US 11,907,913 B2
Maintaining an aircraft with automated acquisition of replacement aircraft parts
Rahul C. Thakkar, Leesburg, VA (US); Leontios Christodoulou, Alexandria, VA (US); and Surya Pandrangi, Sammamish, WA (US)
Assigned to The Boeing Company, Chicago, IL (US)
Filed by The Boeing Company, Chicago, IL (US)
Filed on Jul. 15, 2021, as Appl. No. 17/376,805.
Claims priority of provisional application 63/065,065, filed on Aug. 13, 2020.
Prior Publication US 2022/0051198 A1, Feb. 17, 2022
Int. Cl. G06Q 10/20 (2023.01); G06Q 10/087 (2023.01); G06Q 10/0631 (2023.01); G06Q 10/10 (2023.01); G06N 20/20 (2019.01); G06F 16/23 (2019.01); G07C 5/08 (2006.01); B64F 5/40 (2017.01); G06Q 30/0601 (2023.01); G07C 5/00 (2006.01)
CPC G06Q 10/20 (2013.01) [B64F 5/40 (2017.01); G06F 16/2379 (2019.01); G06N 20/20 (2019.01); G06Q 10/06315 (2013.01); G06Q 10/087 (2013.01); G06Q 10/10 (2013.01); G06Q 30/0633 (2013.01); G07C 5/085 (2013.01); G07C 5/008 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for maintaining an aircraft from a plurality of aircraft of a type of aircraft that includes a plurality of aircraft parts, the system comprising:
at least one source of data including at least one series of observations related to the aircraft; and
an electronic device in communication with the at least one source of data, the electronic device including:
a memory configured to store computer-readable program code; and
processing circuitry configured to access the memory, and execute the computer-readable program code to cause the electronic device to at least:
access a first time series of observations of first variables that describe in-service operation of the aircraft, including for a plurality of flights of the aircraft, flight plans, and geographic locations, operational context and status of the aircraft;
access a second time series of observations of second variables that describe maintenance of the aircraft, including a maintenance history of the aircraft, historical orders of replacement aircraft parts for respective aircraft parts of the plurality of aircraft parts, and historical time to replace the respective aircraft parts with the replacement aircraft parts;
access a third time series of observations of third variables that describe at least one of weather or terrain in an environment of the aircraft during the in-service operation and the maintenance of the aircraft;
determine a behavior model of the type of aircraft that is trained to predict demand for the replacement aircraft parts from a training set of observations of the first variables, the second variables and the third variables across the plurality of aircraft of the type of aircraft, the behavior model implemented as a directed acyclic graph of machine learning models that are connected to one another, an output of a first of the machine learning models fed as input to a second of the machine learning models;
apply the first time series, the second time series and, the third time series to the behavior model to predict the demand for the replacement aircraft parts;
create a plan for acquisition of the replacement aircraft parts, and maintenance of the aircraft in which the respective aircraft parts are replaced with the replacement aircraft parts, based on the demand as predicted; and
execute the plan to maintain the aircraft.