects

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.

Lancaster University Management School Student Project

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.”

 

 

Using artificial intelligence to identify non-compliance against engineering standards for railways

Artificial Intelligence

Get paid to do a Masters with the Centre for Global Eco-Innovation at Lancaster University (The Sunday Times’ University of the Year 2018) and Digital Rail Ltd.

 

The Project

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.

 

Enterprise and collaborative partners

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).

 

1 Year Enterprise-led Funded Masters by Research, Ref. No. 90

  • Get paid £15,000 tax-free
  • Have you tuition fees reduced. Your partner company pays £2,000 towards your fees, meaning UK/EU students pay £2,260, and International students pay £15,945.
  • Be part of the multi award winning Centre for Global Eco-Innovation with a cohort of 50 talented graduates working on exciting business-led R&D.
  • The Centre is based at Lancaster University, so you will gain your Masters from a Top Ten University, recognised as The Sunday Times University of the Year 2018.
  • Finish in a strong position to enter a competitive job market in the UK and overseas.

 

Apply Here

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.

Application deadline

Midnight Tuesday 19th June 2018

Start: October 2018

We Assessed Shanghai’s New Driver-Less People Mover

Shanghai Metro

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.

 

Shanghai Metro

 

Research, Development and Deployment at Digital Rail

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.

Data Fusion Group (DFG)

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.

Data Fusion Group

 

Autonomous Vision Group (AVG)

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.

PTI Vision

 

Intelligent Systems Engineering Group (ISEG)

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.

WEFACT work management tool

Intelligent Computer Vision Agents Optimising PTI Safety and Train Dwell Times

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.

Findings

The Vision system:

  • Can detect where passengers are on the platform (if they cross the yellow line)
  • Alerts be sent to drivers, staff and other stakeholders if someone is stuck in the gap
  • Can get a measure of the number of passengers on both the train carriages and the platforms
  • All using existing camera infrastructure
  • processing can be done in real time on a computer the size of a credit card (Raspberry Pi for example)

How it works

  • Uses CV analysis with filters applied to remove clutter
  • Computationally efficient
  • Detects static and moving objects
  • Distinguish between objects based on motion criteria
  • Analyses both platforms and carriages
  • Provides a busyness measure for each, compensating for perspective distortion

Platform Train Interface Vision System

Stakeholder Benefit

Passengers (customers)

  • Improved safety at the Platform Train Interface
  • Optimisation of their Platform Train Interface experience

Drivers

  • Increase driver’s experience and go some way to reduce stress and pressure
  • Assist the driver to maintain on-time services by reducing passenger boarding dwell time, further helping to reduce safety related incidents

Train and Station Operators

  • Improved safety of passengers through a reduction in Platform Train Interface FWI incidents
  • Faster passenger boarding at the Platform Train Interface can aid station operators in running a safer and quicker service
  • (side effect) improved passenger movement will help to reduce larger crowds and subsequently improving safety

Upcoming Conferences

Conference Railway

7th European Transport Research Arena

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

Fourth International Conference on Railway Technology: Research, Development and Maintenance

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.

www.railwaysconference.com

Upcoming Conference: Railway

Reducing the Carbon Toll of Railway

flood

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.

 

Development V Lifecycle NLP

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.

 

Linkage between Resilience and Environmental Impact NLP

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.

NLP Intelligent High Resilience Low Carbon System Development Model

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.

flood NLP