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US 12,749,034 B2Granted
Machine learning to predict part consumption using flight demographics
AssigneeThe Boeing Company
InventorsAndrea Sanzone, Millie Sterling, Rahul Ashok, Rowena Loh
Life of this patent
Estimating the term…
Abstract
The present disclosure provides for using machine learning to evaluate flight demographics and predict part consumption. Historical aircraft part consumption data indicating prior consumption of aircraft parts is accessed, and historical aircraft flight demographics associated with the historical aircraft part consumption data are determined. A machine learning model is trained based on the historical aircraft part consumption data and the historical aircraft flight demographics, and the machine learning model is deployed to predict future aircraft part consumption.
Claims18
A computer program product, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation, the operation comprising:
- forecasting, for at least a first aircraft type of a plurality of aircraft types, a number of aircraft of the first aircraft type that will be in service during a future window of time;
- forecasting future flight demographics during the future window of time based at least in part on the forecasted number of aircraft;
- determining an aircraft configuration for the first aircraft type, the aircraft configuration indicating a number of parts of a first part type on each aircraft of the first aircraft type;
- generating predicted part consumption of the first part type during the future window of time by processing the future flight demographics and the aircraft configuration using a machine learning model, wherein the machine learning model is a neural network, and refining the machine learning model comprises using backpropagation to iteratively refine at least one of weights or biases of the neural network;
- wherein the predicted part consumption represents a predicted number of parts of the first part type that will be consumed during the future window of time by the aircraft of the first aircraft type;
- facilitating reconfiguration of a spare part system based on the predicted part consumption, wherein the one or more computer processors are configured to facilitate the reconfiguration of the spare part system by controlling a number of the first part type that is at least one of constructed, ordered, stored, or shipped based on the predicted number of parts of the first part type that will be consumed during the future window of time;
- monitoring actual part consumption of the first part type during the future window of time;
- and refining the machine learning model based on a comparison of the actual part consumption to the predicted part consumption.
5 more elements
A method, comprising:
- forecasting, for at least a first aircraft type of a plurality of aircraft types and via one or more processors, a number of aircraft of the first aircraft type that will be in service during a future window of time;
- forecasting, via the one or more processors, future flight demographics during the future window of time based at least in part on the forecasted number of aircraft;
- determining, via the one or more processors, an aircraft configuration for the first aircraft type, the aircraft configuration indicating a number of parts of a first part type on each aircraft of the first aircraft type;
- generating predicted part consumption of the first part type during the future window of time by processing the future flight demographics and the aircraft configuration using a machine learning model, wherein the machine learning model is a neural network, and refining the machine learning model comprises using backpropagation to iteratively refine at least one of weights or biases of the neural network;
- wherein the predicted part consumption represents a predicted number of parts of the first part type that will be consumed during the future window of time by the aircraft of the first aircraft type;
- facilitating reconfiguration of a spare part system, via the one or more processors, based on the predicted part consumption, wherein facilitating the reconfiguration of the spare part system comprises controlling a number of the first part type that is at least one of constructed, ordered,
- stored, or shipped based on the predicted number of parts of the first part type that will be consumed during the future window of time;
- monitoring actual part consumption of the first part type during the future window of time;
- refining the machine learning model based on a comparison of the actual part consumption to the predicted part consumption.
6 more elements
A system, comprising:
- one or more processors;
- and
- a memory storage device communicatively connected to the one or more processors and including program instructions, the one or more processors configured to execute the program instructions to:
- forecast a number of aircraft of a first aircraft type that will be in service during a future window of time;
- forecast future flight demographics during the future window of time based at least in part on the forecasted number of aircraft and future flight schedules for flights scheduled to occur within the future window of time;
- determine an aircraft configuration for the first aircraft type, the aircraft configuration indicating a number of parts of a first part type on each aircraft of the first aircraft type;
- generate predicted part consumption of the first part type during the future window of time by processing the future flight demographics and the aircraft configuration using a machine learning model, wherein the machine learning model is a neural network, and refining the machine learning model comprises using backpropagation to iteratively refine at least one of weights or biases of the neural network;
- wherein the predicted part consumption represents a predicted number of parts of the first part type that will be consumed during the future window of time by the aircraft of the first aircraft type;
- facilitate reconfiguration of a spare part system based on the predicted part consumption, wherein the one or more computer processors are configured to facilitate the reconfiguration of the spare part system by controlling a number of the first part type that is at least one of constructed, ordered, stored, or shipped based on the predicted number of parts of the first part type that will be consumed during the future window of time;
- monitor actual part consumption of the first part type during the future window of time;
- and refine the machine learning model based on a comparison of the actual part consumption to the predicted part consumption.
8 more elements
Dependencies are inferred from the claim text.