Honeywell International Inc.

Megan L Hawley

Raj Mohan Bharadwaj

Kwong Wing Au

Kyusung Kim

David Daniel Lilly

Paul Frederick Dietrich

Andrew Peter Vechart

Piyush Ranade

Abraham Reddy

Dinkar A Mylaraswamy

Craig L Schimmel

Timothy Griffith

Ananda Vel Murugan Chandra Mohan

Cavene Robinson

Craig Schimmel

A method in an aircraft of using prognostic indicators for aircraft maintenance includes retrieving aircraft health data for a plurality of aircraft components wherein the aircraft health data includes at least one of mechanical systems condition indicator (CI) data, vibration spectrum data, resampled time-domain (RTD) data, and RTD spectrum data. The method includes estimating component health status information for the plurality of aircraft components using a plurality of prognostic modules wherein each prognostic module is configured to generate health status information for at least one of the aircraft components, the health status information includes at least one of a current health indicator and a prognostic indicator. The method also includes storing the component health status information for the aircraft components in a database onboard the aircraft, and causing the display of the health status information for the specific component on an aircraft display.

System and method of using mechanical systems prognostic indicators for aircraft maintenance

Disclosed are methods, systems, and non-transitory computer-readable mediums for detecting and avoiding loss of separation between vehicles. A first method may include training a vehicle interaction machine learning model to predict future vehicle interactions based on identified vehicle interactions and an identified risk of encounter between two or more selected vehicles. A second method may include obtaining real-time data associated with a vehicle-of-interest; evaluating the real-time data associated with the vehicle-of-interest to form encounter models; monitoring the encounter models with a model access function of the vehicle interaction machine learning model to detect real-time anomalies; and in response to detecting a real-time anomaly, transmitting an alert. A third method may include obtaining trajectory information; analyzing the trajectory information to determine whether a trajectory is a new trajectory type or whether the trajectory is a member of a new interaction; updating training data for the vehicle interaction machine learning model.

Method and system for detecting and avoiding loss of separation between vehicles and updating the same

Methods and systems are provided for predictive maintenance of a vehicle component. One method involves mapping a current instance of a component of a vehicle to one of plurality of degradation groups of prior lifecycles for other instances of the component based on a relationship between performance measurement data for the current instance and historical performance measurement data associated with that respective degradation group, obtaining contextual data associated with operation of the vehicle, and determining a maintenance recommendation for the current instance of the component based on the contextual data using a predictive maintenance model associated with the mapped degradation group.

Maintenance recommendations using lifecycle clustering

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Roseville, MN, United States of America

Megan L Hawley