As asset-intensive industries search for more ways to meet sustainability goals, digitalization has become key to optimizing resources and improving overall efficiency of business processes. This is driving greater adoption of “digital twins,” which help companies understand the impact of their physical assets today to forecast future scenarios in order to make informed decisions that support sustainable operations. In fact, digital twin market size, which was valued at USD 12.9 billion in 2022, is expected to grow at a compound annual growth rate (CAGR) of 36.3% through 2030.

The role of digital twins in asset management

A digital twin is a virtual representation of an object or system that spans its lifecycle. Updated from real-time data, digital twins use simulation, machine learning and reasoning to help decision-making.

A digital twin plays a key role in asset management by creating a virtual replica of a physical asset. It could represent a bridge, an airplane, a train, a building, a large or small machine component, or even an entire city.

By centralizing asset data in an intelligent asset management platform, a digital twin can bridge the gap between the physical and digital worlds. For asset-intensive industries, such as energy and utilities, a digital twin can be used to mirror an entire asset lifecycle from design to testing, to construction and commissioning, to maintenance and operations, to end of life. By generating and collecting an abundance of operational data, a digital twin helps mirror and monitor the energy and utility systems, providing powerful insights that improve overall efficiency and optimize maintenance activities.

The supporting roles of Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML)

At the business enterprise level, IoT, AI and ML all play integral roles in supporting a digital twin, coming together to create an intelligent asset management solution. IoT allows equipment and devices to be interconnected so they can collect data that’s usually transformed into dashboards and visualizations to identify patterns, detect anomalies, trigger actions based on rules, and predict outcomes. While IoT sensors capture information, AI and ML work hand in hand, teaching the system to make the best decisions for the asset and thus optimize performance.

Digital twin technology integrates information from EAM systems with IoT sensor readings, data feeds from geographic information system (GIS) and weather intelligence to identify patterns that affect assets and equipment. Looking at the renewable energy industry, wind turbines can be outfitted with IoT sensors that collect real-time data and operational status. This data is then relayed to a processing system and applied to the digital replica, or digital twin. Once informed with such data, the virtual model can be used to run simulations, study performance issues and generate possible improvements. This allows wind farm operators, for instance, to leverage the data from a digital twin to predict when issues will occur and take maintenance actions before disruptions occur or predict the outcomes of, say, changing the power rate of the turbine.

Real-time information on how a wind turbine is responding to specific weather data such as air pressure, temperature, wind direction and turbulence intensity can also be used in the development of future digital wind turbines. In short, a digital twin provides a risk-based approach that uses centralized asset information to simulate operations based on historical data to predict future changes.

Using IoT data to measure asset health and performance

Another industry where digital twins and intelligent asset management solutions are changing the game is civil infrastructure. Sund & Bælt, which owns and operates some of the largest infrastructures in the world, is moving from time-based to predictive maintenance.

To that end, Sund & Bælt engaged with IBM to create an AI, IoT and digital twin-powered system to help prolong the lifespan of aging infrastructure by streamlining inspections and their predictive maintenance strategies . The solution includes the IBM® Maximo® Application Suite, which helps inspect the conditions of bridges, tunnels and other infrastructure components by accessing insights with mobile devices on location and using AI technology to analyze data. This intelligent asset management solution centralizes this data along with maintenance records, design documents and 3D models to help identify issues such as corrosion, rust, cracks and stress. This information is then used to create a digital story of the asset, which can be used to identify potential failures before they occur, improve engineering design, predict future performance and optimize maintenance schedules.

Meet sustainability goals with digital twin technology

The Downer Group, a leading integrated urban services company in Australia, began working with IBM in 2017 to modernize its technology platform, embedding digital and intelligent capabilities into its infrastructure operations. Seeking a roadmap to reduce its carbon footprint across its rail and transit systems, Downer recently entered a new engagement with IBM for the ongoing development and enhancement of its asset management platform, TrainDNA. Currently powered by IBM Maximo Application Suite, collects complex analytics and near real-time data to support predictive maintenance efforts for more than 200 trains across Australia. Downer and IBM’s joint sustainability team are now leveraging the platform to find ways to reduce energy consumption of the rail fleet. By understanding which rail systems are using the most energy — and understanding how they need to respond throughout the day as the weather and passenger demands change — Downer can optimize use and improve overall sustainability outcomes.

Creating a digital story with IBM Maximo Application Suite

By creating a digital twin of a physical asset, you can tell a story that answers what-if questions, and present the insights, in an intuitive way.

IBM Maximo Application Suite — a single, integrated cloud-based platform that uses AI, IoT and analytics — effectively creates the pages in that digital story, containing everything you need to know about a certain asset throughout its lifecycle. This includes when it was first commissioned, how it was built, the cost of the asset, how it was maintained and all of the essential features associated with it. This makes consumption and usage of a digital twin easier than ever before.