Public Funding for Incremental Solutions Ltd

Incremental and Amey will bring together their skills and solutions to develop SPRINT. A first of a kind look into the granular detail of performance inefficiencies caused by speed restrictions. SPEED will integrate existing data from Incremental's AEGIS and Amey's SPRINT solutions to overlay speed restriction data with speed profiles for differing class units to understand the impact speed restrictions can have on sectional running times, but also to identify where performance initiatives could be put in place to improve overall performance of the network. To do this, Incremental and Amey will combine to-the-second berth-level information, accurate GPS movement data, train descriptor information and the live possession planning database to fully map train movement across the network. The large array of detailed data shall allow users can accurately identify: * Which speed restrictions are causing the most impact to performance on a cumulative basis, * How speed restrictions affect differing class differently * How acceleration and deceleration vary at different speed restrictions, * Which speed restrictions cause the most knock-on effect to the timetable, * How driving technique differs amongst the fleets. In addition to allowing users to accurately identify locations causing the largest impact, it will provide rail colleagues with all the necessary information to proactively mitigate these issues and optimise performance.

Railways cost too much to maintain and put workers at risk while looking for track defects. Our proposal uses existing trackside assets in a new way that has the potential to do away with the need to send out workers onto the tracks looking for problems instead of solving them. Railways are high-value national assets and are a critical part of national transport infrastructure globally. Around £9Bn is spent annually maintaining railway infrastructure to ensure safe and reliable operation -- with nearly £2B being used to locate and rectify defects. Over a million manhours were spent last year walking the track. This reliance on manual inspection and intervention puts workers in a risky environment and is not in keeping with the ambitions of the railway. Our proposed innovative solution will define and detect unique acoustic signatures using existing trackside fibre-optic cables and combines it with a novel system tracking train location to yield an automated always-on monitoring system that does away with the need to send workers onto a live railway to hunt for defects. The FORSE project will develop and trial an innovative railway monitoring system designed to automate the detection of multiple defect types over large lengths of the network with low deployment costs compared to existing bespoke monitoring solution. Our novel concept will fuse distributed fibre-optic acoustic sensing data from trackside interrogators with train location and type data from onboard systems to generate new insights on track and train defects that cannot be detected from these systems in isolation. The resulting monitoring solution has the potential to significantly reduce the resource requirements for rail asset inspection allowing resource to be redirected and targeted towards fixing problems -- reducing safety risk for rail workers and improving asset value and availability.

With the world pushing towards a global carbon neutral society, rail travel will need to become a staple part of people's daily commutes. For this to ever to come to fruition, improvements to rail travel must be made and on-time performance must increase so that commuters and passengers can travel by train with the assurance that they arrive when they need to. Incremental believe that this must start at the bottom up and our AEGIS analytical platform will enable the Canadian Rail Industry to delve deep in to cause and effect of all their rail delays. With this knowledge at hand, funding and mitigating measures can be applied in the correct areas to achieve the best improvements. Similarly, AEGIS will then be able to validate these measures from it's ability to compare run on run journeys. With the addition of live train visualisation tracking, users of the software will be able to make more informed decisions when delays have occurred and better manage the network on both a micro and macro scale to alleviate any accumulating delays.

In order to maximise the growth opportunities currently being presented to the rail freight industry through the availability of increased capacity today and in the future, intermodal terminal transition improvements must be made to better optimise the modal shift from rail to road and rail to port. TRACO will provide an innovative solution to this challenge by presenting better and more accurate location information and predicted terminal arrivals to all organisations within the logistical supply chain. By accurately tracking and predicting arrivals for all vehicles, wagons and containers within the supply chain, paper-based systems can be reduced, and freight operations can be better planned and managed to improve the delivery of goods for customers. With this information readily available, rail freight growth opportunities such as high-speed deliveries and perishable goods can be realised thereby further boosting the £1.7Bn which freight already brings to the economy By reducing uncertainty in the timings of freight movements, and demonstrating enhanced predictability of modal transitions, capacity, growth and diversity of goods can be maximised and a much-needed modal shift towards rail freight can be realised, helping to reduce road congestion, optimise port logistics and bring about a significant decrease in carbon emissions.

BACKGROUND TO THE PROJECT: An electrified railway requires a pantograph on top of the train to connect with the overhead line via carbon strips attached to the pantograph. An uplift force is applied by the pantograph to maintain the contact between the carbon strip and the overhead line. These carbon strips wear due to the motion of the train and so to maximise their life the overhead line is set up with a horizontal “zig-zag” (stagger) to distribute the wear across the full width of the carbon strip. Additionally, variations in the uplift force between the wire and the carbon strip lead to local notches. Excessive stagger or notches can cause the contact point between the wire and the pantograph to come off the end of the carbon strip resulting in pantograph flips and in extreme cases a dewirement. To monitor and maintain the equipment, measurement of the interface between the overhead line and the pantograph is required and set out in in British Standard 50317:2012. The key challenge for OLErt is to provide a system that CONTINUOUSLY monitors the condition of the OLE and pantograph and IMMEDIATELY alerts the relevant rail team when either a catastrophic failure occurs, or an emerging problem has exceeded parameter limits. A further challenge is to deliver the system using existing on-train sensors and cameras so that costs are kept low for both installation and maintenance. KEY TECHNICAL CHALLENGES: The milestones described in question 4 outline the key activities required to deliver OLErt. A number of technical challenges and brand-new innovations will be brought to bear to deliver the OLErt system. These are 1. To re-engineer the existing algorithm so that real-time image processing is achieved. The current algorithm was designed and developed through an R&D project using Matlab academic coding techniques. This has resulted in a processing time ratio of approximately 10 x real-time to produce the output. In order to improve the processing speed of the algorithm, we will build on the previously completed R&D to further evaluate the technical design of the algorithm and re-write the Matlab code using industrial coding techniques and standards. Further research into hardware upgrades will also be investigated if required. 2. To enhance the existing algorithm to detect dewirements, pantograph flips and close-to-failure events IMMEDIATELY so that operational teams can take emergency preventative and damage-limitation actions. To achieve this the algorithm will be enhanced to instantly analyse each frame of video in real-time, by comparing it against tolerance limits and provide data to a centralised system when a tolerance limit has been exceeded so that a failure or emerging alert can be generated. The existing algorithm has partly achieved this, but the volume of false positives must be reduced to make the algorithm fit for purpose. OLErt could also be integrated to auto pan-lower or train stop systems in a next phase. 3. To augment the processed video data-stream with accurate video positional data to allow the trending engine to use accurate positional references for run-on-run pattern matching analysis. The video positional technology has been developed using forward-facing rail camera algorithms. These algorithms will require to be adapted to provide positional referencing output using pantograph cameras and then merged with the vid data. 4. To integrate the algorithm on to existing train hardware. Icomera currently provides onboard communications systems, passenger wifi and sensor aggregation hardware across two thirds of the UK rail fleet. The image processing algorithm will be integrated on to this existing hardware and testing will be conducted to ensure the processing power is sufficient for real-time image processing. Following successful testing, the existing Icomera data transmission process established during the PoC project will be used to transmit the data from the train to a centralised cloud-hosted database provided by Incremental 5. To develop a run-on-run trending algorithm which uses machine learning and data collected during previous R&D studies, to detect and alert on emerging problems so that preventative maintenance can be undertaken by the relevant operational rail teams before a dewirement or failure occurs. 6. To develop joint TOC and NR procedures to respond to alerts and responses provided by the OLErt system. The procedures for response to static intelligent infrastructure are already well developed. These will be expanded to include the results from OLErt, which will be an early use of train borne intelligence. The routing will allow operational critical decisions to be made – diverting or stopping services as necessary. Most notifications will be of indicative change and therefore align with risk-based maintenance requirements. These will route via control to the maintenance scheduling process – allowing a timely adjustment or replacement of OLE or train equipment to mitigate risk of system outage from more serious failures if the fault is left unattended or unobserved.

RAPPORT is an industrial research project developing technology solutions to reduce disruption and delays in UK rail. Working in collaboration with key industry partners and academic institutions including Network Rail, Arriva Rail North, Icomera UK and Leeds University Institute for Transport Research, the RAPPORT project will develop and help bring to market a suite of innovative and revolutionary technology tools that will transform operational awareness of train locations and movements. Through the exploitation of enhanced location information and interactive mapping, RAPPORT will deliver useable tools with practical operational benefits. These include accelerating accident and emergency response times to rail incidents; providing supplementary information to signallers at User-Worked Crossings to enable more accurate train location awareness and better decision making; improving service recovery procedures by presenting deeper insights into real-time delays caused by incidents. By introducing innovative technology to existing systems provided by collaboration partners, the project will deliver a live trial of high value, low cost, state-of-the-art products with immediate use and benefit to the rail network, its users and moreover to the UK economy as a whole.