Abstract: A system to predict a startup condition of an auxiliary power unit (APU) of an aircraft includes a machine learning device configured to receive data including sensor data of the aircraft and weather forecast data of a destination airport. The machine learning device is also configured to process the data to generate a prediction regarding the startup condition and to generate a message based on the prediction. The message indicates that an alternate startup procedure of the APU is to be performed after the aircraft has landed at the destination airport to avoid an error condition associated with a primary startup procedure of the APU.

Abstract: A system includes a machine-learning device configured to receive data associated with an aircraft. The data includes a flying time associated with one or more flight phases of travel to a destination airport, temperature data associated with one or more of the flight phases, and a number of occupants on board the aircraft. The machine-learning device is configured to process the data to generate prediction data regarding usage of an auxiliary power unit of the aircraft and to generate a message based on the prediction data. The message indicates at least one of an estimated number of on/off events of the auxiliary power unit or an estimated duration of use of the auxiliary power unit.

Abstract: A method includes receiving a component history for a processor module, the component history including identities of host vehicles in a set of host vehicles in which the processor module was installed; receiving an operational history for the set of host vehicles for a time period the processor module was installed on the set of host vehicles; receiving indirect sensor measurements related to the set of host vehicles for the time period; receiving a part survival model that is based at least in part on a part status of a plurality of historical processor modules, the plurality of historical processor modules having a historical component history, a historical operational history, and historical indirect sensor measurements; and determining a survival probability of the processor module based at least in part on the component history, the operational history, the indirect sensor measurements, and the part survival model.

Abstract: A diagnostic system for a closed fluid system is provided. The closed fluid system includes a first fluid subsystem and a second fluid subsystem connected using a valve or valve/actuator. The diagnostic system includes a first sensor disposed in the first fluid subsystem, a second sensor disposed in the second fluid subsystem and a computer. The computer receives first time-ordered fluid level measurements and second time-ordered fluid level measurements from respectively the first sensor and the second sensor and performs a comparison of first fluid level of the first fluid subsystem and second fluid level of the second fluid subsystem. The computer produces a notification indicating a predicted transfer of hydraulic fluid when the first time-ordered fluid level measurements and the second time-ordered fluid level measurements have at least a threshold divergence and outputs the notification for display to a user.

Abstract: A maintenance prediction system for generating one or more recommended actions for an individual to perform upon a vehicle is disclosed, and includes a camera configured to capture image data of one or more components of interest and image data of one or more body size parameters of the individual. The maintenance prediction system also includes one or more assembly databases storing three-dimensional model data representing components of the vehicle, one or more processors in electronic communication with the camera and the one or more assembly databases, and a memory coupled to the one or more processors. The maintenance prediction system generates one or more notifications indicating the recommended actions for the individual to perform upon the vehicle.

Abstract: A system and method for detecting faulty engine anti-ice sensor is disclosed and may include obtaining first pressure data representing a first pressure over a period of time at a first engine anti-ice pressure sensor on an aircraft engine. The method may further include obtaining second pressure data representing a second pressure over the period of time at a second engine anti-ice pressure sensor on the aircraft engine. The method may also include generating a variance value based at least partially on a variance of a difference between the first pressure data and the second pressure data. The method may include providing an indication that liquid is within the first engine anti-ice pressure sensor or the second engine anti-ice pressure sensor when the variance value exceeds a threshold.

Abstract: A method includes obtaining an initial set of fastener parameter values for a fastener, executing a neural network model using features extracted at least from the initial set of fastener parameter values to query a fastener description repository, obtaining, from the fastener description repository, a set of possible matching fasteners, and presenting a matching fastener when the matching fastener is in the set of possible matching fasteners.

Abstract: Disclosed herein is a method that comprises determining one or more structural constraints for a structural design of a flexure mechanism. The method also comprises optimizing a level-set formula describing the structural design of the flexure mechanism subject to the one or more structural constraints for manufacturing the structural design of the flexure mechanism. The method further comprises outputting the structural design of the flexure mechanism based on the optimized level-set formula for manufacturing.

Abstract: A system includes a machine-learning device configured to receive data associated with an aircraft. The data includes a flying time associated with one or more flight phases of travel to a destination airport, temperature data associated with one or more of the flight phases, and a number of occupants on board the aircraft. The machine-learning device is configured to process the data to generate prediction data regarding usage of an auxiliary power unit of the aircraft and to generate a message based on the prediction data. The message indicates at least one of an estimated number of on/off events of the auxiliary power unit or an estimated duration of use of the auxiliary power unit.

Abstract: A maintenance prediction system for generating one or more recommended actions for an individual to perform upon a vehicle is disclosed, and includes a camera configured to capture image data of one or more components of interest and image data of one or more body size parameters of the individual. The maintenance prediction system also includes one or more assembly databases storing three-dimensional model data representing components of the vehicle, one or more processors in electronic communication with the camera and the one or more assembly databases, and a memory coupled to the one or more processors. The maintenance prediction system generates one or more notifications indicating the recommended actions for the individual to perform upon the vehicle.

Abstract: Disclosed herein is a method that comprises detecting that a rate of decrease in fuel quantity for an aircraft exceeds a previously-determined baseline rate of decrease in fuel quantity for the aircraft by a threshold rate during a corresponding segment of flight of the aircraft. The method also comprises determining that a fuel jettison setting for the aircraft is enabled in response to the detected increase in the rate of decrease in fuel quantity. The method further comprises determining that an angle of one or more flaps of the aircraft satisfies a threshold angle. The method additionally comprises generating an alert indicating possible fuel contamination of the one or more flaps due to jettisoned fuel.

Abstract: A method includes receiving a component history for a processor module, the component history including identities of host vehicles in a set of host vehicles in which the processor module was installed; receiving an operational history for the set of host vehicles for a time period the processor module was installed on the set of host vehicles; receiving indirect sensor measurements related to the set of host vehicles for the time period; receiving a part survival model that is based at least in part on a part status of a plurality of historical processor modules, the plurality of historical processor modules having a historical component history, a historical operational history, and historical indirect sensor measurements; and determining a survival probability of the processor module based at least in part on the component history, the operational history, the indirect sensor measurements, and the part survival model.

Abstract: Landing gear monitoring systems and methods for an aircraft include a landing gear timing analysis control unit that is configured to analyze one or both of landing gear motion of one or more landing gears of the aircraft or door motion of one or more doors proximate to the landing gear(s) to determine an operational status of one or both of the landing gear(s) or the door(s).

Abstract: A method comprises mapping a plurality of variable values of a database in a multi-dimensional search space, each dimension corresponding to a variable of the database. The method comprises defining a surface within the multi-dimensional search space based on a plurality of search points defined by values for at least a pair of variables, the surface comprising a closed-loop, convex shape that defines an area of the multi-dimensional search space that has been searched. The method comprises repositioning at least one search point of the plurality of search points within the multi-dimensional search space based on a target objective, the at least one search point of the plurality of search points moved a predefined distance in a direction of the target objective such that the shape of the surface is changed while maintaining a closed-loop, convex shape. The method comprises reporting the variable values for the plurality of search points.

Abstract: A method including receiving a joint parameter for a joint between at least two composite structural components, a ply stackup for each of the at least two composite structural components and an electromagnetic effect parameter in a controller, iteratively processing with the controller, the joint parameter and the ply stackup and outputting an optimized joint parameter based on at least a convergence of an electromagnetic effect resistance of the joint and the at least two composite structural components, and changing, with the controller, a value of one or more of the joint parameters and the ply stackup during the iterative processing to reach the convergence of the electromagnetic effect resistance.

Abstract: A system to predict a startup condition of an auxiliary power unit (APU) of an aircraft includes a machine learning device configured to receive data including sensor data of the aircraft and weather forecast data of a destination airport. The machine learning device is also configured to process the data to generate a prediction regarding the startup condition and to generate a message based on the prediction. The message indicates that an alternate startup procedure of the APU is to be performed after the aircraft has landed at the destination airport to avoid an error condition associated with a primary startup procedure of the APU.

Abstract: A system and method for detecting faulty engine anti-ice sensor is disclosed and may include obtaining first pressure data representing a first pressure over a period of time at a first engine anti-ice pressure sensor on an aircraft engine. The method may further include obtaining second pressure data representing a second pressure over the period of time at a second engine anti-ice pressure sensor on the aircraft engine. The method may also include generating a variance value based at least partially on a variance of a difference between the first pressure data and the second pressure data. The method may include providing an indication that liquid is within the first engine anti-ice pressure sensor or the second engine anti-ice pressure sensor when the variance value exceeds a threshold.

Abstract: An aircraft landing weight determination system and method are configured to determine an accurate gross weight of an aircraft. The aircraft landing weight determination system and method include a gross weight determination control unit that is configured to determine an accurate gross weight of an aircraft based on a first source of gross weight data of the aircraft and a second source of gross weight data of the aircraft.

Abstract: Disclosed herein is a method that comprises detecting that a rate of decrease in fuel quantity for an aircraft exceeds a previously-determined baseline rate of decrease in fuel quantity for the aircraft by a threshold rate during a corresponding segment of flight of the aircraft. The method also comprises determining that a fuel jettison setting for the aircraft is enabled in response to the detected increase in the rate of decrease in fuel quantity. The method further comprises determining that an angle of one or more flaps of the aircraft satisfies a threshold angle. The method additionally comprises generating an alert indicating possible fuel contamination of the one or more flaps due to jettisoned fuel.

Abstract: Landing gear monitoring systems and methods for an aircraft include a landing gear timing analysis control unit that is configured to analyze one or both of landing gear motion of one or more landing gears of the aircraft or door motion of one or more doors proximate to the landing gear(s) to determine an operational status of one or both of the landing gear(s) or the door(s).