An architecture for monitoring at least one aircraft. The architecture comprises an avionics system configured to generate avionics data during use of the aircraft; a mobile electronic device including an analysis unit configured to convert at least one maintenance operation into operational data; and an alerter configured to display at least one item of monitoring information; and a cloud computing infrastructure. The analysis unit and the alerter are configured to implement a local operating mode and an operating mode connected to the cloud computing infrastructure.

This disclosure describes at least embodiments of an aircraft monitoring system for an electric or hybrid airplane. The aircraft monitoring system can be constructed to enable the electric or hybrid aircraft to pass certification requirements relating to a safety risk analysis. The aircraft monitoring system can have different subsystems for monitoring and alerting of failures of a component, such as a battery pack, a motor controller, and/or a motors. The failures that pose a greater safety risk may be monitored and indicated by one or more subsystems without use of programmable components.

Methods and systems for depicting avionics data anomalies in an aircraft. Time series data is received from the avionics data source, a future time is predicted when a first anomaly threshold will be crossed based on the time series data, and the future time when the first anomaly threshold will be crossed is depicted on a display device associated with the aircraft.

A method is provided for diagnosing a failure on an aircraft that includes aircraft systems and monitors configured to report effects of failure modes of the aircraft systems. The method includes receiving a fault report that indicates one or more of the monitors that reported the effects of a failure mode in an aircraft system of the aircraft systems, and accessing a fault pattern library that describes relationships between possible failure modes and patterns of those of the monitors configured to report the effects of the possible failure modes. The method also includes diagnosing the failure mode of the aircraft system from the one or more of the monitors that reported, and using the fault pattern library and a greedy selection algorithm, determining a maintenance action for the failure mode; and generating a maintenance message including at least the maintenance action.

A device for monitoring a state of a propulsion engine includes an acquisition module that acquires data of flights of the propulsion engine, comprising, for each flight, values of input variables, environment variables, and output variables of the propulsion engine during the flight, a learning module that computes, by learning from the data of each flight, an individual flight model for the flight, a using module that computes, for each flight, estimates of the values of the output variables, by applying the individual flight model to reference values of the input variables and the environment variables, and an error associated with the estimates of the values of the output variables that is obtained by applying the individual flight model to the reference values of the input variables and the environment variables. The reference values belong to a set of reference data, which are identical for the individual flight models.

A system predicting reliability for an engine of an aircraft includes an electronic engine controller, command input mechanisms in a cockpit of the aircraft, manual or automatic, a plurality of line replaceable units controlling functions for the aircraft engine and a computing and reporting system remote from the aircraft. The command input mechanisms transmit electronic command signals to the electronic engine controller, the electronic engine controller signals appropriate ones of the line replaceable units to carry out the commands, the line replaceable units perform the commanded functions and feed back to the electronic engine controller a record of functions actually performed and timing of the performance, and wherein at the end of each flight the records of actual performance are communicated to a data repository remote from the aircraft, associated with a specific aircraft and a specific engine.

The non-limiting technology described herein is a failure managing framework for complex systems that determines and restores functionality of failing systems and sub-systems using a function-based intervention approach having ontological content such as provided in a System State Graph directed graph. An integration framework allows integration of multiple intervention definition paradigms and selects the best for the current scenario; modifies procedures according to current context by encapsulating operator's tacit knowledge; provides an additional safety net during application of intervention and allows both autonomous operations and assistance to a human operator in the loop.

The present disclosure provides an aircraft engine graphical diagnostic tool, as well as a method and electronic device for operating the same. The graphical diagnostic tool comprises an input element configured for obtaining a data value for a first data dimension, and a visualization element having at least two dimensions. The visualization element is configured for presenting a dataset for at least second and third data dimensions associated with the first data dimension. The dataset presented by the visualization element is selected based on the data value for the first data dimension.

A method for designing a material for an aircraft component includes training a neural network to correlate microstructural features of an alloy with material properties of the alloy by at least providing a set of images of the alloy to the neural network. Each of the images in the set of images has varied constituent compositions. The method further includes providing the neural network with a set of determined material properties corresponding to each image, associating the microstructural features of each image with the set of empirically determined data corresponding to the image, and determining non-linear relationships between the microstructural features and corresponding empirically determined material properties via a machine learning algorithm, receiving a set of desired material properties of the alloy for aircraft component, and determining a set of microstructural features capable of achieving the desired material properties of the alloy based on the determined non-linear relationships.

A system for flight control for managing actuators for an electric aircraft is provided. The system includes a controller, wherein the controller is designed and configured to receive a sensor datum from at least a sensor, generate an actuator performance model as a function of the sensor datum, identify a defunct actuator of the electric aircraft as a function of the sensor datum and the actuator performance model, generate an actuator allocation command datum as a function of at least the actuator performance model and at least the identification of the defunct actuator, and perform a torque allocation as a function of the actuator allocation command datum.