Aircraft manufacturers are exploiting the opportunities that come with collecting the vast amount of data available, from customer reports to engine exhaust temperatures. Why is it potentially so useful? What are some of the best ways to use it?

The hype phrase today is “Big Data.” It is a phrase Mark Bünger, Research Director for Lux Research, believes can have a number of definitions depending on who is doing the defining. Bünger was the lead analyst on a report that Lux published in April 2015 that surveyed both the technology as it is today and how various industries are adapting to the opportunities—and perils—in trying to exploit it.

Avoiding a precise definition in an interview with Aerospace & Defense Technology, Bünger explained Big Data is about raw data, databases, and speed. Companies now have access to a growing number of sensors attached to infrastructure and assets, from oil derricks to aircraft engines. These sensors provide vast volumes of data in high velocity. The data is variable—think spreadsheets to Twitter tweets. A major finding of its study is that, while information industries like banking and media know how to derive benefit from vast data sets, there is risk for industries that Lux terms “material-centric.”

There is one material-centric industry that is gaining advantage in collecting, storing, and using data vast enough to qualify as Big Data. “The aircraft industry is pretty far along and other industries should follow its lead,” he said.

This is especially true for engines over airframes, according to Bünger. “The [operating] conditions in engines are dynamic, with thousands of parameters, sometimes every millisecond. With airframes, the data rate is lower because data is only interesting during takeoff, some during flight, and then landing.”

Mark Bunger from Lux Research notes that while the Velocity, Volume, and Variety definition of Big Data works for companies in IT, finance, and media, a better practical concept for industries like aircraft manufacturing and maintenance is around operational realities.
He also noted that it’s not just the data for engines during flight that is worth tracking. Tools and parts in support of maintenance and repair operations are also useful. These require more advanced data capturing techniques, since the information is less structured even while using them efficiently is important. All of it becomes useful to the right company that knows how to use it.

“Big Data is a technology that is going to be very impactful for us,” said Larry Volz, Chief Information Officer and Vice President for Pratt & Whitney. He too was wary about putting a precise definition on the term. ”It is more about using the information we already have from our products and processes in a different way, moving from a descriptive analytical look at current information into being more predictive.”

Predictive analytics—predicting future behavior and actions—is really what it is all about, according to Volz.

Moving From Reacting to Predicting

The key to predictive analytics is modeling all of that data statistically to gain new insights. The challenge and opportunity is in the nature of the data. The data store available to Pratt & Whitney today is indeed both vast and varied.

Pratt & Whitney plans to capture 50 times more data on its newest engines, such as the geared turbofan PurePower PW1100G-JM engine shown here, compared to previous models.
“We take data during manufacturing from our ERP/SAP system, we collect data during the build and overhaul operations, from customer service, our global field representatives, and warranty reporting system. We combine that with data from our on-wing engine health monitoring system, the Advanced Diagnostics & Engine Management, or ADEM,” Volz said.

He also stressed that the company has all along been working with large data sets, such as simulation data used by design engineers to predict engine power output, NVH, and fuel burn. Now, by applying statistical models, they extend predictive capabilities to in-service use. He relates that they accurately predict unplanned engine events that could cause a delay or interruption in a flight.