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As MATISA celebrates its 75th anniversary, the Swiss manufacturer is looking at how it can continue to provide the rail maintenance and renewal machines of the future.
At the end of WWII, the railway lines that had crisscrossed Europe were in terrible shape. By some estimates, over 70 per cent of all track and bridges were destroyed in France. To rebuild the continent, a new way of constructing and maintaining track was needed so that people and goods could easily be transported, and connections could be created between nations that were previously at war.
Prior to the 1940s, most track construction and maintenance was conducted manually, requiring large gangs of workers to complete the heavy tasks. To meet the scale of what was required in 1945, a new way of working needed to be found.
In Switzerland, a contractor by the name of Auguste Scheuchzer saw this need and developed the prototypes of three machines that would overcome these challenges.
These were a weeder, which could scrape the surface of the ballast; a combined excavating and screening machine that would clean the ballast; and a tamping machine, which could compact the ballast under the sleepers.
A newly formed Swiss company bought Scheuchzer’s designs, and these became the foundation of modern track renewal and maintenance as it is known today. That company, Matériel Industriel S.A., commonly known as MATISA, is now celebrating its 75th anniversary and has continued to build on this history of product innovation and unique solutions designed for the rail environment.
Having begun to produce and manufacture machines that were based on Scheuchzer’s original designs, current CEO Franz Messerli tells Rail Express that MATISA saw a need for these machines to better fit the needs of rail operators.
“The tamping machines in the early days were machines similar to those used for civil engineering jobs. MATISA turned them into railway vehicles that can also be incorporated into trains and this was quite a job from a mentality point of view – to make these early machines more like a locomotive than just a working machine.”
With the renewal and maintenance of ballast now able to be mechanised, the next step was to come up with a solution for the rails and sleepers. Again, MATISA took the visionary ideas of Valditerra, an Italian contractor, and turned them into a solution for the rail industry.
“He had this brilliant idea of how you can change the rails and sleepers in one continuous go,” said Messerli. “MATISA also bought this licence from him and then from them moved this technology quite a bit further.”
From these beginnings, MATISA is now known as one of the leaders in the manufacture of track renewal machines. Other innovations that the company has produced include the introduction of high-frequency elliptical tamping, continuous action tampers, the NEMO light-based guiding system and turnout installation wagons. Being able to produce machines that meet the varied requirements of rail operators around the world has led to MATISA becoming the manufacturer of choice for challenging tasks, said Messerli.
“We produced some large machines for the French market that work around Paris where they have very little access to the track. These machines can not only do track renewal but also ballast cleaning, compacting and tamping the track, all within one machine. Combining these functions makes it very important that all systems are working properly because if somewhere in the whole production line fails, then the whole machine is stopped.”
MATISA IN AUSTRALASIA Some of these MATISA machines have made their way to Australia over the years, However, in 2018, the company returned to the Australia and New Zealand market with the creation of a local subsidiary. MATISA’s ability overseas to meet varied requirements sets up the company well for the Australian environment, said Steven Johnson, managing director of MATISA Australia.
“From MATISA’s perspective we have three different railways in Australia. We have a heavy haul environment, which is similar to UIC/European standards from the perspective of vehicle size. While the interstate ARTC network is similar to the UIC standard as well, there’s constraints on the network around Sydney and Melbourne which, when taken collectively, means we have to conform to the Narrow Non-Electric envelope. Then there’s narrow gauge networks in Queensland, Western Australia, Tasmania, and New Zealand.”
These constraints mean that even when equipment is gauge convertible, machines such as tampers, ballast regulators and track laying machines in Australia must fit within one of the three envelopes.
With the increasing demand for track construction and renewal, the supply of equipment is becoming a bottleneck.
“I do think there is the potential for a constraint in equipment. Now that the government is talking about stimulus and how that is going to roll out. It’s going to coincide with Inland Rail as well as current construction projects in the Pilbara,” said Johnson.
With ageing plant fleets currently in service, there may be a need for contractors to upgrade their machinery.
“Tampers and regulators, they start to age. These machines have a limited life; 25 years, and 30-40 years if it’s looked after really well. While some operators are refurbishing equipment to a high standard, there’s lots of that older equipment but that has a limited remaining lifespan,” said Johnson.
Unlike other manufacturers of track construction and renewal equipment, MATISA does not expect to produce a high volume of machinery. Rather, as the company has done in the UK, MATISA will work with a local partner to provide the specialised and reliable equipment that is needed in rail environments.
“Historically, MATISA has grown through working with specialist service providers and providing them with tools and equipment to deliver high quality services to their customer,” said Johnson.
MATISA has been a long-term partner with the Rhomberg Sersa Group, who have used MATISA’s equipment.
In urban rail environments, where systems are becoming more complex with the introduction of new signalling systems and dense networks of lines, MATISA has developed equipment that confronts these challenges.
“The B 66 UC is a continuous action machine for plain track but also contains workheads designed for tamping turnouts. It has the flexibility to avoid obstacles, which especially in Sydney, there are lots of,” said Johnson.
The B 66 UC can tamp sleeper by sleeper in turnouts and includes a third arm for lifting the diverging track. On plain track, the machine achieves the high performance standards expected of modern tamping machines using high-frequency, elliptical tamping technology to ensure accuracy and compaction quality.
As urban networks adopt European Train Control System (ETCS) and Communications- Based Train Control (CBTC) technology with the associated balises and axle counters that sit between tracks, having a tamping machine that will not damage these pieces of technology is crucial.
“There are joints with cabling everywhere, lots of other equipment in and around the track and there’s going to be more. It starts to become more and more hazardous and you need flexibility with where you’re positioning the tamping tools, so having the universal machine or a combination machine, which is really good on plain track but also really good at turnouts, is important,” said Johnson.
The alternative is to miss critical sections of track.
“So they’re tamping and they miss two sleepers, right where the joint is, right where the train stop is, so the joint is going to get worn out quicker, the train stop is going to get damaged quicker. The point of failure has become an even greater point of failure, and that’s the consequence of just using plain line high-production machines in a congested environment,” said Johnson.
Being able to produce the machines that meet these technical demands is partly down to MATISA retaining its manufacturing base in Switzerland, said Messerli.
“We have good access to qualified personnel. I worked for a couple of years in the UK and you find engineers easily in the UK but an engineer that is also prepared to put an overall on and go alongside the machines and get their hands dirty, that’s more difficult to find. In Switzerland, we have a dual training system which is an apprenticeship scheme that normally goes for four years that also includes a theoretical component.”
Combining practical and theoretical knowledge means that those who work in the production side of MATISA can problem solve and find creative solutions to customer’s requirements.
“If we made hundreds and hundreds of identical machines then we would already have left Switzerland many years ago, but having the technical specialists next to the place where you produce and assemble the machine is kind of a key for these machines that you often tailor around customer needs,” said Messerli.
THE NEXT 75 YEARS As MATISA reaches its 75th year it is continuing to innovate in its tradition of providing customer-focused solutions for rail track and maintenance. Although Switzerland has largely deindustrialised,
Messerli sees a future for MATISA in the country in providing high-quality, reliable machinery.
“We look back with pride on the last 75 years. We have established a reputation of being a reliable supplier that takes care of the special needs of difficult railways around the world,” said Messerli.
Avoiding dwelling on the past, however, is what will ensure MATISA survives. Messerli is keenly focused on upcoming challenges within the rail industry, and how MATISA will meet new requirements. One area the company is investigating is the digitalisation of track construction and renewal.
“Digitalisation is part of our agenda, but we have to do it in a clever way,” said Messerli. “We have to find a system that is helping to make our machines more reliable and to help in preventative maintenance. The worst thing that can happen to a yellow machine is if it breaks down on a major line, so implementing predictive maintenance technologies to make sure that this machine will not break down is very important.”
MATISA is also looking to develop machines with lower energy consumption. This includes investigating ways of using the braking energy from a discontinuous tamper to accelerate the machine to the next sleeper.
What could be the greatest shift, however, is the implementation of artificial intelligence. With autonomous trains already running in many regions, similar forces are at work in the field of track maintenance. Johnson sees three key reasons why automation will become the norm in trackwork.
“Firstly, people don’t want to work nights, in the rain, or in the heat, and the machines are getting more and more complex, so finding people that can fault find and do repairs and maintenance is getting harder,” said Johnson. “Secondly, customers are sick of the machines hitting equipment and these machines do hit stuff, regularly. So if we can find solutions that reduces the incidence of equipment getting hit then that makes everybody’s life simpler.
“The third reason is productivity. There is a real opportunity for industry to maximise the output from each of these pieces of equipment, be more consistent but also increase speed. If the machine knows what’s going on, where it is, and what it’s doing then it will be able to take over, preparing itself for the next function. Then, during the advance of the machine, all the tools are ready for the next insertion. That will deliver huge benefits in time and speed of tamping turnouts and plain track. A consequential benefit of all of that will be less wear and tear on the machine, fewer repairs to track equipment because it’s not getting hit, the guys that are operating aren’t as tired, and they’re not making mistakes.”
MATISA has developed its Human Assistance Track Intelligence (HATI) platform that can be used on multiple machine types. The sensor system is being developed to learn the track, identify where obstacles are located, and integrate this data with the control of the tamping tools and lifting clamp. The real-time, machine learning of HATI is an example of MATISA looking at the issues its customers and the rail industry are facing and developing a solution to meet this need.
“We have to continue listening to our customers,” said Messerli. “But we also have to talk to the railway administrations because they look at the needs of five, 10, even 20 years in the future.”
Having a history of being at the forefront of rail machinery, MATISA is prepared to provide the rail industry with solutions for the next 75 years.
Artificial intelligence (AI) will be applied to CCTV footage from cameras on the Sydney train network to detect threatening behaviours.
The trial is the result of Transport for NSW’s Safety After Dark Innovation Challenge, which sought initiatives to improve safety for women travelling on public transport.
The AI CCTV solution was proposed by the University of Wollongong’s SMART Infrastructure Facility. The software would automatically analyse real-time footage and alert an operator when it detect a suspicious incident or unsafe environment.
Lead researcher John Barthelemy said the software could be applied in a number of ways.
“The AI will be trained to detect incidents such as people fighting, a group of agitated persons, people following someone else, and arguments or other abnormal behaviour,” he said.
“It can also identify an unsafe environment, such as where there is a lack of lighting. The system will then alert a human operator who can quickly react if there is an issue.”
The project is based on PhD student Yan Qian’s research that is using computer vision across multiple cameras to improve understandings of traffic and pedestrian movements.
“We are using open-source code that tries to estimate the poses of a human being and predict if there’s a fight,” she said.
“As far as we know, nothing like this has been attempted globally. We are pushing the limits of the technology.”
Other successful projects came from data sharing platform She’s a Crowd, safety technology vendors Guardian LifeStream and Cardno/UNSW.
Minister for Transport Andrew Constance said that transport operators had an obligation to improve the experience of travelling on their networks.
“We want all our customers to feel safe on the network and it is not good enough that 9 out of 10 Australian women experience harassment on the street and modify their behaviour in response,” Constance said.
“The winners were chosen for their potential to meaningfully address real safety issues, and their ability to use creative and sophisticated new technologies to make a real difference.”
4Tel is working to bring the latest in artificial intelligence technologies to simplify the uptake of condition monitoring.
In a report prepared for Infrastructure Australia ahead of the first Australian Infrastructure Audit, consultants GHD surveyed the maintenance needs of all major categories of Australian infrastructure. When it came to rail, the report found that maintaining Australia’s diverse rail networks was a high priority and in regional rail in particular there was a high likelihood of a coming maintenance gap.
For the regional rail networks, the combination of competition with road freight and existing infrastructure reaching the end of its useful life left much of these networks facing maintenance issues. As the provider and maintainer of train control technology for the Country Regional Network (CRN), Newcastle-based software and hardware engineering firm 4Tel is on the front line of developing innovative technology solutions that provide the ability to bridge the maintenance gap.
General manager of control systems Graham Hjort describes how condition monitoring has been enhanced on the Country Regional Network through application of an Internet of Things (IoT) approach.
“The I/O ports on selected field signalling and telemetry assets are connected to a modem which connects the ports remotely back into a central asset management system called 4Site, which then allows the health of the asset to be interpreted and, if need be, alarms or reports triggered based on the information received from the asset.”
The process also allows changes to be directed back to the field asset by the reverse connection to change selected settings.
“Another way in which condition monitoring has been improved is through improved analysis of information from the field sites,” Hjort continues. “One of the typical functions that 4Site is able to perform is a real time analysis of how long it takes a set of points to move between positions. If the time taken for those points to move and lock into place is above an acceptable threshold, an alarm is raised via 4Site and an appropriate course of action initiated.
By tapping into the existing telemetry, for remote connectivity, 4Tel has been able to remotely control field assets and their reporting without the need for any additional communications hardware. When you start to talk about return on investment, it is minimal outlay, maximum return.”
While this approach to condition monitoring has its benefits, unless maintenance providers use asset condition information as part of their infrastructure maintenance practices, then the benefits may be illusory.
Many physical rail assets are unable to provide an interface for health information, however 4Tel is using emerging technologies to solve this issue. In 2018 4Tel partnered with the University of Pretoria, South Africa, to understand the role that Artificial Intelligence (AI) and Machine Learning (ML) could play in remotely identifying and assessing the health of rail infrastructure. This relationship, along with an existing relationship with the University of Newcastle, NSW, has proven fruitful by providing a platform for researchers to practically apply their work to solving current issues facing one of the largest industries across the globe. With students from these universities, 4Tel is exploring how AI will improve operations for both train operators and rail infrastructure maintainers.
4Tel’s senior artificial intelligence scientist, Dr Aaron Wong is part of the 4Tel Artificial Intelligence Engineering team that includes staff in Australia and internationally. He also continues his work as a conjoint lecturer at the University of Newcastle.
“The use of AI not only can assist in the identification and analysis of defects and faults, but it can also help to reduce cost and risk by allowing the AI to trudge through the data to identify the areas of concern,” said Wong.
Putting these software-driven solutions into practice has also enabled 4Tel to take condition monitoring beyond signalling and cover a broader range of rail infrastructure.
“AI allows us the ability to move beyond track circuits, points, and interlockings for condition monitoring. AI can be applied to rail, ballast, sleeper, and structural defects,” said Wong.
With rail maintenance vehicles and trains travelling across the network, 4Tel is developing a suite of sensors and cameras which are able to easily be fitted to a range of vehicles to provide continuous monitoring of rail condition. The aim of this project is that faults are able to be identified in real time, geo-located and tagged, and then reported back to a maintainer, said Hjort.
“What we are aiming to do here is detect where the fault is or is developing, and if needed, send the maintenance team information about the defect to allow them to conduct their initial assessments before they’ve even left their depot.”
Wong highlighted that ML teaches the AI system the different characteristics of a fault or defect.
“Then the system will be able to utilise that learning in future assessments to identify these faults as they develop over time,” he said.
The introduction of AI into the rail industry in Australia is just beginning with practical applications across a range of environments.
“4Tel’s AI solution allows for multiple inputs into our AI and Machine Learning application. We are able to cater for all the different environments that impact rail operations including in areas of low light such as tunnels, fog, and other challenging spaces including those with high traffic, with the aim of reducing people in the corridor.” said Wong.
“Once the information has been captured through the sensors and/or cameras, the AI processing mines through the data that is collected and then provides detailed assessments to the maintenance provider on the state or the health of the asset,” he said.
AI can significantly shift the rail industry in Australia to more proactive maintenance structure. While this is an example of 4Tel using AI to monitor the health of rail infrastructure, the application of this technology also extends to the above rail operations.
Railway networks and train operations are going to be extensively impacted by AI-based innovation over the current decade and in the future.
Using a digital twin to drive operational decisions when it comes to maintenance is about turning what could be a cost into an asset.
By 2025, the world will be creating 175 zettabytes annually, according to market research firm IDC’s Data Age 2025 report. To put that in context, one zettabyte is equivalent to one trillion gigabytes. How rapidly this data is growing can be demonstrated by the fact that in 2012, only one zettabyte of data existed.
But, with all this data being produced, how much of it is actually useful? While a rail organisation is only a small proportion of the global data total, according to Andrew Smith, solutions executive responsible for Bentley’s Rail and Transit solution, they are still producing a significant amount of data.
“Rail organisations typically are very data rich,” said Smith. “They’ve got a large number of asset disciplines because it’s a huge complex system and each of those asset disciplines has a number of inspection and measurement mechanisms that can produce data.”
This data on its own, however, is not yet a useful resource.
“Data is a discrete fact about something,” said Smith. “For example, the distance between the left and right rail at this location is X, but data is no use to you when you’re actually trying to either work out short term what you’re going to do or longer term what may happen in the future. What you need to do is start a transformation process, so the first step of that is to go from data to information, which is data in context with meaning attached.”
Giving data its context turns what can be seen as a cost, the accumulation and storage of data, into a resource, information that can be used to make a decision.
“In order to be able to do that, you need to have a framework in place that allows you to pull all the different classes of data together, such that you can see all of that data in context,” said Smith. “And to me, that’s at the heart of the digital twin.”
Digital twins are a replica or model of a system or asset that can be used to take the information that a rail organisation has, in the form of data, to create insights, that are conclusions drawn from data and information.
“When you bring all this information together, the digital twin can tell you how as well and why things are happening, and it can give you contextual history,” said Smith. “The digital twin can give you design intent information that you wouldn’t necessarily have otherwise, as well as the as-constructed record. Critically, a railway is a system, it’s not just a set of isolated components, and what a digital twin allows us to do is understand specifically the relationships between those components and how they can be affecting each other.”
While digital twins are widely used in many fields, including construction and manufacturing, they have a distinct role to play when it comes to the maintenance and management of rail assets. As the complexity of operating a railway requires various departments covering different skills and mandates, applying a digital twin can overcome the data and organisational silos. Smith, who has been working in the rail industry for over 20 years, highlights one way in which this can be applied.
“For anywhere that’s got overhead electrification for example, if you’re on ballasted track you can move the track from side to side through maintenance, but you need to maintain the relationship with the overhead wires, but these are often managed by two different teams. The digital twin will manage by design the relationship between the two. The maintenance records, where you’re going to go, and the type of maintenance you’re doing means that there is a chance that you will actually introduce a change to the overhead wire relationship. Therefore, you need to tag that work order as needing somebody to go out and actually measure the overhead wire relationship as well, whereas historically that relationship wouldn’t be as tightly coupled.”
DESIGNING A RAIL-BASED DIGITAL TWIN Getting to this level of maturity with a digital twin takes a deep understanding of how a rail network operates and how best to design a digital twin that fits the reality of a rail organisation. Bentley, as part of its portfolio of solutions in the rail and transit space, has experience working with rail operators around the globe to design and deploy digital twins. From this experience, Smith highlights, the usual understanding of what a digital twin is can be re-evaluated.
“Normally if you think about a digital twin you actually start with a four-dimensional model, however railways often don’t think in terms of XYZ axes. They tend to think in terms of linear distances with lateral and vertical offsets and that drives the way that measurements are made, the way that inspections are made, but also the way that maintenance is actually managed. If you’re sending someone to go out and do some tamping along a piece of track, you don’t send them to an XYZ coordinate or a latitude- longitude coordinate, you’ll send them this many metres past kilometre post seven on such and such a track.”
With this in mind, Smith suggests that digital twins in the rail space can be more useful if they are designed to fit the way that railways are understood. Then, the data that makes up the digital twin can be overlaid on the representation of the network. When needed, for example at a station or in yards, this data can be visualised as a three-dimensional model, but linear visualisations may be more appropriate for a section of track.
To get to the point of having a representation of a rail network, a large amount of data will have to be collected and interpreted. As managers of an array of legacy assets, rail organisations can turn to the use of artificial intelligence (AI) to sort and organise the vast streams of data, said Smith.
“One of the challenges that we see with a digital twin for a lot of brownfield sites in particular is that there are a large number of assets in place that are not being represented digitally. Being able to use image recognition or identifying features from reality meshes and then being able to put an attribution against them is a great use of AI to be able to identify where the assets are.”
With this data in place, the twin must be maintained and kept up to date. With networks spanning across hundreds of kilometres, rail organisations can use automated surveys of a network to provide the constant data upkeep needed.
With the digital twin now operating as a living representation of a rail network, defect detection can be done in a way that gets to a root cause, rather than just addressing individual issues. One example, that Smith describes is if measurement scans identify vertical deterioration. A digital twin would then allow for a cross referencing against other assets that are in place, to see if there is a culvert on that section of track.
“Then I’m not going to send a tamper out,” said Smith. “The first thing that I’m going to do is send a crew out to inspect a culvert to see if it’s collapsing over time. The next thing I might want to do there is ask, if I’ve got twin track, am I seeing the same deterioration on both tracks? Normally they’d be considered in isolation, separate from each other. Then I would ask, has any maintenance taken place at this region? That’s not just maintenance of this asset, but all maintenance records, so I could say, ‘Hang on, someone actually went in there and did some maintenance work on the drainage in-between, but it happens to be in an area that’s close enough that it could’ve had an unexpected knock on onto the condition of the track.’”
These kinds of insights can only be gained through the kinds of insights a digital twin is able to offer, by bringing together disparate data and putting that data into context.
DRIVING THE SOLUTION While a digital twin may seem like a laudable goal on its own, according to Smith, the implementation of such a tool only makes sense when a rail organisation has identified what are the issues that it needs to solve.
“The driver here is not a technology change. The driver is to change the way of working, so an organisation has to first understand its current working practices, where the efficiencies and inefficiencies are, where the limitations and constraints may be, and then we can understand the aspirational state, where they actually want to be at some stage in the future.”
Implementing a digital twin begins with understanding the process of going from a current state to an aspirational state in the future. Rather than jumping in straight to a predictive maintenance solution, the first step may be to identify where the current most significant issue is, with a plan or vision to have a predictive system at a point in the future. Understanding where the technology is going to be implemented comes down to working with the people who are going to be using the software.
“It is absolutely critical that those people are engaged right from the outset, not just the management but the end users,” said Smith.
To get people on board, Bentley has used model offices where representative users are invited to be involved in the design process and give their insights into the particular challenges they face.
“Then there’s buy in,” said Smith. “There’s engagement at that side, which means that the final product is a tool that the engineers have designed and set up to help them do their job better that means they’re positive about the tool and they’re positive about the process change that’s in place to be able to do it.”
Rather than success looking like a piece of software that is installed to contract specifications, Smith outlines how in developing a success plan for the implementation of the software, the outcome is about delivering value.
“Owner operators of railways aren’t installing these systems because they like technology. Technology is an overhead to them – it’s a cost, an expense, and it’s a risk, so the only time that it’s worth doing is when they can show that the value is greater than the cost associated with it, so what we’re moving to is making sure that the focus is now on the value to the users instead,” said Smith.
“You can look into the future and run ‘what if’ scenarios. So, I’m going to increase the tonnage over a particular length of rail and I’m going to run a simulation of what that’s going to do to my rail replacement strategy that I have in place. We can use AI on top of this to look both tactically how do I optimise right now, where do I best spend money, but also starting to look further out by running simulations and trying to predict what the impact the change is going to have.”
This value can be defined in any number of ways, but as Smith highlights, it is the process of creating insights out of data.
Alstom is using artificial intelligence (AI) technology to manage passenger flow and maintain social distancing.
The system is currently in use on the Panama Metro, where Alstom has deployed its Mastria multimodal supervision and mobility orchestration solution.
Initially used to manage passenger crowding in peak periods, the system has been adapted to maintain social distancing requirements due to the coronavirus (COVID-19).
“The ability of this tool to analyse millions of pieces data in real time makes it an indispensable ally for operators at all times, but especially in the current context. Simply put, it matches transport offer to demand, no matter the conditions,” said Stephane Feray-Beaumont, vice president innovation & smart mobility of Alstom Digital Mobility.
The system gathers data from a various of data sources, including train weight sensors, ticketing machines, traffic signalling, management systems, surveillance cameras, and mobile network.
This data is then fed into an algorithm, which determines when the network is reaching its capacity limit. The operator can then carry out actions in response to the data, whether that be increasing train frequency, adjusting entry to the system, managing people on the platform, or suggesting changes to transport systems that feed into the rail network.
Since being installed on the Panama Metro late in 2019, Mastria has been mining the system’s data to be able to intelligently predict when the system will be reaching capacity through machine learning techniques. After three months, the system could predict saturation up to 30 minutes before it was visibly observed, enabling remedial action to be taken, and reducing wait times in stations by 12 per cent.
During COVID-19, the system has been used to limit train loads to 40 per cent of maximum capacity. To achieve this, new features such as real time monitoring of passenger density and flows, simulating limiting access to stations, and analysing the distribution of passengers along trains have been developed.
When the COVID-19 threat recedes, Panamanian operators will be able to use the new features to manage the return to public transport, said Feray-Beaumont.
“All experts agree that public transportation, and particularly rail, will continue to be the backbone of urban mobility. Artificial intelligence will be our best travel partner in this new era of mobility.”
There have been significant advances in the realm of artificial intelligence (AI) and autonomous vehicles in industry, with segments such as the mining sector pioneering the implementation of this technology – including autonomous trains – in Australia. Driving these developments is the computing systems that enable autonomous platforms and AI to operate effectively and securely. This white paper discusses the technology in context of its global growth and Australian adoption. It provides insights into the features that enable the success of in-vehicle computing systems in harsh environments, along with examples of why and how these systems are best suited to industrial rail applications.
Digital twins have become one of the most talked about topics because of their promise to leverage innovation to improve design, visually enhance collaboration, and increase asset reliability, and performance, explains Meg Davis, senior product marketing manager for the Bentley AssetWise transportation asset management products.
However, rail is a very traditional and safety-sensitive industry, and with the backdrop of owner-operators and project delivery firms needing to work within tighter budgets, shorter deadlines, and with increased legislation, change can be slow and challenging.
While the risks associated with changing a tried-and-true formula weigh heavily on the minds of those responsible, the upside is that the highly complex nature of rail networks and systems allow for the opportunity to innovate and leverage technology to change the way rail networks do business.
Many owner-operators around the world have recognised the potential for digital twins in their work and have begun to explore the opportunities for applying big data analytics, artificial intelligence (AI), and machine learning (ML) throughout the design, construction, operation, and maintenance of rail and transit networks.
What is a digital twin?
A digital twin is a digital representation of a physical asset, process, or system, as well as the engineering information that allows us to understand and model its performance. Plainly stated, a digital twin is a highly detailed digital model that is the counterpart (or twin) of a physical asset. That asset might be anything from a ticket machine or escalator in a station, through track and the switches and crossings within it, to related infrastructure like overpasses or overhead line structures, right up to and including an entire city.
Connected devices and sensors on the physical asset collect data that might relate to condition or performance that can be mapped onto the digital twin to understand how the physical asset is performing in the real world, but also, through analysis or simulation, how it might perform in the future or with a different set of parameters.
Why are digital twins important?
Digital twin technology has existed in industries like manufacturing for many years, driving lean processes, improving performance, and predicting and highlighting components at risk of failure. Additionally, digital twin technology ensures that the lessons learned contribute to design enhancement and are applied to future products and systems. The relevance and influence of digital twins, which span the entire asset lifecycle, are significant when applied to rail infrastructure.
During the planning, design, and construction of a new railway or major upgrade, project digital twins can enable the optimisation of design in line with operational requirements and reduce the risk of delayed or nonconformant construction through simulation. Project digital twins can also improve logistics and communication within the supply chain, which can help maintain the schedule and budget.
During operations, performance digital twins become the most valuable. Owner-operators gain insight when inputs from Internet of Things (IoT) connected devices, such as drones that deliver continuous surveys to provide real-time tracking of asset changes in real-world conditions, add to the digital representation. This transparency helps owner-operators prioritise and improve maintenance or upgrades.
Consequently, the most significant value a rail or transit system can achieve is through the successful implementation of digital twin technology. By using digital twins to plan, design, and build the network, and utilising the digital twin during operations, a rail or transit owner-operator will improve performance and reliability.
With the application of AI and ML, analytics visibility gained from big data can provide insight and immersive digital operations to enhance the effectiveness of operations and maintenance. In this instance, access to performance digital twins might enable staff to anticipate and avoid issues before they arise or improve reaction times to system failures to reduced downtime.
With the application of drones and robots, plus AI-based computer vision, automating inspection tasks via a digital twin experts can conduct inspections remotely, increase productivity, leveraging the value of specialists, and reducing the risk of exposing team members to dangerous environments.
Realising the potential of digital twins
There must be practical solutions for the synchronisation of the physical asset’s changing condition to realise the full potential of digital twins. The timing and scope of this synchronisation is key because certain assets update in near real-time, which can be critical to their reliability. For others, a weekly, monthly, or even annual update on condition may be sufficient. Therefore, it is important that the organisations and professionals involved have a clear strategy when setting the criteria for synchronisation, including which assets should be analysed, when, and by what parameters.
However, merely capturing and representing physical conditions, including IoT inputs, can never be sufficient enough to understand, analyse, or model intended improvements, without also comprehending the digital engineering information used in the project’s or asset’s engineering design and specification.
Digital engineering information is like the “digital DNA” for infrastructure assets. Just as doctors can analyse human DNA to anticipate health issues and personalise care for better health outcomes, project delivery firms can harness digital engineering information to enable collaboration, improve decision making, and deliver better project outcomes.
For owners, leveraging “digital DNA” is all about creating and using digital twins to their full advantage—personalising asset maintenance and maximising asset reliability and uptime. It is about creating an open, connected data environment (CDE) that provides trusted information wherever and whenever it is needed to help design, build, operate, and maintain physical assets. Then, owners will use digital twins to make better decisions, gain more efficiency, and improve performance.
Current networks are the digital twins for future projects
Bentley sees its users advancing digital workflows and using intelligent components, and digital context to improve project delivery and/or enable assets to perform better, every day and all around the world. One organisation achieving these objectives is Maharashtra Metro (Maha Metro) in Nagpur, India.
Maha Metro’s implementation of Bentley’s OpenRail solution uses iModels as its final delivery format due to their ability to provide reliable, long-lasting asset models for reference. The organisation is committed to a full lifecycle approach and has deployed a digital project delivery system with OpenRail’s connected data environment (CDE) at its core and encompassing every phase of the asset lifecycle from planning to performance.
Maha Metro’s CDE is configured to record all data and uses asset tags to link components created with Bentley’s open modelling applications, such as its enterprise resource planning system. Hundreds of thousands of drawings and documents are transacted among approximately 400 users within the CDE currently, providing real-time access to trusted information wherever and whenever it is needed. The expansive CDE also provides data mobility to close communication gaps, speed up design issue resolution and approvals, and achieve millions of US dollars in cost savings.
The digital DNA Maha Metro and its supply chain is creating during design and construction will allow the organisation to manage current, future, and refurbished assets. By ensuring this trusted information remains current and accessible, the organisation’s system will enable strategic decision making, establish condition-based monitoring, and progress toward predictive maintenance strategies that are expected to save at least USD 222 million over 25 years of the railway’s operational life.
It is clear that digital twins are gaining momentum, particularly within organisations that presently have IoT initiatives. The emergent nature of digital twins will require an approach with clear business objectives and an agile approach to experiment and learn from experiences. Just as Maha Metro is setting the agenda and direction for the industry, we at Bentley fully expect to see the use and adoption of digital twins become common place within rail owners and their supply chains.