How artificial intelligence could lead to self-healing airplanes

How artificial intelligence could lead to self-healing airplanes

A new partnership between aviation giant Boeing and Carnegie Mellon University hints at the power of fields such as artificial intelligence and big data to transform huge, multi-billion-dollar industries. As part of a three-year, $7.5 million deal that will establish a new Aerospace Data Analytics Lab, Boeing and the Carnegie Mellon School of Computer Science will work on a range of new projects that will apply the principles of AI and big data to improving the quality of Boeing’s aerospace activities.

The goal of the new partnership, first and foremost is to make sense of the burgeoning amount of data in the aerospace industry. By applying principles of machine learning, it might be possible to optimize many aspects of Boeing’s operations — including those related to design, construction and operation – and turn ordinary data into real-world insights.

According to Jaime Carbonell, the Carnegie Mellon professor and Director of the Language Technologies Institute, who will head up the new Aerospace Data Analytics Lab, ”The mass of data generated daily by the aerospace industry overwhelms human understanding, but recent advances in language technologies and machine learning give us every reason to expect that we can gain useful insights from that data.“

One example of how machine learning can be used to gain useful insights is the whole issue of airline maintenance. Think of airline maintenance the same way you think of maintenance for our car – you can follow the generally suggested guidelines for your vehicle (e.g. an oil change every 3,000 miles) – or you can use real-time data to see which planes needs fixing, when. By fixing planes before – not after – they need maintenance, an airline flying Boeing planes could gain a real competitive advantage over its peers.

“A Boeing aircraft such as the 787 Dreamliner combines thousands of on-board sensors, text from pilots and mechanics, structured engineering data bases, across the entire fleet collected from each of the client airlines,” Carbonell told me. “This provides a golden opportunity, merging CMU’s capabilities and Boeing data to address problems such as predictive analysis for preventive maintenance — rather than after-the-plane-is-grounded maintenance.”

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