Rtx Corporation

Raytheon Technologies Corporation

Sikorsky Aircraft Corporation

United Technologies Corporation

Raytheon Company

Goodrich Corporation

Amit Surana

Ozgur Erdinc

Mahmoud El Chamie

Alan Matthew Finn

Edgar A Bernal

David W Morganson

George A Mathew

Luca F Bertuccelli

Matthew O Williams

Michael J Giering

Kunal Srivastava

Kenji Homma

Kishore K Reddy

Sudarshan N Koushik

Soumalya Sarkar

Vivek Venugopalan

Greg C Ojard

Michael Walter Bennett

Brigid A Blakeslee

Tuhin Sahai

Francesca Stramandinoli

Laura A Gordon

Rebecca L Runkle

Francesco Leonardi

Bruno Abreu Calfa

Jerry Ding

Christoph Schulze

Ayman Elkfrawy

Matthew L Brantley, Jr

Yiqing Lin

Shaunak D Bopardikar

David C Fisher

Jr Matthew L Brantley

Eklou E Hiheglo

An auction-based distributed method for cooperative multi-target tracking by a plurality of satellites, includes distributed target tracking error and utilization computation as an auction bid across the satellite and uses message-passing to find best bid, handles different tracking requirements via definition of bids and modification of task definition used during the bidding process, and selected a list of auction winners to track the target.

Distributed satellite constellation for cooperative multi-target tracking

A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for indication confirmation for detecting a sub-surface defect, the set of instructions comprising: an instruction to initialize a transducer starting location and a transducer orientation responsive to a prior determination of a potential flaw location; an instruction to optimize an observation point of the transducer responsive to the transducer starting location and the transducer orientation responsive to a flaw response model; an instruction to move the transducer to the observation point location and orientation; an instruction to collect the scan data at the observation point location and orientation; and an instruction to analyze the scan data to extract a measure of the flaw response model; and an instruction to update the flaw response model.

System and method for automated indication confirmation in ultrasonic testing

A method of inspecting a component for anomalous regions includes recording a plurality of sensor readings using one or more sensors, each reading corresponding to a different region of the component; determining an appearance metric for each reading; and determining a nominal appearance metric based on individual values of the appearance metric for a first subset of the readings. The method includes, for a particular sensor reading outside the first subset: determining a difference between the nominal appearance metric and the appearance metric of the particular sensor reading; updating the nominal appearance metric based on the particular sensor reading; and, based on the difference exceeding a threshold: determining that the particular sensor reading is anomalous and corresponds to an anomalous region of the component, and recording additional sensor readings of the anomalous region using one or more sensing parameters that differ from those used to record the anomalous sensor reading.

System and method for inspecting a component for anomalous regions

A method for determining structural vibration mode characteristics includes preparing a digital image correlation (DIC) dataset for the component, applying a dynamic mode decomposition (DMD) technique to the DIC dataset to determine a plurality of DMD modes, selecting at least one dominant DMD mode from the plurality of DMD modes, classifying each of the at least one selected dominant DMD mode as a standing wave or a traveling wave, and determining structural vibration mode characteristics of the component by extracting the structural vibration mode characteristics from each of the at least one selected dominant DMD mode having a standing wave classification.

System and method for structural vibration mode identification

A system for detecting a sub-surface defect comprising a transducer fluidly coupled to a part located in a tank containing a liquid configured to transmit ultrasonic energy, the transducer configured to scan the part to create scan data of the scanned part; a pulser/receiver coupled to the transducer configured to receive and transmit the scan data; a processor coupled to the pulser/receiver, the processor configured to communicate with the pulser/receiver and collect the scan data; and the processor configured to detect the sub-surface defect and the processor configured to have a sub-surface defect confidence assessment and a prioritization for further human evaluation.

System and method for dual pulse-echo sub-surface detection

A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for detecting a sub-surface defect, the set of instructions comprising an instruction to receive scan data for a part from a transducer; an instruction to collect the scan data; an instruction to determine an indication in the scan data that indicates a distractor, wherein the indication is based on a learning phase module and an inference phase module that the processor uses to self-assess the indication; and an instruction to create a defect indication report.

Automated scan data quality assessment in ultrasonic testing

Data indicative of a plurality of observations of an environment are received at a control system. Machine learning using deep reinforcement learning is applied to determine an action based on the observations. The deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations and applies a training set to locate one or more regions having a higher reward. The action is applied to the environment. A reward token indicative of alignment between the action and a desired result is received. A policy parameter of the control system is updated based on the reward token. The updated policy parameter is applied to determine a subsequent action responsive to a subsequent observation.

Control systems using deep reinforcement learning

A method for nondestructive inspection of a component, the method includes determining a first pulse-echo scan from a first side of a component; determining a second pulse-echo scan from a second side of the component; determining a through-transmission scan based on the first pulse-echo scan, the second pulse-echo scan, and a model of the component, the model comprises a rigid internal structure of the component; and classifying the component based on comparing the through-transmission scan to a 'gold' model.

Nondestructive inspection using dual pulse-echo ultrasonics and method therefor

A method of sensor node position determination for a sensor network is provided. A coverage distribution is defined based on a number of sensor nodes and sensor footprints of the sensor nodes. A desired position for each of the sensor nodes is determined based on the coverage distribution and a prior probability distribution defined on a bounded domain for the number of sensor nodes as a minimization of a distance between the coverage distribution and the prior probability distribution. The desired position to configure the sensor nodes is output.

Coverage optimization for sensor networks

A method for nondestructive vibrothermography inspection of a component, the method includes generating ultrasonic excitations in a component over a range of frequencies; determining a thermal signature in the component from the excitations; registering a model with the thermal signature; determining damage based on the thermal signal and model; and classifying the component based on the determining.

Nondestructive inspection using thermoacoustic imagery and method therefor

A method of controlling a directional facing of a sensor mounted to a vehicle includes storing, by a sensor control computer, previously-searched locations of a terrain at which the sensor is directed. The method includes determining a likelihood of detecting a target at one or more points on the terrain and controlling a directional facing of the sensor onto the terrain based on the previously-searched locations and the likelihood of detecting the target.

Locational and directional sensor control for search

A method and system for optimizing a human supervised cyber-physical system determines a state of the human operator based on data from multiple psycho physiological sensors, determines a state of each of multiple cyber-physical systems in the human supervised cyber-physical system based on data provided by the cyber-physical systems, and fuses the state of the human operator and the state of each of the plurality of cyber-physical systems into a single state of the human supervised cyber-physical system. The single state is then used to generate recommendations for optimizing a user interface and to generate high level control signals for the cyber-physical systems.

Optimization of human supervisors and cyber-physical systems

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West Hartford, CT, United States of America

Amit Surana