In the railway sector, ‘digital twins’ could be used across several different engineering, planning, and operational aspects, such as monitoring physical assets (rolling stock and infrastructure), monitoring train movements and providing information about passenger behaviour on-board trains, in stations or on platforms.
Mean distance between failures
A twin isn’t just a visual 3D model of the line. Each element within the model – every section of track, every switch, signal, and siding – is also a data repository. By selecting an element, users can see what it is made of, its specifications and tolerances, details of when it was last maintained, data that shows its importance to operations
It is therefore clear that digital transformation improves a passenger’s experience of the railway and the quality of services a rail system can provide. Technology is advancing now at a pace that allows the previously unimaginable to become routine, and for us to harness levels of data we could previously only have dreamed of.
[Digital twins] are helping us develop an even greater understanding of our passenger movements and the infrastructure that underpins the rail network, helping us to clearly see how we can make journeys better for our passengers.
— Richard Thorp, Engineering Director, HS11
Benefits of digital twins
Operations, maintenance, headquarters whilst reducing the number of communications from the moment a fault was reported to it being fixed.
Maintenance based on data, not miles.
Condition-based anticipation of service needs.
By spotting track issues earlier, major disruption can be avoided, and delays minimised.
Reduces in-person track inspections, saving labour and travel along the network.
Allows continuous checking at no additional cost. Avoiding major disruptions and reducing service delays by repairing issues before they impact services.
Repetitive review (anytime, any number of times, ML, AI) of faults in a controlled environment.
AI maintenance review.
Automatic, 24/7, instant, and accurate.
Scenarios to service trains more cost effectively—simulating changes such as train fleet maintenance regimes, scheduling strategies, depot capacities.
Reliable estimates with data and simulations to support proposals, be flexible with project changes and consider limitations, and then monitoring delivery implementation.
Optimise design plans, capture design v as-built changes, model to make forecasts, considering end-of-life options.
... sensors have been installed on each switch to measure things such as the time of a swing to graph the smoothness of its swing motion, motor current drawn and so on. Over time, they help to build a picture of what ‘normal’ looks like. As soon as a switch trends away from normal or a certain variable passes a set threshold – for instance a switch takes too long to swing open – operators can react and create a maintenance plan designed to prevent any downtime. This process change is already helping to significantly improve performance across the line.
— Ronald Powell, GM Rail & Transit, SNC-Lavalin2
Nextspace digital twins
Nextspace’s platform provides the data interoperability that brings multiple technologies together in one data model for analysis and visualisations to help teams understand complex situations at a glance—more informed and faster decision making.
fewer unscheduled depot stops through precise maintenance planning
in maintenance costs by eliminating unnecessary work like premature part replacement
100% fleet availability
through elimination of unplanned downtime3
reduction of track geometry defects
in fault-related communications4
- “HS1’s digital twin trial gives a glimpse of the future of railway” by Richard Thorp, Global Railway ReviewVisit link
- “How digital twinning is making Canada’s trains run on time” by Ronald Powell, Global Railway ReviewVisit link
- “Maintenance intelligence for rail vehicles” by SiemensVisit link
- “Empowering the rail industry to leverage digital solutions to improve performance” by Gerard Francis, Global Railway ReviewVisit link
When implemented correctly, digital twins deliver significant ROI. This is why more industries are building digital twins into their core asset and operational management processes.
Data-first digital twins built on Nextspace are customizable and extensible. Our platform helps you integrate, federate, and futureproof valuable data.
There’s so much information out there, so we’ve collected our favourites
Igiri Onaji, Divya Tiwari, Payam Soulatiantork, Boyang Song, and Ashutosh Tiwari
Taylor & Francis Group, Informa UK
A robust research paper focused on the question: “How does the digital twin concept support the realisation of an integrated, flexible and collaborative manufacturing environment as one of the goals projected by the fourth industrial revolution?”
Leaders must take pragmatic and targeted actions to improve their enterprise data quality if they want to accelerate their organization's digital transformation.
Data quality management systems are thoroughly researched but the advent of Big Data might pose some serious questions pertaining to the applicability of existing data quality concepts. This paper aims to investigate various components and activities forming part of data quality management such as dimensions, metrics, data quality rules, data profiling and data cleansing.
Martin Van Holten, Chris Greenwood, Alastair Pearson
An entry-level article by PWC Australia of the benefits of industrial digital twins
Utility reduces CSO volume by 80%, saving $400 million in CapEx spending using “smart sewer” technology
Xylem content team
A case study from Xylem with quantified ROI from the implementation of Xylem smart wastewater technology for the City of South Bend, Indiana.
SAS Content team
SAS. Originally published in The Economist
The SAS content piece advocates that heart of a digital twin is analytics. Not whether you can collect the data, but whether you can turn it from data to valuable transformative information.