Today’s Industrial Internet of Things unfolds before our eyes as businesses leverage new and rapidly evolving technologies. The latest concept is the digital twin, a solution that is far more than a product: It is the outcome that industry demands.
The Internet of Things (IoT) has leap-frogged from consumer applications that facilitate mere interaction and collaboration. Industry leaders like General Electric (GE) extended this connectivity to operating machines. The resulting Industrial Internet of Things (IIoT) enables commercial organizations to engage with large complex machines — wind and gas turbines, jet engines, locomotives — to improve performance, reduce downtime and accelerate new product development. But it doesn’t stop there. Today’s cost models for sensor technology, internet connectivity, and simulation and analytics enable connectivity to not only highly complex, capital-intensive machinery but to almost every piece of equipment in operation.
Data and the industrial internet
The IIoT, in practice, is best used to determine or suggest an action: For example, instruct a wind turbine to tilt its rotors for optimum wind exposure. First, sensor data collected from assets are added to all other available digital information. A dashboard combines this information with the equipment’s real-time and expected-performance data to produce descriptive analytics, which can be mined to forecast potential failures and schedule maintenance. The final step is optimization, which considers individual assets in all their configurations along with systems of assets to arrive at multiple solutions. The objective is to optimize a very complex ecosystem around a particular asset. The very rich models describing structure, context and behavior of industrial assets are called digital twins.
There is a cost for this improved performance: The IIoT manages huge amounts of data, extracting information and gaining actionable insights through big data analytics and deep learning. For security, and also to manage the quantity of data, some data is stored and processed locally “at the edge.” Other functions are performed on data in the cloud. This hybrid edge-to-cloud approach helps manage the quantity of data and allows the best computational approach to be taken for different types of objectives while maintaining safety and security of operation and protecting a company’s valuable IP.
Getting started with a digital twin
A digital twin begins with a basic model that describes the asset. For example, a wind turbine model could include PLM system information with details on materials and components, a 3-D geometric model, a simulation model that predicts expected behavior based on physics algorithms, or recommendations from analytics created using machine-learning techniques. The model also can include service logs of maintenance, and defect and solution details, capturing the entire life cycle of the asset.
Initially the digital twin represents a class of assets — in this example, a wind turbine of type x. This generic twin must be individualized for a specific wind turbine on a particular farm. Consider that the machine has operated for five years, enduring weather specific to its location, running among 50 other turbines. So the entire wind farm must be modeled. Each turbine is similar but different based on its position or experience (wind direction, maintenance record, wake effects). In the end, the twin’s rich digital representation contains its past and present condition moment by moment. The future of a specific wind turbine, in this case, is codified in that digital twin.
Digital twins provide accurate operational pictures of assets right now. There is a significant business value in identifying underutilized devices, so analyzing twin information can lead to optimal usage. For example, GE Power leveraged a digital twin to get 5 percent more output from a wind farm without making wholesale changes. The team optimized the turbines to changing wind conditions and orchestrated the interaction of individual twins on-site. One insight seemed counterintuitive: In specific scenarios, shutting down some turbines improved output compared to running all turbines. By predicting potential problems in a fine-grained way, operators can schedule maintenance to minimize service disruption. Once the information is codified across a system of assets, the team can take that knowledge and turn it into actions that will obtain the desired outcomes.
Building a twin model at the outset is the key to creating a rich set of applications that produce asset-related outcomes — not, as some think, just developing a dashboard for equipment operator decision-making.
A full-featured twin makes it easier to develop and deploy applications later. The physics, analytics and simulation information within the model pave the way for machine learning; many digital twins linked together produce a mass of actionable industrial knowledge.
GE Digital leverages ANSYS simulation in its digital-twin use cases, so the two organizations immediately benefit from collaboration
Platforms support the industrial internet
The latest IIoT challenge is how to make such sophisticated technology user-friendly so end-users (who are manufacturers and engineers, but not programmers) can solve business problems.
To that end, GE has developed the Predix® platform to connect industrial equipment, analyze data and deliver real-time insights.
Predix is an aggregation of microservices that are useful in building, deploying and managing industrial internet applications. Customers consult with GE Digital on business problems, such as increasing a wind farm’s output or optimizing a gas turbine that services a variable-power infrastructure. Within a few weeks, these organizations assemble an initial solution to address the problem.
GE also uses the Predix platform internally to optimize its own production processes and build more efficient solutions for customers.
Digital twins can be practically applied in almost every industry
Simulation and the digital twin
For decades, GE has gathered data on many assets, such as jet engines. Combining such data with statistical models predicts what is likely to happen and when — but it falls short of determining why and how it happens. Adding in physics-based simulation is the final step to gleaning this additional insight. GE’s Predix can overlay data with simulation in an industrial context that operates as a common data model. Simulations can be run on-site or in the cloud at scale — pushing models to the edge then bringing insights they create back to the cloud. Complete integration requires connecting to the customer’s PLM system, linking in CAD data and other valuable information recorded in enterprise systems.
A digital twin that centers on a common model and incorporates many information sources enriches knowledge.
GE Digital leverages ANSYS simulation in its digital twin use cases, so the two organizations immediately benefit from collaboration. ANSYS software’s greatest value is in bringing together different aspects of simulation, so it helps designers completely think through their designs. Because a simulation model demonstrates how the assets should work, the twin approach shows exactly when operation is amiss. Digital twins take simulation results out of the design studio and into real life to provide immediate feedback on one asset or many. Soon the technology will enable optimizing an individual asset in the field; furthermore, it could be deployed throughout an asset’s entire life cycle.
Digital twins can be practically applied in almost every industry: transportation, energy, manufacturing, aviation and more. Companies already are saving millions of dollars by bringing together data, simulation, platform, cloud-based functions and machine learning. Organizations can only imagine the future benefits as the digital twin concept grows more prevalent.