Smart system for rapid and accurate aircraft maintenance decision making
AssigneeThe Boeing Company
InventorsGary E. Georgeson, Gregory J. Sweers
Life of this patent
Estimating the term…
Abstract
Certain aspects of the present disclosure provide techniques for a method, including: receiving multi-dimensional event data associated with a vehicle event; determining, based on the multi-dimensional event data, an inspection classification for the vehicle event; receiving multi-dimensional analysis data associated with the inspection classification for the vehicle event; determining, based on the multi-dimensional analysis data, a repair classification for the vehicle event; receiving multi-dimensional action data associated with the repair classification for the vehicle event; and determining, based on the multi-dimensional action data, a monitoring classification for the vehicle event.
Claims20
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.
A processing system, comprising:
- a memory comprising computer-executable instructions;
- and
- one or more processors configured to execute the computer-executable instructions and cause the processing system to:
- receive multi-dimensional event data associated with an aircraft event;
- train 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;
- determine, based on applying the first machine learning model to the multi-dimensional event data, an inspection classification in response to the aircraft event;
- receive multi-dimensional analysis data associated with the inspection classification in response to the aircraft event;
- determine, based on applying a second machine learning model to the multi-dimensional analysis data, a repair classification in response to the aircraft event;
- receive multi-dimensional action data associated with the repair classification in response to the aircraft event;
- determine, based on applying a third machine learning model to the multi-dimensional action data, a monitoring classification in response to the aircraft event;
- receive feedback data associated with the inspection classification;
- modify the set of factors based on the feedback data;
- and
- retrain the first machine learning model based on the modified set of factors.
A non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform a method, the method comprising:
- 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.
Dependencies are inferred from the claim text.