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Boeing wants to know if its planes are getting a little too rickety.
The aerospace manufacturer is seeking to patent a system for "component fault prediction." Essentially, this system uses a prediction model to detect the likelihood that a vehicle component needs to be replaced, providing a recommendation for "addressing the predicted removal" of bad components.
This system collects tons of sensor data created by an aircraft during flight, including speed, temperature, and altitude. The model then weeds out data that is highly correlated -- the things that are obvious -- such as temperature and humidity, combining those pairs into individual data points to feed the system only non-correlated data.
What's particularly interesting is the way the system arrives at its answers. Boeing's system reduces the amount of data needed to make accurate predictions, allowing it to rapidly make decisions with reduced computational power. This is because, for conventional analysis, the number of data points to monitor can "easily reach into the hundreds of thousands," Boeing noted.
Once the model decides that a part needs to be replaced, the patent notes a couple of options to amend the situation. For one, it could send that prediction to a "maintenance/parts inventory system," which in turn instructs autonomous robots or systems to do the replacement work, reducing the time and manpower needed for upkeep.
If this prediction happens mid-flight, the system may reroute the aircraft to the closest airport that has that component.
Though the patent focuses specifically on component replacement, the prediction model itself could actually be applied in a number of different scenarios, said Rhonda Dibachi, CEO of Manufacturing-as-a-Service company HeyScottie. The model, she said, is basically just a way to reduce the amount of data needed to get to the same answer.
"The patent itself is for the algorithm for reducing a huge dataset into an actionable one," Dibachi said. "I'm surprised it's as narrow as it is in its application, because this algorithm can be applied to anything where you have a huge, unruly set of data."
While the scope of Boeing's application is quite narrow, the aerospace manufacturer may not be the only person to have figured out this method, Dibachi noted. Several academic papers have been written on the topic, specifically seeking to efficiently predict breakdowns using non-correlated data. Boeing's confined approach may make it more likely to actually secure the patent.
Whether or not Boeing secures this patent, this tech could stand to save the company tons of time and resources, Dibachi said. The patent states that this tech has the potential to arrive at its predictions 10 times faster than any other kind of sorting algorithm, potentially allowing for corrective action to be taken preventatively, not reactively. Plus, this tech doesn't require industry knowledge or a specialist to interpret the data, she said.
The cons are similar to that of any AI model, Dibachi said: AI is data-driven, and if the data spells out something that isn't necessarily true, it can lead to false positives and false negatives, she said.
"You don't know if it's making a totally random prediction on something where the cards just happen to fall correctly," Dibachi said. "Correlation is not causation, right?"
And given that Boeing is one of the biggest providers of commercial jets in the world, implementing this tech comes with an added layer of liability, and additional responsibility in getting it right -- or at least having proper fail-safes in place.
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