Digital Twins and Data Analysis

The U.S. military is exploring ways to make virtually everything—from the uniform on a soldier’s body to the engine contained within a vehicle—connected and mission ready. Troop movements are being monitored, as are soldiers’ health statuses. Aircraft and other assets are providing real-time insight into enemy movements and other potential threats. Decisions are being made based on this information, which has the ability to flow in an unerring stream. Indeed, the Internet of Things (IoT) has further elevated the military’s reputation as a well-oiled machine.   

But, what if the machines that power that machine break down? What if heavy transport machinery stops running, or advanced weapons systems fail?

To alleviate these concerns, military personnel must be able to easily manage the lifecycle of their connected assets, from creation to maintenance to retirement. They can do this by creating a digital representation of a physical object, which they can use for a number of purposes, including monitoring the asset’s health status, movements, location and more.

The concept behind these “digital twins” was first presented in 2002 during a University of Michigan presentation by Dr. Michael Grieves, who posited that there are two systems: one physical, the other a digital representation that contained all of the information about the physical system. His thought was that the digital twin could be used to monitor and support the entire life cycle of its physical sibling and, in the process, keep that sibling functioning and healthy.

Digitizing a vehicle

Consider a military vehicle that has just rolled off the assembly line and is ready to be commissioned. This hardened vehicle is an expensive yet essential piece of battlefield equipment that is expected to run efficiently and dependably for many years.

Getting the most out of this asset requires consistent maintenance. Ideally, that maintenance can be performed proactively to prevent any potential breakdowns, which can incur additional—and potentially high—costs, not to mention putting warfighters in danger with a mechanical failure at an inopportune time. Preventative maintenance can extend the life cycle of the vehicle, save tax dollars and keep missions moving forward. It can be difficult to know or keep track of when the vehicle may need maintenance, and impossible to predict when a breakdown may occur.

Fortunately, the data being collected by the various sensors contained within the vehicle can be used to create a digital twin. This representation can provide a very clear picture, in real time, of its status. For example, the digital twin will be able to show how the engine is performing, or whether the vehicle’s temperature gauges are off, or even if the vehicle’s tire pressure is low.

Further, by collecting this information over time, the digital twin has the ability to create an evolving yet extraordinarily accurate picture of how the vehicle will perform in the future. This is derived from a combination of machine learning and predictive analysis based on historical data gathered from the sensors and past performance. As the sensors continue to report information, the digital twin will continue to learn, model and adapt its prediction of future performance.

This information can help teams in a number of ways. The analytics derived from historical performance data can be used to point to potential warning signs and predict failures before they occur, thereby avoiding unwanted downtime. Data can also be used to diagnose a problem and even, in some cases, solve the issue remotely. At the least, digital twins can be used to help guide soldiers and repair specialists to quickly fix the problem on the ground.

The life-cycle management process also becomes much more efficient. Digital twins can help simplify and accelerate management of a particular thing, in this case, a physical entity like a vehicle. Digital representations and data can be easily parsed and analyzed—and assets controlled and managed—by the IT professionals at hand, without the need for embedded programmers and specialized communications protocols.

Monitoring performance

Data is the vital resource that powers a digital twin, and many of the tools that agencies already have on hand can be used to analyze this information. For example, monitoring tools can collect and analyze sensor data in real time. That information can be used to optimize current asset performance levels, but also to predict future performance, enhancing maintenance operations.

Of course, the benefits of continuous monitoring go beyond asset maintenance and lead straight to the heart of network optimization. Monitoring can be used effectively for improving network capacity planning, optimizing resource usage and assisting in other areas in which connected devices can impact military networks.

Taking the next step

The digital twin concept is a logical next step for defense agencies that have already begun investing in software-defined services. These services were created to simplify and accelerate the management of core technology concepts, including computing, storage and networking. The idea was to improve the management of each of these concepts throughout their life cycles, from planning and design through production, deployment, maintenance and, finally, retirement.

Digital twins take this mentality a step further by applying it to physical objects. It’s a logical evolution for the military’s ever-growing web of connectivity. Digital twins, and the data analysis they depend on, can open the doors to more efficient and effective asset lifecycle management.

Joe Kim is the executive vice president for engineering and the global chief technology officer, SolarWinds.