Our Solutions
Transforming infrastructure-wide condition monitoring
RailLoc
A single centralised map of track features, with automatically positioned condition data, from inspection and in-service trains.
Predictive maintenance is the optimal approach to maintenance in many industries, including rail. However, the sheer expanse of the infrastructure involved, and the impracticality of placing millions of static sensors, poses a challenge to the industry.
Assessing the strategy and thinking alongside our technological capability we reviewed the problem and looked at what could be done differently from the very beginning of the process.
Our solution supplies the link between remote condition monitoring data, both existing and new, and millimeter-accurate location data to create a centralised map of track features for the complete rail network. The map we build is like a blueprint and each additional data stream is a fully positioned layer.
From this map, meaningful information regarding the condition of the network can be drawn to help optimise track maintenance regimes, reduce costs and improve customer experience.
This means that data can be taken from completely different vehicles, and precisely positioned using our software to show correlations and give completely new insights.
Negating the human administration required to position the data from different sources, along with our low cost and easy to fit autonomous hardware, enables condition monitoring to scale across the whole rail infrastructure.
With a proven track record of delivering quality data and value within the inspection fleets of Europe's leading rail infrastructure organisations, our solution is easy to fit and inexpensive, yet highly accurate, making it an ideal system to extend condition monitoring beyond measurement trains and on to in-service trains.
We call this solution RailLoc.
Example Applications
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As RailLoc IM data from multiple runs are independently positioned they can be compared without the use of lengthy manual alignment processes to detect qualitative trending of certain measurements at points of interest (defects, suspects etc.) over time. This may allow detection of gradual or sudden changes before thresholds are exceeded, and so enabling timely intervention works.
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Removing the uncertainty of suspects produced by the UTU fleet. Navigate precisely and quickly to the suspect using the companion track worker handheld app. Upon identification of a real defect or false positive, quick elimination of any repeat false positive suspect being recorded in subsequent runs. Accurate run-on-run positioning by RailLoc removes any ambiguity as to whether the suspect has been inspected before and identified as a false positive.
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RailLoc makes it easy to combine data sets from different measurement systems. With highly accurate position it is easy to overlay OLE or Conductor Rail measurement with track geometry even when data has been recorded using different vehicles at different times.
This can all be combined with video data from different sources (Forward facing video, Linescan, Thermal and IR) allowing you to understand the full picture before attending site and even whether a site visit is needed at all.
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RailLoc produces accurate location continuously everywhere, this makes it easy to combine measurement runs from different sources, whether from the IMF or passenger vehicles (UGMS), you can always determine the exact location of a measurement.
The hardware at a glance:
Inexpensive
Easy to fit
Operates unattended
Minimal comms network demand
Highly accurate
The software at a glance:
Fully automates data positioning
Reduces human administration
Real-time data communications and processing
Integrates with data from other sources
How does it work?
Our camera-based sensor system is fitted to the underside of a train. As the train travels round the network, our sensor is continuously capturing millimetre-accurate location data and third-party condition sensors are taking measurements. The data from the condition sensors is imported, algorithmically positioned and recorded in our millimetre-accurate visual condition map of the track bed. As subsequent runs are made, the new data captured is automatically positioned with previous runs and added to the centralised condition map. The condition map data is then analysed to show any changes in the condition of the network, and more importantly the size of the change and the speed at which that change is being effected. Further analysis can then be applied to predict when and where network failures will occur and so provide decision makers with the information they need to optimise maintenance activity and schedules.
Fault Navigator
Reducing the number of repeat site visits and the time spent finding the location of a fault, directly improves the safety of track maintenance engineers, getting boots off ballast
Track workers frequently fail to locate faults detected by track monitoring vehicles and systems. Track geometry defects are measured under load and may not be evident when visited on foot. Ultrasonic defects aren’t usually visible as they are inside the rail. The fault must be detected again by workers searching along the track on foot.
Failing to locate a fault has a significant negative impact:
A measured defect isn’t verified
Repairs are not done or are done in the wrong place
Costly return visits are required to the same defect
The positional accuracy of traditional handheld apps means that tracks may not be correctly identified potentially causing safety issues. And traditional methods of reporting sensor data make alignment between different data sets difficult or impossible.
Powered with the positional accuracy and ‘right track every time’ of RailLoc, our accompanying Fault Navigator mobile app directs track workers, with visual and audible prompts, to the precise location of faults, allowing them to reject a suspect or validate a defect; quickly, precisely and safely. It works everywhere, even in GPS denied areas like tunnels.
Fault Navigator puts the train technology in the palm of your hand.
Case Study
Omnicom Balfour Beatty and MWV have joined forces for a case study on PLPR (Plain Line Pattern Recognition)
Continuing our involvement with Network Rail’s Class 153 project, which now includes the next generation of Plain Line Pattern Recognition (PLPR). Our RailLoc system working hand in hand with OmniVision Edge from Omnicom Balfour Beatty to provide accurate and timely results to the end users.
The project has always been built on a foundation of collaboration and we are proud to continue that on this exciting new application of the technologies both companies bring.
Working together we have built a case study covering the project so far and the potential for the service to deliver more benefits in the future.Read the full case study here
Rail maintenance
We are currently working with Deutsche Bahn in Germany and Network Rail in the UK to enable them to optimise their condition monitoring and track maintenance.
With the two providers spending nearly €4 billion per year on maintenance, our solution, in enabling more accurate and cost-effective predictive maintenance, is expected to realise a 10% efficiency: €400 million per year.
If this sounds of interest to you, please get in touch, we would love to hear from you.
