General Electric Company

United Technologies Corporation

Toyota Motor Engineering & Manufacturing North America, Inc.

University of Connecticut

Meggitt Aircraft Braking System Corporation

Raytheon Technologies Corporation

Meggitt Aircraft Braking Systems Corporation

Ravi Rajamani

Coy Bruce Wood

Rajendra K Agrawal

Bruce Gordon Norman

Alexander I Khibnik

William Donat

Allan J Volponi

William F Schneider

Jihua Wang

Shailesh N Joshi

Narendra Digamber Joshi

Harry Kirk Mathews, Jr

Nelson R Corby, Jr

Pierino Gianni Bonanni

Mark Erik Nelson

Karl Dean Minto

Leroy Omar Tomlinson

Jonathan Carl Thatcher

Michael Richard Durling

Krishna R Pattipati

Frederic Ghislain Pla

Muhamed K Farooq

Nicolas Wadih Chbat

Sal Albert Leone

George Charles Goodman

Ali M Bazzi

Kurt Burkhalter

Thomas Edward Wickert

John Harry Down

Douglas D Moseley

Steven Keith Handelsman

David Allen Flodman

Richard B Hook

Robert Scott Garry

Donald Mcmenemy

Howard A Winston

Robert N Tomastik

William Lee Barrow

Sorin Costiner

Anju Narendra

John Kaminski

Vijay Kumar Millikarjun Hanagandi

James David Rackmales

Benjamin E Bulkley

Sampath K Yerramalla

Robert T Dirgo

Thomas R Matthis

Ricardo A Benitez

Joshua H Adams

Julie A Ireland

Robert M Cohen

Peter E Chenard

Kyle J Hamblin

Frederick A Just-Agosto

David Serrano

Michel D Paquet

Baban V Baliga

Andrew Paul Gagnon

Thomas Gannon

Mark Stephen Lipton

Robert John Pearson

Vijay Kumar Mallikarjun Hanagandi

Todd Alan Ashley

Rejendra K Agrawal

Sampath K Yeramalla

A method, system, and non-transitory computer readable medium describing an autoencoder that creates a reduced feature space from healthy power electronics devices for training. Devices under test are then encoded and compared to the encoded features of the healthy devices to determine health of the other devices. Contextual information is used to build multiple models that compare power electronics devices from similarly operated vehicles with one another.

Autoencoder utilizing vehicle contextual information

A method of diagnosis and prognosis for various operative systems is provided so that maintenance can be efficiently and effectively scheduled and provided. The invention is applicable to a broad range of systems, but particularly to aircraft brake systems and tires. An aircraft brake may be modeled by attributing brake wear factor values to various parameters associated with the aircraft brake that impact wear. Over the course of time and during braking events, the brake wear factor values are summed and a running total of that sum is kept over the life of the brake. At a point in time when the sum of weighted factors exceeds a preset threshold, an indicia is generated that service is required.

Aircraft brake and tire condition diagnosis and prognosis

An example aircraft debris monitoring sensor assembly includes an aircraft conduit defining a hollow core passage extending axially from an inlet opening to an outlet opening. A sensor arrangement detects debris carried by a fluid within the hollow core passage.

Aircraft debris monitoring sensor assembly

An example method of partitioning turbomachine faults includes using a modeling computer to model a system, to establish a modeled gas path parameter, and to establish a modeled subsystem parameter. The method then determines a gas path condition representing a difference between an actual gas path parameter and the modeled gas path parameter. The method also determines a subsystem condition representing a difference between an actual subsystem parameter and the modeled subsystem parameter. The method diagnoses a sensor failure based on the gas path condition and the subsystem condition.

Partitioning of turbomachine faults

A method for operating a debris monitoring system comprises continuously sensing the passage of particulates through a gas turbine engine to produce a time-domain sensor signal. The time-domain sensor signal is Fourier transformed to produce a frequency domain sensor signal. The frequency domain sensor signal is partitioned into bins corresponding to particulate composition categories. At least one feature is identified within each bin, and is used to determine the amount of particulate flow in each particulate composition category.

IDMS signal processing to distinguish inlet particulates

A gas turbine engine system includes an air inlet, an inlet particle separator located at the air inlet and having a blower selectively driven by a variable output motor, and a controller for dynamically controlling the variable output motor that selectively drives the blower of the inlet particle separator.

Particle separator and debris control system

A process for optimizing maintenance work schedules for at least one engine includes the steps of retrieving at least one set of data for an engine from a computer readable storage medium; selecting at least one scheduling parameter for the engine; selecting a set of maintenance rules for the engine; selecting at least one maintenance work decision; selecting at least one objective for the engine; optimizing the at least one objective to generate at least one optimal maintenance work decision; and generating at least one optimal maintenance work schedule for the engine.

Process and system for multi-objective global optimization of maintenance schedules

An example method of controlling gas turbine engine debris monitoring sensors includes detecting debris carried by air moving through an engine using at least first and second debris sensors, and processing signals from both the first and second debris sensors using a common signal conditioning unit. Another example method of monitoring gas turbine engine debris includes configuring at least one first debris sensor to detect debris carried by airflow moving through a first portion of an engine and configuring at least one second debris sensor to detect debris carried by airflow moving though a second portion of the engine. The method alternates between using the first debris sensor to detect debris and using the second debris sensor to detect debris.

Gas turbine engine debris monitoring arrangement

An example method of estimating gas turbine engine performance deterioration includes monitoring debris in at least a portion of an engine and estimating performance deterioration of at least one component of the engine using information from the monitoring. The method may use gas path parameters, such as pressures, temperatures, and speeds to establish the estimated performance deterioration. An example gas turbine engine performance assessment system includes a debris monitoring system configured to monitor debris moving through a portion of an engine and a controller programmed to estimate performance deterioration of at least one component of the engine based on information from the debris monitoring system.

System and method of estimating gas turbine engine performance

An example method of assessing a component of a gas turbine engine includes monitoring debris in at least a portion of an engine and establishing an estimated thermal energy level for a component of the engine based on the monitoring. The method may use gas path parameters to establish the estimated thermal energy level. An example gas turbine engine component assessment system includes a debris monitoring system configured to monitor debris moving through a portion of an engine and a controller programmed to execute an engine deterioration model that assesses a thermal energy level of at least one component of the engine based on information from the debris monitoring system.

System and method of assessing thermal energy levels of a gas turbine engine component

A variable vane control system for use with a gas turbine engine includes a plurality of vanes, an actuation assembly, a mechanical linkage assembly, and a sensor. Each of the plurality of vanes has an airfoil portion disposed in a gas flowpath of the gas turbine engine, and a position of each of the vanes is adjustable with respect to an angle of attack of the airfoil portion of each vane. The actuation assembly is configured for generating actuation force to position the plurality of vanes. The mechanical linkage assembly operably connects the actuation assembly to at least one of the plurality of vanes. The sensor is configured to sense at least one of the position of the airfoil portions of the plurality of vanes and the mechanical linkage assembly, and to generate a position output signal.

System and method for controlling stator assemblies

A method for monitoring performance of an actuator is provided that includes the steps of: 1) determining an actuator response time constant in a control system, which actuator response time constant is representative of a task time taken by the actuator to complete a selected percentage of a task; 2) determining a rate of change of the actuator response time constant over a predetermined number of events; 3) determining a fault value indicative of when the actuator response time constant will equal or exceed an error time constant using the determined rate of change of the actuator response time constant; and 4) outputting a signal from the control system representative of the determined fault value.

Actuator performance monitoring system

Systems and methods involving engine models are provided. A representative method for updating a gas turbine engine reference model includes: determining whether an absolute difference between a dominant time constant of a sensor and an assumed sensor model time constant is outside a tolerance value; and responsive to determining that the absolute difference is outside the tolerance value, updating the assumed sensor model time constant with the dominant time constant of the sensor.

Systems and methods involving engine models

A fault reasoning system combines both a model-based reasoning approach based upon predicted root causes of a plurality of fault signals, and a case-based reasoner that looks at the actual root cause of actual cases.

Hybrid fault reasoning and guided troubleshooting system that uses case-based reasoning and model-based reasoning

A method for conducting a repairable inventory analysis calculates the minimum number of repairable parts to be held in inventory while still being able to satisfy the demand for spare parts even in view of uncertain repair lead times. The method uses a set of equations that relates a customer service level to arbitrary probability distribution functions of a repair lead-time and a part arrival process. The distributions are then searched to locate the optimal inventory level in the distribution.

Rotable inventory calculation method

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West Hartford, CT, United States of America

Ravi Rajamani