Using what is already there has allowed Infinitive to deploy an advanced predictive maintenance solution based on existing data.
Rail and transit owners have recognised the potential for better outcomes by using digital twins in analytics, artificial intelligence, and machine learning in simulations and decision support throughout the lifecycle of design, construction, and operations.
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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.
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.”
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.