Digital Twin Technology

Digital twin technology involves creating a virtual replication of a physical object, system or process. This digital counterpart is designed to accurately reflect the real-world entity, allowing for simulations, monitoring, and analysis real real-time.

Here are some key aspects of digital twin technology:

Real-Time Data Integration: Digital twins can be connected to real data sources, such as sensors on physical objects, which continuously update the digital model to reflect the current state of the physical counterpart.

Simulation and Analysis: They can simulate various scenarios to predict outcomes, optimize performance, and identify potential issues before they occur.

Lifecycle Management: Digital twins span the entire lifecycle of the physical entity, from design and manufacturing to operation and maintenance.

Enhanced Decision-Making: By providing a comprehensive view of the physical entity, digital twins help in making informed decisions, improving efficiency, and reducing costs.

Digital twins are used in various industries, including manufacturing, healthcare, smart cities, and more, to enhance performance, predict maintenance needs, and optimize operations. GE Vernova leverages digital twins in several of their software solutions. For example, for Energy Industries digital twins are used for predictive analytics to reduce unplanned downtime. For power generation, a digital twin is enhanced with AI/ML for automated gas turbine tuning to significantly reduce missions. For manufacturing, digital twins are used for advanced analytics and real-time data integration to improve industrial processes.

A digital twin in manufacturing is a virtual representation of a physical asset, process, or system. It integrates near real-time data from sensors and IoT devices to create an accurate simulation that reflects the current state of the physical counterpart. This allows manufacturers to finetune production processes, predict and prevent equipment failures, improve efficiency and quality of part production, and enable data-driven design and optimization. They can also enable real-time diagnostics and prognostics and enable plug-and-play customization and modular improvement. Key benefits include enhanced decision-making through data analytics, reduced downtime by anticipating failures, and improved product development cycles through testing and simulations. Digital twins enable manufacturers to experiment with changes in a virtual environment before applying them in the real world, leading to increased efficiency and innovation. This concept supports Industry 4.0 initiatives by fostering a more connected and responsive manufacturing ecosystem.

There are different methods and steps for creating and building a digital twin. The first step is to define the purpose of the digital twin, which will determine the model type. For example, is the digital twin for future state, present state, or predictive maintenance or process optimization? The next step varies on the standard work of the team. It could be collecting the basic data on how the physical product operates, creating a blueprint or building a detailed model, and then pairing it with the data.

Note: to accurately represent the real-world object or process, a deep understanding of the object or system's design, performance and maintenance requirements are critical. If data was collected first, developers then will create blueprints or simulations that are based on that data. Either way, the information is fed into the model to create a digital twin. If the digital twin is to provide near real-time data, developers can use coding to use real-time data to update and replicate what is happening in the real world. The digital twin is connected to the physical asset through data streams. Next, data analysis and algorithms are implemented. Machine learning, for example, will be used for predictive analytics. To visualize the data and insights, a developer will create interfaces and dashboards.

Digital twins simulate the behavior, performance, and interactions of its physical counterpart. Depending on the maturity of the digital twin technology, people may be able to visualize the data or receive insights and recommendation on how to optimize. In advanced software, digital twins can also be used for What-If scenarios and/or predict behavior.

When you purchase a solution that leverages digital twins, the vendor will likely ask your company to assemble a team that includes IT, operations, and users. This team is necessary to integrate data from various sources such as IoT sensors, systems, and other databases. A digital thread will be established to ensure a continuous flow of data between the physical and digital worlds. Before making the purchase, the vendor will check if your company has all the required technology. Sometimes, this technology, such as an Edge device, is included in the solution package. Other required technology, like IoT sensors and data storage, might need to be purchased separately. Next, the digital twin model is built. This is followed by validation and testing to ensure accurate real-time or near-real-time data collection. A reliable data collection infrastructure is essential to capture and transmit the data. This process may involve multiple systems or a single cloud platform. Digital twin implementation may also require additional sensors. To receive insights, alerts, and recommendations, deep domain knowledge is needed to build machine learning models and algorithms. Interoperability is crucial for integrating the digital twin into simulations for accurate modeling. The final step is to deploy the digital twin and implement it in your company’s operational environment.

Digital twins do not inherently come with Artificial Intelligence (AI). If the digital twin is integrated with AI, however, can be integrated with AI. If integrated with AI, the digital twin will be able to perform more complex tasks typically done by humans, such as making autonomous decisions and interacting with other AI systems. AI ranges in abilities and may include machine learning, natural language processing, robotics and more. AI can also simulate various scenarios and predict outcomes. Generative AI can create new data and generate possible future states of physical object or system. This can be used to test different methods or strategies. Machine Learning is a subset of AI that focuses specifically on developing algorithms that can learn from data and includes pattern recognition. Since ML excels at recognizing patterns and correlations in data, ML digital twins can help with predictive maintenance, anomaly detection and optimization based on historical data. Domain expertise is essential for creating accurate and relevant models. The domain expert(s) will have knowledge about the physical object's behavior, constraints and operational environment, all of which improve the model performance.

Digital twins have numerous benefits. These benefits include:

Enhanced product development with virtual prototypes to identify potential issues and refining prior to physical production. Predictive maintenance by monitoring the condition of assets and equipment and alerting to an issue before it happens, reducing downtime, and extending the lifespan of assets.

Operational efficiency with continuous analysis of data, digital twins can help to optimize operations and reduce costs.

Risk Management with simulating and analyzing potential risks enabling improved preparedness and mitigation strategies.

Sustainability with help monitoring and reducing environmental impact by optimizing resource usage and minimizing waste.

The challenges that digital twins solve depend on the industry and purpose for which the digital twin was created. Here are some key challenges that digital twins help solve:

Operational efficiency to identify inefficiencies and optimize processes by simulating different scenarios and predicting outcomes.

Downtime reduction by monitoring systems in near real-time, digital twins can predict and prevent equipment failures.

Remote management can be enabled by digital twin software, which can be particularly useful for power generation in remote and hard-to-reach areas, construction, and manufacturing.

Cost savings by optimizing operations and maintenance schedules, digital twins can help to significantly reduce costs.

Data driven decision-making by providing real world insightsSustainability with visibility on ways to optimize resource usage to reduce greenhouse gas emissions.

In the oil and gas industry, a digital twin is a dynamic, virtual representation of a physical asset or system. It can be created using near real-time data. This technology offers several benefits and applications:

Predictive Maintenance: Digital twins continuously monitor equipment performance and can be designed to use predictive analytics to forecast potential failures, allowing for timely maintenance and reducing downtime.

Operational Optimization: By simulating various operational scenarios, digital twins help optimize processes, improve efficiency, and boost productivity. For example, they can model the flow of oil and gas through pipelines to identify bottlenecks.

Asset Performance Management: Digital twins provide a comprehensive view of asset performance, enabling better management and optimization of critical assets like oil rigs, refineries, and pipelines.

Safety and Emergency Preparedness: Digital twins can be designed for running simulations where the digital twin model output is projected into the future based on past data to simulate emergency situations, helping operators prepare and respond more effectively to potential hazard.

Environmental Monitoring: Digital twins help monitor environmental parameters, ensuring compliance with regulations and minimizing the environmental impact of operations.

A real example of a digital twin is GE Vernova's Asset Performance Management (APM) SmartSignal predictive analytics software. The solution leverages digital twins of critical assets such as turbines, compressors, and entire power stations. The digital twins in SmartSignal use near real-time data from various sensors to monitor and predict the performance and maintenance needs of these assets. By using digital twins connected to near real-time data, predictive maintenance and prevention of unplanned downtime is made possible. Total EP uses SmartSignal to achieve zero unanticipated failures by leveraging a monitoring center powered by this digital twin technology. Another real-world example is STEG that caught a 4MW recovery using GE Vernova APM Performance Intelligence, which leverages physics-based digital twins to ensure entitlement. When assets are not reaching entitlement, the solution is able to provide visibility to where the issue is occurring and recommendations on how to fix it. This is all possible from digital twin models customized to the plant's equipment, system design and dispatch provide.

The four main types of digital twin are component twins, asset twins, system twins, and process twins. Each type of digital twin offers unique benefits and can be sued in various industries to improve efficiency, reduce costs and enhance performance.

Component twins are digital models of individual components or parts, such as motors, sensors or valves. They provide detailed information about a component's performance and behavior. If connected to sensors, this might be in real time or information over time.

Asset Twins represent entire assets, such as machines, gas turbine or vehicles and include all the components that make up the asset. Asset twins help in understanding how different components interact and affect the overall performance of the asset.

System twins are digital representations of entire systems such as a production line or a power plant. System twins provide insights into how different assets work together within a system, helping to optimize performance and efficiency.

Process twins focus on the process and workflows within a system. They help in understanding and optimizing the flow of operations, such as manufacturing processes or supply chain logistics.

Different mathematical models can be used in the four types of digital twins. For example, physics-based models in a component twin will model a motor understanding its thermal and mechanical behavior under different operating conditions, whereas in system twins, a digital twin may simulate the interactions between various assets (like gas turbines and generators) to optimize overall efficiency and predict potential failures.

Digital twins are used in a variety of applications across different industries. Key examples include:

Predictive maintenance: By continuously monitoring the condition of equipment, digital twins can predict when maintenance is needed, as well as extend the lifespan of assets. GE Vernova’s SmartSignal, for example, uses digital twin technology to help companies prevent unplanned downtime and unexpected failures.

Product Development: They allow engineers to test and refine designs in a virtual environment before physical prototypes are built, speeding up the development process and reducing costs.

Performance Optimization: Digital twins can simulate different scenarios to find the most efficient way to operate a system or process, leading to improved performance and reduced cost.

Improved Decision-making: With near real-time data and insights, advanced digital twins can simulate different scenarios and predict outcomes helping to optimize processes.

Operational efficiency with continuous analysis of data, digital twins can help to optimize operations and reduce costs.

Resources:
Click here Deloitte link
https://www.sciencedirect.com/science/article/pii/S2212827120314438
https://www.sciencedirect.com/science/article/pii/S2667344423000099
https://www.challenge.org/insights/creation-of-digital-twins/
https://akselos.com/digital-twins-a-comprehensive-guide/#What_is_a_Digital_Twin_and_How_are_they_Created