Recently, the innovation in predictive maintenance has been digital twins. This can be defining—procuring virtual replicas of physical assets, enabling organizations to maximize performance and minimize downtime.
With the growing realization among industries of the advantages afforded by digital twin technology, using virtual models has reformed the way in which businesses tackle maintenance and asset management.
Origins and Growth of Digital Twin Technology
Dr. Michael Grieves introduced the concept of digital twins (and also coined it, in a more modern way) at the University of Michigan in 2002 as an approach to developing a digital model of a real system. With time, predictive maintenance solutions evolved considerably to become much popular among all kinds of sectors.
As highlighted in a report by Markets and Markets, the digital twin market worldwide to represent $48.2 billion by 2026 from $3.1 billion in 2020, with rapidly growing demand for this technology around the globe.
Transforming Maintenance Through Real-Time Modeling
Enter digital twin technology for predictive maintenance, and the way organizations keep an eye on their assets has never been the same. Companies can model the physical behavior of assets in real time (creating virtual models for maintenance).
This helps provide advanced predictive analysis which allows organizations to predict fault and correct them before it goes up as a huge cost. A Deloitte study itself showed that a predictive maintenance program can help reduce maintenance costs by 10-40% and also reduce equipment downtime by 50%.
Enhancing Operational Efficiency in Manufacturing
Digital twins in manufacturing have largely helped enhance the operational efficiency of this sector. Manufacturing companies have used digital twin technology for quite some time to model their production lines and machinery down to the smallest details.
Siemens, for example, used digital twin simulations to replicate the behavior of gas turbines while running. As a result, the company leveraged virtual data to improve performance and longevity of turbines, achieving savings of $5 million per unit. This induces relevance of digital twin technology impact relative to equipment optimization and performance.
Real-Time Monitoring and Predictive Maintenance Strategies
In addition, integration of real-time monitoring digital twins is a key part of predictive maintenance strategies. They use the sensors which are implemented in their physical assets to gather data on certain parameters like temperature, vibration, and pressure.
This data is then used to feed the digital twin, enabling constant monitoring and evaluation. In parallel, a report by Frost & Sullivan found that digital twins could decrease operational costs by 25% through real-time monitoring, making clear the financial incentive to use them.
Applications in Asset Management Across Key Sectors
The conceptual idea of asset management digital twins has also been highly marked out, especially in domains such as oil and gas, transportation, or utilities. For example, companies in the oil and gas industry such as BP have leveraged digital twins to track and control their drilling rigs.
Sensors on the rigs provided BP with data that allowed it to predict when equipment would need replacing, optimize drilling operations, and cut down on maintenance costs. This translated into a reduction in downtime of up to 20% and has demonstrated the effectiveness of digital twin technology for asset management.
Facilitated Decision-Making with Predictive Analytics
Facilitated decision-making is an advantage that has been provided by digital twin predictive analytics. By leveraging historical data and combining it with real-time information, organizations can develop models that predict future performance and maintenance requirements.
In the General Electric (GE) case, where digital twins had been used for jet engines, it helped in forecasting maintenance visits based on flight data available. Using predictive analytics, GE could schedule maintenance while engines were already down for service, minimizing disruptions and maximizing engine performance.
Expanding Beyond Industrial Operations
Beyond manufacturing and asset management, digital twins in the context of industrial operations have been used in other industries like healthcare, automotive, and smart cities. For example, healthcare has seen digital twins of patients created to predict outcomes for treatment based on patient data. An individualized approach allows providers to tailor the treatment experience—optimizing care and outcomes for each unique patient profile.
Innovations in Automotive and Smart City Applications
The automotive industry is also harnessing digital twin technology. Ford, for example, is using digital twins to simulate vehicle behavior and analyze wear and tear in the car. Similarly, the realm of smart cities has even employed digital twin simulations. Digital twins can be used by urban planners and city officials to analyze traffic patterns, resource consumption, and environmental impacts of city infrastructure.
IoT and Digital Twins: A Powerful Combination
IoT & digital twins: The coupling of IoT with digital twins has broadened the horizons for predictive maintenance. Connected assets generate a large amount of data that IoT devices can read, and easy integration with Azure IoT Central enables this data to be ingested into digital twins for analysis.
Future Directions and Skill Development for Digital Twin Technology
One after the other, we are looking at more and more digital twins in predictive maintenance in the future as technology evolves further to amplify their benefits. With companies increasingly adopting predictive maintenance technologies, we will see a rise in the need for digital twin solutions.
Digital Twins Redefining Predictive Maintenance
In short, the application of digital twin technology as part of predictive maintenance is a major step forward in efforts to control assets and performance. The evolution of digital twins is anything but complete and will unquestionably influence the way we tackle predictive maintenance and asset management in the future.
