Air Force Taps Machine Learning to Speed Up Flight Certifications

A F-16 flies during a mission at Eglin Air Force Base, Florida, Feb. 14, 2019.

A F-16 flies during a mission at Eglin Air Force Base, Florida, Feb. 14, 2019. John Raven/Air Force

The Air Force SEEK EAGLE Office cleaned up its “data swamp” and implemented a machine learning application, and it's making the flight certification process more efficient.

Machine learning is transforming the way an Air Force office analyzes and certifies new flight configurations. 

The Air Force SEEK EAGLE Office sets standards for safe flight configurations by testing and looking at historical data to see how different stores—like a weapon system attached to an F-16—affect flight. A project AFSEO developed along with industry partners can now automate up to 80% of requests for analysis, according to the office’s Chief Data Officer Donna Cotton. 

“The application is kind of like an eager junior engineer consulting a senior engineer,” Cotton said. “It makes the straightforward calls without any input, but in the hard cases it walks into the senior engineer’s office and says: ‘Hey, I did a bunch of research and this is what I found out. Can you give me your opinion?’”

Cotton spoke at a Tuesday webinar hosted by Tamr, one of the industry partners involved in the project. Tamr announced July 30 AFSEO awarded the company a $60 million contract for its machine learning application. Two other companies, Dell and Cloudera, helped AFSEO take decades of historical data from simulations, performance studies and the like that were siloed across various specialities and organize them into a searchable data lake. 

On top of this new data architecture, the machine learning application provided by Tamr searches through all the historical data to find past records that can help answer new safety recommendation requests automatically. 

This tool is critical because the vast majority of AFSEO’s flight certification recommendations are made by analogy, meaning using previous data rather than new flight tests. But in the past, data was disorganized and lacked unification. This made tracking down these helpful records a challenge for engineers.  

Now, a cleaner AFSEO data lake cuts the amount of time engineers waste on looking for the information they need. Machine learning further speeds up the process by generating safety reports automatically while still keeping the professional engineers in the loop. Even when engineers need to produce original research, the machine learning application can smooth the process by collecting related records to serve as a jumping off point.

The new process helps AFSEO avoid doing costly flight tests while also increasing confidence that the team is making the safety certification correctly with all the information available to them, Cotton said.

“We are able to be more productive,” Cotton said. “It's saving us a lot of money because for us, it's not about profit, but it's about hours. It's about how much effort are we going to have to use to solve or to answer a new request.”