The Boeing Company

Alexander Stephen Burch

John M Boggio

Richard A Effler

Clinton E Thompson

John Boggio

Zoe A Klesmith

An apparatus for predicting structural degradation and performing maintenance induction for a plurality of in-service aircraft is provided. The apparatus identifies maintenance requirements for the plurality of in-service aircraft and receives fatigue metric values on historical usage and structural health condition of the plurality of in-service aircraft. The apparatus predicts levels of structural degradation to the plurality of in-service aircraft based on the fatigue metric values and assigns maintenance priorities to the plurality of in-service aircraft based on the levels of structural degradation. The apparatus generates an instruction to route an aircraft of the plurality of in-service aircraft to a maintenance facility for maintenance based on a maintenance priority of the maintenance priorities assigned to the aircraft.

Maintenance induction for aircraft

A computing device, method and computer program product are provided to generate synthetic images of abnormalities on the surface of an object, such as a vehicle. The synthetic images of abnormalities on the surface of an object may be utilized for training a machine learning algorithm to detect and/or classify abnormalities. In the context of a method, a respective abnormality is parametrically modeled by selecting one or more control points that satisfy parameters associated with the respective abnormality and generating a surface representative of the respective abnormality based on the one or more control points. The method also renders a synthetic image of at least a portion of the surface of the object having the respective abnormality as defined by the parametric modeling thereof. The rendering of the synthetic image includes rendering the synthetic image in accordance with a predefined lighting condition and from a predefined viewpoint.

Generation of synthetic images of abnormalities for training a machine learning algorithm

Software-based solutions may mitigate physical damage to multi-layer networks, such as neural networks having shortcut (residual) connections. An example includes: providing a multi-layer network comprising a plurality of nodes; for each of a plurality of training cases: determining a set of dropout nodes, based at least on a damage model having a probability of a node being selected for dropout that is based at least on a target operating environment of the multi-layer network, wherein the probability of a node being selected is spatially correlated; and training the multi-layer network with the determined set of dropout nodes disabled (with a different set of dropout nodes for different training cases). In some examples the damage model involves expected physical radiation damage to a computing device hosting the multi-layer network, such as on board an aircraft or an earth-orbiting satellite. Thus, multiple degrees of expected damage may be addressed.

Mitigating damage to multi-layer networks

Examples for relational time-series classification method for corrosion maintenance dispatch. A method may involve generating a self-organizing map for each parameter that corresponds to a group of aircraft. The self-organizing map organizes time-series data associated with a parameter into a two-dimensional representation. The method may further involve, for each map, identifying polar coordinates that represent a location of a particular node, in a grid of nodes within the self-organizing map, that is located closest to the time-series data organized in the two-dimensional representation. The method may then involve providing polar coordinates identified from each self-organizing map, along with indications of corrosion levels corresponding to the aircraft, as inputs into a classification model to train the classification model to determine trends between the parameters. The classification model is configured to subsequently use the one or more trends collectively to estimate a corrosion level for a particular aircraft.

Growing community of inventors

Berkeley, MO, United States of America

Alexander Stephen Burch