How AI can help power digital twins

Digital twins are virtual representations of physical objects or systems used for simulation, analysis, and control. AI enhances this technology by adding sophisticated predictive capabilities and intelligent automation features, revolutionizing how businesses observe, replicate, and streamline their processes. Through AI, digital twins are transformed into proactive tools, as algorithms can analyze historical and real-time data to predict future behavior and potential failures in systems or machinery. This predictive capability saves time and money and enhances safety, particularly in manufacturing and aviation industries where equipment downtime can be costly. Additionally, AI-equipped digital twins in urban planning can optimize energy consumption and predict traffic patterns.

The combination of AI and digital twins revolutionizes various industries by offering advanced predictive maintenance, improved design processes, and enhanced operational efficiency. They also help organizations make smarter, data-driven decisions.

The role of AI in enhancing digital twins

Data integration and analysis

AI plays a crucial role in processing and interpreting vast amounts of data beyond what a regular sensor can. A digital twin of a physical system or object integrates data from sensors, historical records, environmental conditions, and operational inputs. The process begins with data collection and preprocessing, followed by data fusion to create a comprehensive dataset. 

Intelligent algorithms then decide what tests must be run on the data and predict the best action to achieve the desired results. For instance, machine learning, deep learning, and natural language processing analyze this data to detect anomalies, predict failures, and optimize performance. 

Predictive maintenance

AI-driven digital twins revolutionize predictive maintenance for pieces of equipment by continuously monitoring it through real-time and historical data analysis. Sensors track parameters like temperature, vibration, and pressure, feeding data to AI algorithms to access a machine's health and detect early warning signs of potential failures. Machine learning models predict when components are likely to fail, allowing for proactive scheduling of maintenance activities and thus preventing unexpected breakdowns. This approach optimizes maintenance schedules, reduces downtime, lowers costs, and extends equipment lifespan, enhancing operational efficiency and reliability.

Siemens Mobility is a system that uses AI and digital twin technology to predict and prevent railway component failures in real time. The technology allows the organization to understand the environment of a railway system by using data from sensors to understand the infrastructure, passenger load, and any obstacles on the track. This data is essential in enabling semi- or fully autonomous train movement, as well as in visualizing how the vehicle is holding up and identifying maintenance needs. By 2030, Seimen's AI-aided digital twins will predict the performance of railroads and stations through scenario simulations, enhancing operations and planning. 

Optimization and simulation

AI optimizes processes and improves efficiency by analyzing vast data, recognizing patterns, and predicting outcomes. It enhances resource allocation, automates repetitive tasks, and uses predictive modeling for proactive maintenance. 

AI simulates various scenarios through "what-if" analysis and virtual testing, allowing organizations to make informed decisions and adjust dynamically to changing conditions. This testing leads to optimized production schedules, improved supply chain efficiency, enhanced healthcare processes, and reduced energy consumption, ultimately driving cost savings and performance improvements.

GE uses digital twins to optimize and speed up the design process of its jet engines and power turbines. This system helps the company evaluate the design and fluid dynamics of a turbine blade or jet engine component to access a million variations in just 15 minutes, something that would otherwise take two days. Digital twins help cut the design process in half, allowing the organization to do more design work.

Real-time monitoring and decision making

Continuous monitoring

AI enables continuous real-time monitoring of physical systems through digital twins by constantly collecting and analyzing data from sensors and various sources. This constant data flow allows for dynamic updates and proactive interventions, ensuring optimal performance and immediate response to issues. 

For example, in a smart city, AI-powered digital twins monitor traffic systems, energy grids, and public utilities. They optimize traffic flow, predict and manage energy distribution, and ensure the efficiency of water and waste management systems. This technology enhances operational efficiency, reduces downtime, and improves overall system management by providing real-time insights and enabling timely maintenance and adjustment.

Autonomous decision making

AI enables digital twins to make autonomous decisions by continuously analyzing and 'learning' from real-time data from various sensors and sources. By repeatedly analyzing data, digital twins can detect patterns, predict potential issues, and dynamically adjust operations. For example, a smart city infrastructure powered by a digital twin can adapt traffic signals based on congestion or balance electricity distribution according to demand.

For instance, Passive Logic, a Salt Lake City-based startup, provides an AI platform that allows any stakeholder building an infrastructure project to autonomously operate Internet of Things (IoT) components within buildings. Their AI engine comprehensively understands the interplay of building components, even at the physics level, and can simulate building systems and learn how to improve building operations.

Predictive analysis and risk management

Predictive analysis

AI analyzes historical data to predict future trends and behaviors by collecting and cleaning large datasets, extracting relevant features, and training machine learning models to recognize patterns and trends. These models, such as linear regression, decision trees, and neural networks, forecast future outcomes by identifying relationships within the data. Continuous learning and feedback loops refine these predictions over time. 

For instance, a digital twin of an electricity grid can integrate with AI to help the physical structure function reliably during emergency events and operate the grid in autopilot mode. Japan and Singapore use predictive digital twins for their grids to predict outages based on extreme weather conditions by analyzing past data on asset performance and weather prediction data.

Risk management

AI enhances risk identification and mitigation through predictive analytics by analyzing large datasets to uncover patterns, predict future events, and provide actionable insights. For example, oil companies face high risks and costs when exploring new reservoirs or optimizing production. Digital twins for reservoir simulations offer a solution by modeling underground hydrocarbon flows and production strategies on supercomputers. This technology helps companies assess risks beforehand, minimizing losses in new projects and optimizing real-world production based on digital twin analytics. The digital twin can help companies save millions of dollars and avoid environmental risks. 

In the healthcare industry, digital twins can be used for risk management in surgical procedures. For example, a hospital could create a digital twin of a patient's anatomy based on imaging scans. Surgeons can then use this digital twin to simulate the procedure before operating, identifying potential risks and planning the surgery more effectively. Doing so can reduce the risk of complications during the surgery, improving patient outcomes and safety.

Case studies of AI-powered digital twins

Industrial applications

AI-powered digital twins are transforming the manufacturing industry by enhancing operational efficiency, predictive maintenance, and innovation. Here are some fundamental case studies that demonstrate their impact:

Bayer Crop Science

Bayer Crop Science's "Shaping Business Strategy and Future Operations Through Virtual Factory" project leverages AI to create dynamic digital representations of its nine corn seed manufacturing sites in North America. This AI-powered approach integrates machine learning and decision science to model equipment, processes, and product flows, enabling "what-if" analyses for each site. The project has significantly optimized operational efficiency, compressing ten months of operations across nine manufacturing sites into just two minutes, allowing for over 100,000 simulations within 24 hours.

Rolls-Royce

Rolls-Royce has harnessed digital twin technology, analytics, and machine learning to revolutionize aircraft engine efficiency, testing, lifespan extension, and maintenance. The company monitors individual engines in real time, considering flight conditions, pilot usage, and specific mission parameters. This personalized approach optimizes maintenance schedules based on each engine's unique characteristics, extending the time between maintenance and reducing the need for spare parts.

Mars

Mars, the confectionary, pet care, and food giant, has streamlined its supply chain using a digital twin powered by AI and Microsoft Azure. The company employs Microsoft's Azure Digital Twins IoT service to create a virtual replica of its 160 manufacturing facilities. This digital twin, infused with AI capabilities, optimizes operations by processing data from production machines, enhancing capacity, implementing predictive maintenance for increased machine uptime, and reducing waste associated with packaging inconsistencies.

Urban planning

AI-powered digital twins are transforming urban planning and management by creating detailed, real-time virtual models of cities that integrate data from various sources. These models enable real-time infrastructure monitoring, predictive maintenance, and simulation of urban development impacts. For example, cities can optimize traffic flow, energy consumption, and disaster response strategies. 

Singapore's Virtual Singapore is a prime example of using digital twins to manage infrastructure and plan new developments efficiently. This technology enhances decision-making, sustainability, and citizen engagement, leading to smarter and more resilient urban environments. The 'Virtual Singapore' project is a dynamic 3D city model and collaborative data platform that integrates geospatial data from various agencies. It enables planners to conduct "what-if" analyses and simulations to optimize land use, improve service accessibility, and manage infrastructure utilization. The project employs AI tools to create a virtual replica of Singapore's 721 square kilometers of land area. This digital twin processes data from sensors across the city to model factors like traffic, weather, and population density in real-time.

The city is also working on simulating scenarios at different scales—from the entire island down to individual buildings. For example, Singapore's Digital Urban Climate Twin (DUCT) can model the urban heat island effect and test mitigation strategies to address rising temperatures due to climate change.

Training and education

Boeing

Boeing uses virtual environments and simulations to onboard and train new engineers and technicians. Digital representations of aircraft systems and components allow trainees to virtually explore and interact with complex machinery. This hands-on experience helps them understand the intricacies of aircraft maintenance and assembly, reducing the learning curve and enhancing their technical skills.

Walmart

Walmart has implemented virtual reality (VR) technology to train employees in customer service and store operations. Virtual simulations and digital replicas of store layouts and customer interactions help trainees practice handling various scenarios, such as assisting customers, managing inventory, and operating checkout systems. This immersive training enhances employee readiness and improves customer service quality.

DHL

DHL uses virtual environments to train new warehouse operations and logistics management employees. Virtual replicas of warehouses and supply chain processes allow trainees to learn about inventory management, order picking, shipment loading, and route optimization. The AI-driven simulations provide a safe and controlled environment for new hires to gain practical experience and improve operational efficiency.