GE Vernova is the Pioneer ofDigital Twin TechnologyApply advanced analytics and machine learning to reduce operational costs, risks and emissions and accelerate your Digital Transformation and Energy Transition with Digital Twin TechnologyExperience Predictive Analytics in Action Title text Overview Types of Digital Twin Technology Asset Digital Twins GE Vernova’s SmartSignal Predictive analytics software uses AI/ML Digital Twins and is trusted by the world’s foremost energy industrials. More than 350 OEM-specific and OEM-agnostic models cover the critical assets within the energy sector.Our Industrial Managed Services team uses SmartSignal on behalf of customers to monitor more than 7,000 critical assets worldwide and have saved them more than $1.6B.GE Vernova also uses 3D Digital Twins that dynamically visualize and contextualize data for operations and maintenance activities help make inspectors, planners, maintenance technicians, corrosion analysts, and Risk Based Inspection (RBI) analysts. Grid Digital Twins GE Vernova’s Geo Network Management, ADMS, AEMS, and DERMS technologies help grid operators create a Grid Digital Twin, which provides a real-time view of the end-to-end network of assets, based on operational data and model. It includes as-is, to-be, and real-time views of the network and enables all departments to contribute and consume data that help deliver better outcomes. Grid Digital Twins can help operators simulate the behavior of the grid over various time horizons and optimize the operation of the grid. Process Digital Twins Process Digital Twins create models of ‘the best way’ to run a process in a given environment – often referred to as ‘the golden batch.’ By identifying the most optimal process to manufacture a given product, plant operators can ensure they are consistently delivering against quality, cost and volume objectives.Through our Proficy CSense solutions, Process Digital Twins help manufacturers meet the challenges of fast-changing consumer demand, regulatory requirements, and looming generation knowledge gap with results like reduced product waste by up to 75%, quality complaints reduced by 38%, throughput increased by 5% – 20%, and OEE increased by 10%. View All Customer Stories Trusted by Leading Energy Industrials Expertise in Digital TwinsGE Vernova is both the pioneer in Digital Twin technology, as well as a current innovator.350+Asset Digital Twins1.6B+Asset Digital Twin Cost Avoided20%Process Digital Twin Reduction in Planning Achieve Zero Surprises and Zero DowntimeLearn why GE Vernova carefully considered the engineer’s needs when it selected similarity-based modeling (SBM) as the technology foundation for its SmartSignal predictive analytics solution.Download Whitepaper The World’s Leading Brands Harness the Power of Digital Twin Technology's Italian Green-Tech Power Generator Reduces EmissionsThe team was alerted to a 10% decrease in plant cooling capability. This sub-optimal efficiency led to more carbon dioxide (CO2) emissions being released.Read Now Manufacturer Improves Asset & Process PerformanceThis manufacturer optimized control of their cement cooler process to maximize heat recovery, energy efficiency and reduce net energy consumption with Proficy CSense.Learn More Lonmin Increases Throughput, Revenue and MarginIn its smelter operations, Lonmin had a 10% increase in throughout, 25% - 45% decreases in variations, and other significant outcomes.Learn More Total EP Achieves Zero Unanticipated FailuresThe monitoring operation includes 30,000 sensors monitoring 260 shaft lines and 540 critical assets. Data is sampled every 10 minutes to limit the burden on data storage.Read Total EP’s SmartSignal Case Study A2A Establishes M&D Center, powered by SmartSignalLearn how this power generator monitors six sites with 10 gas turbines from various manufacturers, developing customized models despite diverse equipment.Explore A2A’s Cloud APM Case Study Load More 3X APM LeaderIndependent analyst, Verdantix, calls out GE Vernova’s market-leading Asset Performance Management software – leveraging Digital Twins – as supporting industrial firms’ energy transition efforts.Download Verdantix GQ 2024 Report Explore Digital Twins Reliability Interactive DemoJoin a team as they investigate a problematic pump adding an analytic that alerts them when the asset’s performance deviates from expected performance.Explore Digital Twins Use Cases The Benefits of Predictive Analytics Across IndustriesPredictive Analytics can protect our access to the basic necessities or energy and water in a time of extreme volatility.Read Blog Sensor Health Monitoring Offers Confidence in Data & AlertsBy identifying and improving any gaps in sensor data, users can have more confidence in alerts from the Digital Twins.Learn More FAQs FAQs What is 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. What is a digital twin in manufacturing? 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. How do you create/build a digital twin? 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. How does digital twin technology work? 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. How to implement a digital twin? 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. Is digital twin in AI? 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. What are the benefits of digital twins? 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. What challenges do digital twin solve? 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. What is a digital twin in oil and gas? 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. What is a real example of a digital twin? 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. What are the four types of digital twins? 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. What are digital twins used for? 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 linkhttps://www.sciencedirect.com/science/article/pii/S2212827120314438https://www.sciencedirect.com/science/article/pii/S2667344423000099https://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 How can we help you? Thank you for getting in touch!We’ve received your message, One of our colleagues will get back to you soon. Have a great day!