Public Funding for Monirail Ltd

The aim of this project is to develop a Quantum-based navigation system for railways to address the issues arising from the loss of GNSS signal in tunnels. Our system will enhance positioning accuracy without relying on expensive infrastructure-based solutions and focus on demonstrating the condition based maintenance use-case. The project will create a highly accurate Quantum Inertial Navigation System (QINS) based on quantum sensors developed in world-leading research laboratories from Imperial College and University of Sussex. Guided by rail systems engineering expertise from University of Birmingham and PA Consulting, quantum sensors will be integrated into MoniRail's existing train monitoring system creating an innovative GNSS-free navigation solution. This will be coupled with positioning and navigation experts Qinetiq and manufacturing specialists, Unipart, to deliver a project that will advance the commercialisation of the technology significantly. This system will be tested on London Underground's live rail network to demonstrate the value of accurate navigation. Trial results will guide the development of the commercialisation roadmap to address applications in Condition Based Monitoring (CBM) and Train Control and Signalling Systems. These use cases have an accessible market of many billions of pounds in the UK and favourable prospects for export to Europe and further abroad. MoniRail, a UK SME, specialising in in-service track monitoring will lead the project in partnership with TfL, Imperial College, University of Sussex, University of Birmingham and PA Consulting (PAC). The project has the potential to unlock significant potential maintenance savings for rail infrastructure managers and has significant commercial potential in the UK and abroad as a key-enabler for rail CBM. In addition adoption and development of the RQINS technology for train-control systems has even greater potential with a global of ~£30bn.

The aim of this project is to develop a Quantum-based navigation system for railways to address the issues arising from the loss of GNSS signal in tunnels. Our system will enhance positioning accuracy without relying on expensive infrastructure-based solutions and focus on demonstrating the condition based maintenance use-case. The project will create a highly accurate Quantum Inertial Navigation System (QINS) based on quantum sensors developed in world-leading research laboratories from Imperial College (MEMs) and University of Sussex (magnetometers). Guided by rail systems engineering expertise from University of Birmingham and PA Consulting, quantum sensors will be integrated into MoniRail's existing train monitoring system creating an innovative GNSS-free navigation solution. This system will be tested on London Underground's live rail network to demonstrate the value of accurate navigation. Trial results will guide the development of the commercialisation roadmap to address applications in Condition Based Monitoring (CBM) and Train Control and Signalling Systems. These use cases have an accessible market of many billions of pounds in the UK and favourable prospects for export to Europe and further abroad. MoniRail, a UK SME, specialising in in-service track monitoring will lead the project in partnership with TfL, Imperial College, University of Sussex, University of Birmingham and PA Consulting (PAC). The project has the potential to unlock significant potential maintenance savings for rail infrastructure managers and has significant commercial potential in the UK and abroad as a key-enabler for rail CBM. In addition adoption and development of the RQINS technology for train-control systems has even greater potential with a global of ~£30bn.

The aim of this project is to develop a Quantum-based navigation system for railways to address the issues arising from the loss of GNSS signal in tunnels. Our system will enhance positioning accuracy without relying on expensive infrastructure-based solutions and focus on demonstrating the condition based maintenance use-case. The project will create a highly accurate Quantum Inertial Navigation System (QINS) based on quantum sensors developed in world-leading research laboratories from Imperial College (MEMs) and University of Sussex (magnetometers). Guided by rail systems engineering expertise from University of Birmingham and PA Consulting, quantum sensors will be integrated into MoniRail's existing train monitoring system creating an innovative GNSS-free navigation solution. This system will be tested on London Underground's live rail network to demonstrate the value of accurate navigation. Trial results will guide the development of the commercialisation roadmap to address applications in Condition Based Monitoring (CBM) and Train Control and Signalling Systems. These use cases have an accessible market of many billions of pounds in the UK and favourable prospects for export to Europe and further abroad. MoniRail, a UK SME, specialising in in-service track monitoring will lead the project in partnership with TfL, Imperial College, University of Sussex, University of Birmingham and PA Consulting (PAC). The project has the potential to unlock significant potential maintenance savings for rail infrastructure managers and has significant commercial potential in the UK and abroad as a key-enabler for rail CBM. In addition adoption and development of the RQINS technology for train-control systems has even greater potential with a global of ~£30bn.

Rail incidents can take many forms and can results in many different types of intervention from temporary speed restrictions to full track closures. Many incidents either result from or cause track damage and in order to remove any speed restrictions or track closures engineers need to be confident that the track is in a safe condition. It is therefore common practice after many incidents where track damage is suspected or track repairs have been undertaken for Track Recording Vehicles (TRVs) to be required to run the track before passenger or freight vehicles are allowed to run the line again. However, the availability of these vehicles can cause significant delays to line reopening or removal of speed restrictions. MoniRail has developed an in-service track monitoring system that can be permanently fitted to passenger vehicles and is currently on trial with Network Rail (NR) in Scotland and also fitted to 2 Eurostar vehicles on HS1\. One potential use-case for the permanent system is for speedier removal of speed restrictions. However, even with the fixed solution delays are likely as track monitoring systems are only likely to be fitted to 1/3 to 1/2 of all vehicles, approx 1500 of 5100 vehicles. This project aims to overcome these delays by providing track engineers with the first ever portable dynamic track geometry measurement system by modifying the permanent solution into a portable one that can be temporarily fixed to vehicles along with a lineside sensor array that can provide additional safety critical track information to the engineer. This solution will therefore provide immediate track information to track engineers such that can make informed decisions about the safety of the track and to what extent speed restrictions can be lifted or line re-opened.

Current inspection of rail track defects utilises Network Rail's four Ultrasonic Testing Units (UTUs) that traverse the UK network, 64,000 miles of track, in 750 shifts per year. With a limitation of 30 miles per hour for rail track inspection, UTUs cannot meet the high demand and increased capacity of customers. Every day, 4.8 million people travel by train in Britain. Around 200,000 tonnes of freight and goods are transported by rail in that same time frame, supporting businesses and consumers, productivity, and economic growth whilst taking thousands of lorries off the road, and helping in the reduction of greenhouse gasses. A risk-free network of rail tracks across the UK is pivotal to Network Rail's long-term planning process strategy and its vision for running a safe, reliable, efficient and growing railway, in Control Period 6 and beyond. Undiscovered rail track defects lead to asset failure, unscheduled maintenance, timetable delays, accidents, and fatalities. Train delays cost passengers 3.6 million hours in 2016, whilst over £72M was claimed by passengers from operators for service disruptions in 2016/17\. With the growing demand on rail transport by passengers, there is need for commercial solutions that offers high-speed (i.e. above 60 miles per hour) high resolution, rail track inspection, and data analysis in real-time. A commercial solution with the capacity to enable UK network-wide coverage. This RAPPID project seeks to address the challenges that the UK rail network faces regarding rapid high-speed high-resolution identification of rail track defects, data collation and analysis, enabling real-time predictive analysis, and predictive maintenance of rail tracks across the UK network and globally. The RAPPID project is based on the novel use of Virtual Source Aperture non-destruction testing techniques in combination with artificial intelligence and deep-learning methodologies that enable real time data processing and analysis of rail track data derived via use of next generation phased-array ultrasonic testing hardware.

Regular, cost-effective collection of accurate track geometry and condition information by measurement of acceleration data from the train body and running-gear remains a key barrier to condition-based maintenance (CBM) approaches for rail-track maintenance. Adoption of CBM approaches could save \>£100M/yr in rail maintenance costs. Rail-track and vehicle maintenance is costly relying on scheduled and reactive maintenance often causing delays. In 19/20 Network Rail (NR) spent £1.395Bn/yr on track maintenance; and track fault speed-restrictions caused 2,460,453 delay minutes. NR use 7 inspection trains, each costing \>£8m to build and £5.5m/yr to operate, track is inspected every 4-26 weeks, insufficient frequency for CBM approaches. MoniRail's solution directly addresses the challenge and is a retrofittable, low-cost sensor systems will be fitted two Eurostar trains and data collected during an 8 month period from the trains running in-service and using data-analytics seek correlation and/or trends in the data to identify developing track and vehicle faults and identify underlying route causes for rough-rides. Development of predictive algorithms will provide engineers with early sight and prediction of failure to enable more effective planning of maintenance. The solution has the potential millions in maintenance costs and reduce delays for passengers.

Current inspection of rail track defects utilises Network Rail's four Ultrasonic Testing Units (UTUs) that traverse the UK network, 64,000 miles of track, in 750 shifts per year. With a limitation of 30 miles per hour for rail track inspection, UTUs cannot meet the high demand and increased capacity of customers. Every day, 4.8 million people travel by train in Britain. Around 200,000 tonnes of freight and goods are transported by rail in that same time frame, supporting businesses and consumers, productivity, and economic growth whilst taking thousands of lorries off the road, and helping in the reduction of greenhouse gasses. A risk-free network of rail tracks across the UK is pivotal to Network Rail's long-term planning process strategy and its vision for running a safe, reliable, efficient and growing railway, in Control Period 6 and beyond. Undiscovered rail track defects lead to asset failure, unscheduled maintenance, timetable delays, accidents, and fatalities. Train delays cost passengers 3.6 million hours in 2016, whilst over £72M was claimed by passengers from operators for service disruptions in 2016/17\. With the growing demand on rail transport by passengers, there is need for commercial solutions that offers high-speed (i.e. above 60 miles per hour) high resolution, rail track inspection, and data analysis in real-time. A commercial solution with the capacity to enable UK network-wide coverage. This RAPPID project seeks to address the challenges that the UK rail network faces regarding rapid high-speed high-resolution identification of rail track defects, data collation and analysis, enabling real-time predictive analysis, and predictive maintenance of rail tracks across the UK network and globally. The RAPPID project is based on the novel use of Virtual Source Aperture non-destruction testing techniques in combination with artificial intelligence and deep-learning methodologies that enable real time data processing and analysis of rail track data derived via use of next generation phased-array ultrasonic testing hardware.

no public description

This project looks to develop research undertaken over the last 10 years at the University of Birmingham into analysis of track data and its application towards predictive maintenance on the rail network. The project will develop commercially robust software and hardware that can collect and analyse track data to provide predictive track maintenance solutions that could significantly reduce delays on the rail network.