US 12,260,683 B2
Smart system for rapid and accurate aircraft maintenance decision making
Gary E. Georgeson, Tacoma, WA (US); and Gregory J. Sweers, Renton, WA (US)
Assigned to The Boeing Company, Chicago, IL (US)
Filed by THE BOEING COMPANY, Chicago, IL (US)
Filed on May 31, 2022, as Appl. No. 17/829,123.
Claims priority of provisional application 63/211,520, filed on Jun. 16, 2021.
Prior Publication US 2022/0406098 A1, Dec. 22, 2022
Int. Cl. G07C 5/00 (2006.01); G06N 20/00 (2019.01); G07C 5/06 (2006.01); G07C 5/08 (2006.01)
CPC G07C 5/006 (2013.01) [G07C 5/06 (2013.01); G07C 5/0808 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for performing machine learning, comprising:
receiving multi-dimensional event data associated with an aircraft event;
training a first machine learning model based on a set of factors, wherein each factor in the set of factors is associated with a dimension of the multi-dimensional event data;
determining, based on applying the first machine learning model to the multi-dimensional event data, an inspection classification in response to the aircraft event;
receiving multi-dimensional analysis data associated with the inspection classification in response to the aircraft event;
determining, based on applying a second machine learning model to the multi-dimensional analysis data, a repair classification in response to the aircraft event;
receiving multi-dimensional action data associated with the repair classification in response to the aircraft event;
determining, based on applying a third machine learning model to the multi-dimensional action data, a monitoring classification in response to the aircraft event;
receiving feedback data associated with the inspection classification;
modifying the set of factors based on the feedback data; and
retraining the first machine learning model based on the modified set of factors.