At GE Aviation, Digital Transformation Hinges on ‘Digital Twins’

Industrial manufacturers turn to digital models of physical equipment to make products faster, cheaper and easier to maintain.

Physically inspecting a jet engine to ensure that it’s operating at peak performance is a necessary but costly task for America’s airlines. The average airline pays hundreds of millions of dollars for each of its aircraft; grounding a plane for an inspection means it isn’t available for passengers and it isn’t making money for the airline.

But what if the airline had a virtual model of the plane’s engine—a model so accurate that assessing the engine’s health digitally would be exactly like examining the actual engine in person?

Enter the “digital twin,” an analytical representation of any physical product, process or system that a company wants to model.

NASA pioneered digital twins in the early days of space travel to monitor spacecraft when they were too remote for physical inspection. Today, the technology is rapidly growing in popularity at major companies like GE Aviation, Siemens, SAP and Fiat Chrysler, among many others.

Physical Inspections Go Digital

At GE Aviation, digital twins have empowered the 125-year-old manufacturer to create computer models of its jet engines so precise that the Federal Aviation Administration now allows the company to use digital analytical techniques to comply with regulatory requirements, eliminating the need for—and cost of—physical inspections.

“For us, it started with our engines as we began our digital transformation,” says Darin DiTommaso, VP of Digital Solutions and Services for GE Aviation. “How can we apply big data and analytics to our engines? We started creating these analytical models with time-series data as the engine is flown, from startup to shutdown.”

Engines manufactured by GE Aviation come equipped with numerous sensors. By capturing performance data, GE Aviation can tell how each of its engines is operating from thermodynamic and mechanical perspectives. Then the company takes that information and creates an analytical model of the engine: the digital twin.

“You can model things like the overall health of the engine down to the part level. It gives you the opportunity to do an engine-specific analytical assessment of its health,” DiTommaso says.

The Power of Predictive Analytics

With digital twins, companies are seeing both internal and external benefits. Thanks to precise analytic modeling, predictive maintenance is replacing unscheduled maintenance. That means airlines have more planes in the sky more of the time—in other words, more money made and saved. It also means fewer airport delays for passengers, resulting in happier fliers.

“An unscheduled event is a really big deal. It throws a monkey wrench into the whole operation,” says DiTommaso. “But if [the airline] knows a problem is coming and they have 30 days’ notice, they can schedule maintenance.”

Using digital twins has become a key component of GE Aviation’s ongoing digital transformation. These analytical models completely change the value proposition that GE brings to an airline customer. No longer is the company selling only a jet engine; it's also providing clients with the insights gleaned from the data it collects and analyzes.

“We feel that in the industrial world, there’s so much more to be gained beyond just selling a piece of equipment,” DiTommaso says. “If you can also couple that with data and analytics associated with products in the field, that's just a big plus for us.”

 
Images courtesy of GE Aviation
Andrew Zaleski
By Andrew Zaleski | December 8, 2017

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