Most railways – whether they are high speed, conventional intercity, commuter or metro – are struggling to maintain rail infrastructure at a level that ensures steady-state performance.
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They are faced with budgetary constraints and the challenge of balancing maintenance time with the desire to provide a high level of service.
What results is a need to make decisions about which maintenance actions are a priority to ensure that optimal safety and reliability are maintained.
Usually resources are constrained and there is not the option to throw more money or resources at the problem and this, put simply, is driving many rail operators to look to technology to help them work smarter and more efficiently.
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Railways don’t tend to be short on data; everyone today is creating or consuming more and more. For today’s rail operator, reams of data come from traditional inspection activities that are increasingly going hi-tech. They’ll have access to a continuous flood of data collected from activities such as track inspections, geometry car surveys, rail defect detectors, ground penetrating radar, rail profile measurements, video surveys, infrared tests, asset surveys, work records, train movement, wayside detectors and more.
Meanwhile, the world of social media is also there to provide details of defects such as ride quality and delays. And, for a brand new railway such as High Speed 2, there should be no excuse for starting on day one with anything less than a full suite of data on the railway as built.
In many ways it is data overload, or at the very least a significant challenge.
But, if you can harness that data and turn it into useful information such as a prioritised plan for maintenance, then you can find the holy grail and not only deliver steady-state asset performance under constrained resources, but actually improve the asset condition and provide a positive return on investment.
Technology is available. For example, Bentley Systems has been steadily building up interest in its Optram linear asset decision support software.
In essence, Optram takes that data, cleans it up, analyses it and, by forecasting ahead, enables engineers to make better-informed decisions about maintenance and renewals.
“Optram is not unique in the way it looks at linear assets and optimises their performance, but it is pretty clever in the way it does it,” explains Bentley Systems industry marketing director, rail Steve Cockerell.
It’s certainly been working for Network Rail. Bentley’s Optram software has been used as part of Network Rail’s Offering Rail Better Information Services (ORBIS) programme as the underlying technology for a Linear Asset Decision Support (LADS) system.
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LADS combines data from 14 disparate data sources and more than 60 discrete data types to allow Network Rail engineers to view many aspects of the track simultaneously.
The data in the system is automatically updated on a regular basis – nightly in the case of key datasets.
The system has been rolled out across 35,000km of track, and 800 engineers have access to the system.
The prize is significant: LADS is tasked with saving Network Rail over £110M on track maintenance renewals in the current five-year Control Period 5 alone; this through optimised interventions based on a better understanding of the rail asset.
And, it’s not just in the UK where it is gaining traction.
In the United States, Washington Metropolitan Area Transit Authority (WMATA) was concerned that it was not fully leveraging the asset data it had on its track when making maintenance decisions.
There was no link between manual track walk defects, automated track inspection data, asset information, and work records. Users had to access paper records to understand the asset condition: the information was all there, but difficult to access and interpret.
WMATA decided to use Bentley’s Optram to collate and manage all of this information, and in doing so improve its engineers’ access to information supporting their decision making.
A track skeleton was first created from existing track schematics. This was used as the reference system for all other datasets. Next, a link to the enterprise asset management system was created, allowing access to defects and work records.
Geometry from WMATA’s track geometry vehicle was then superimposed, and, finally, a set of analysis scripts was produced to assist with interpretation of the resulting datasets.
In the resulting system, over 100 WMATA users, including the track and structures department, use Optram to manage day-to-day maintenance planning and work activities.
It’s clearly an approach that could be rolled out elsewhere, and the emphasis is on developers of new rail systems to bear this in mind at the outset.
After all, it is oft-quoted that in the whole-life cost of an infrastructure project, the capital cost is usually just 20% – the vast majority of the spend is in the maintenance.
“If you look at High Speed 2 – or any other high speed railway around the world – if the data is stored consistently, and handed over in the right manner, then that is very useful information source for the operator,” notes Cockerell.
“For example, right now in the Middle East there are ambitious rail schemes being planned and delivered, and the focus can be on delivering railways as fast and efficiently as possible – but, just as important, is to ensure the focus is also on the future of operating and maintaining it,” he adds.
“So, the way in which you procure has a bearing. If you do it the way Crossrail is doing it and you force the common data environment and are very prescriptive about data standards and outputs then the handover is a lot easier.
“If, on the other hand, you have got multiple contracts and let whoever has them do their own thing then that is fraught with risk as you are likely to get different results from each party.”
Which is far from ideal, given the long-term gain available if, and only if, the data is there, and in a format that can yield valuable information.
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High Speed | Rail maintenance efficiency