United Technologies Corporation

Raytheon Technologies Corporation

Sikorsky Aircraft Corporation

United Technology Corporation

Alexander I Khibnik

Gregory S Hagen

Ravi Rajamani

Rajendra K Agrawal

Coy Bruce Wood

James P Cycon

William Donat

Alan Matthew Finn

Meggan Harris

Mark W Davis

Linda I Rivera

Michael J Giering

Ozgur Erdinc

Jun Ma

Satish Narayanan

Andrzej Banaszuk

Mauro Jorge Atalla

Bernd R Noack

Yiqing Lin

Howard A Winston

Sorin Costiner

Andreas P F Bernhard

Jihua Wang

Andreas Bernhard

Benjamin D Hall

Thomas R Matthis

Ricardo A Benitez

Julie A Ireland

Robert M Cohen

Peter F Horbury

Yiquing Lin

A method for foreign object debris detection in a gas turbine engine may comprise receiving, by a controller, a plurality of master configuration parameters, receiving, by the controller, a plurality of individual configuration parameters in response to the plurality of master configuration parameters, receiving, by the controller, a first time-series data from a database, pre-processing, by the controller, the first time-series data to generate a second time-series data, implementing, by the controller, an anomaly detector model, the anomaly detector model configured to generate a third time-series data, and implementing, by the controller, a foreign object debris (FOD) damage model, the FOD damage model receiving the third time-series data. The pre-processing the first time-series data, the implementing the anomaly detector model, and the implementing the FOD damage model may be defined by the plurality of individual configuration parameters.

Parametric trending architecture concept and design

A system and method for detecting foreign object debris damage is disclosed. A method for foreign object debris detection in a gas turbine engine may include receiving, by a controller, a first time-series data from a database, wherein the first time-series data comprises a feature, pre-processing, by the controller, the first time-series data to generate a second time-series data, generating a third time-series data via an anomaly detector model, sending, by the controller, the third time-series data to a foreign object debris (FOD) damage model, and determining, by the controller, that a FOD event has occurred based on data received from the FOD damage model. In various embodiments, the method may further comprise generating, by the controller, a health report (HR).

Foreign object debris trending concept and design

Virtual sensors can be used to monitor the loads on the system in determining damage accumulation, remaining useful life or retirement time of the components. A virtual sensor is a mathematical construct to infer a desired system measurement (e.g. a structural load) from readily available system state parameters (e.g. speed, weight, load factors, control settings, etc.). The accuracy of the virtual sensor depends upon the mapping between the desired measurement and the state parameters. A hybrid load monitoring system and method includes one or more direct or physical sensor measurements in addition to the plurality of virtual sensors. Signals from the physical sensors are included as an input (as opposed to an output) to the mapping between system state parameters and the various target sensor feature amplitudes.

Hybrid virtual load monitoring system and method

A system and method for debris particle detection with adaptive learning are provided. The method includes receiving oil debris monitoring (ODM) sensor data from an oil debris monitor sensor and fleet data from a database, detecting a feature in the ODM sensor data, generating an anomaly detection signal based on detecting an anomaly by comparing the feature in the ODM sensor data to a limit defined by system information stored in the fleet data, selecting a maintenance action request based on the anomaly detection signal, and adjusting one or more of the feature, the anomaly, the limit, and the maintenance action request by applying an adaptive learning algorithm that uses the ODM sensor data, fleet data, and feedback from field maintenance of one or more engines that evolves over time.

Oil debris monitoring (ODM) with adaptive learning

A system for identifying timeframes for borescope inspections for a gas turbine engine, having: monitoring systems monitor engine conditions, derive engine condition information and communicate such information to processors; feature interpreter module electronically communicates (i) with processors to process engine condition information, and (ii) feature interpreter information to processors, such information defines a probability of a FOD event; accumulator module electronically communicates (i) with processors to processes feature interpreter information, stored information since a last borescope inspection, and (ii) accumulator information to processors, such information defines an accumulated probability of a FOD event; and predictor and inspection planner module electronically communicates (i) with processors to processes accumulator information and stored information defines a threshold limits for a probability of a FOD event, and (ii) predictor and inspection planner information to processors, such information identifies a timeframe for a next borescope inspections predicted to occur before a FOD event.

System and method for planning engine borescope inspections based on FOD probability estimation

An article of manufacture including a tangible, non-transitory computer-readable storage medium having instructions stored thereon for detecting a fault in a gas turbine engine that, in response to execution by a controller, cause the controller to perform operations comprising receiving, by the controller, a first data output from a first flight data source; receiving, by the controller, a second data output from a second flight data source; determining, by the controller, a first flight realm of the first data output based on the second data output; identifying, by the controller, a fault threshold using a first parameter of the first flight realm; and comparing, by the controller, the first data output to the fault threshold.

Fault detection using high resolution realms

A method for monitoring rotating component includes receiving a continuous waveform sensor signal from a sensor apparatus, retaining the continuous waveform in a memory, and isolating at least one characteristic and/or at least one period of the waveform. The isolated characteristic and/or period is analyzed thereby determining the presence of a waveform anomaly.

Rotating machinery monitoring system

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

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 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

A method and system estimates a desired output signal waveform based on measured state parameters. A model training step generates a model by empirically obtaining measured signal waveforms corresponding to measured state parameters. Features, such as mode shapes and mode amplitudes, are extracted from the measured signals and stored in an estimation model along with coefficients of the model that correlate measured state parameters with the mode amplitudes. During part operation, the state parameters measured during operation are entered into the estimation model to obtain estimated features in a estimated signal waveform. The estimated features are then used to synthesize the estimated signal reflecting the actual operation of the operating part. The estimation model therefore allows estimation of an entire signal waveform from the state parameters.

Virtual load monitoring system and method

A diffuser ( ) has a step ( ) in the surface ( ) just upstream of a point where boundary layer separation might occur. An acoustic jet ( ) has a nozzle ( ) immediately downstream of the step ( ); the acoustic jet is driven ( ) by a plurality of frequencies, one or more of which are subharmonics of another of them, and having different phases.

Growing community of inventors

Glastonbury, CT, United States of America

Alexander I Khibnik