Michael J Giering, 6139 Tumblebrook Rd, Bolton, CT 06043

2021020, Jul 8, 2021

May 10, 2019

17/054300

- Palm Beach Gardens FL, US
Michael J. Birnkrant - Wethersfield CT, US
Marcin Piech - East Hampton CT, US
Catherine Thibaud - Cork, IR
Michael J. Giering - Bolton CT, US
Kishore K. Reddy - Vernon CT, US
Vivek Venugopalan - South Riding VA, US

G01N 21/552
G01N 27/12
G01N 29/036
G01N 33/00

An example SPR detection system includes a first prism having a first surface adjacent to a first metal layer exposed to a sample gas, and a second prism having a second surface adjacent to a second metal layer exposed to a reference gas. At least one light source is configured to provide respective beams to the first and second surfaces, where each of the beams causes SPR of a respective one of the metal layers. At least one photodetector is configured to measure a reflection property of reflections of the respective beams from the metal layers during the SPR. A controller is configured to determine whether a target gas is present in the sample gas based on a known composition of the reference gas and at least one of an electrical property of the first and second metal layers during the SPR and the reflection property of the metal layers.

2021010, Apr 8, 2021

Oct 4, 2019

16/593328

- Farmington CT, US
Ryan B. Noraas - Hartford CT, US
Michael J Giering - Bolton CT, US
Olusegun T Oshin - Middletown CT, US

G06N 3/08
B64F 5/00

A method for designing a material for an aircraft component according to one example 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. Each of the images in the set of images has varied constituent compositions and at least one patch of corresponding data is embedded into the image. The method also includes 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.

2020040, Dec 24, 2020

Jun 21, 2019

16/448365

- Farmington CT, US
Justin R. Urban - Tolland CT, US
Michael J. Giering - Bolton CT, US
Quan Long - West Hartford CT, US
Alexandria Dorgan - West Hartford CT, US

G01M 15/14
F02D 35/02
B64F 5/60

A system for providing real time aircraft engine sensor analysis includes a computer system configured to receive an engine operation data set in real time. The computer system includes a machine learning based analysis tool and a user interface configured to display a real time analysis of the engine operation data set. The user interface includes at least one portion configured to identify a plurality of anomalies in the engine operation data set.

2020017, Jun 4, 2020

Dec 3, 2018

16/207452

- Farmington CT, US
Anya B. Merli - Wethersfield CT, US
Ryan B. Noraas - Hartford CT, US
Michael J. Giering - Bolton CT, US
Olusegun T. Oshin - Manchester CT, US

G01M 15/14
G06N 3/08
G06N 3/04

A tool for monitoring a part condition includes a computerized device having a processor and a memory. The computerized device includes at least one of a camera and an image input and a network connection configured to connect the computerized device to a data network. The memory stores instructions for causing the processor to perform the steps of providing an initial micrograph of a part to a trained model, providing a data set representative of operating conditions of the part to the trained model, and outputting an expected state of the part from the trained model based at least in part on the input data set and the initial micrograph.

2020005, Feb 20, 2020

Aug 17, 2018

16/104435

- Farmington CT, US
Ryan B. Noraas - Hartford CT, US
Michael J. Giering - Bolton CT, US

B64F 5/10
G06T 3/40
G06T 7/00
G06K 9/62
G05B 19/4097
G06N 3/08

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.

2019005, Feb 14, 2019

Dec 13, 2017

15/840132

- Farmington CT, US
Edgar A. Bernal - Webster NY, US
Michael J. Giering - Bolton CT, US
Ryan B. Noraas - Vernon CT, US

UNITED TECHNOLOGIES CORPORATION - Farmington CT

G06N 99/00

A sensor system may comprise a sensor; a processor in electronic communication with the sensor; and/or a tangible, non-transitory memory configured to communicate with the processor, the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations. The operations may comprise recording, by the sensor, a preliminary type data sample; and/or applying, by the processor, a mapping function having a plurality of tuned parameters to the preliminary type data sample, producing a desired type data output.

2019005, Feb 14, 2019

Nov 8, 2017

15/807359

- Farmington CT, US
Kishore K. Reddy - Vernon CT, US
Michael J. Giering - Bolton CT, US
Ryan B. Noraas - Hartford CT, US
Kin Gwn Lore - Manchester CT, US

UNITED TECHNOLOGIES CORPORATION - Farmington CT

G06T 5/20
G06K 9/62
H04N 7/18
H04N 5/232

A sensor system may comprise a sensor; a processor in electronic communication with the sensor; and/or a tangible, non-transitory memory configured to communicate with the processor, the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations. The operations may comprise recording, by the sensor, a low quality data sample; and/or applying, by the processor, a mapping function having a plurality of tuned parameters to the low quality data sample, producing a high quality data output.

2018010, Apr 19, 2018

Oct 19, 2016

15/297319

- Farmington CT, US
Yiqing Lin - Glastonbury CT, US
Ozgur Erdinc - Coventry CT, US
Michael J. Giering - Bolton CT, US
Alexander I. Khibnik - Glastonbury CT, US

G05B 23/02
F01D 21/00
F01D 25/16
F01D 25/18
G01N 33/28
G05B 13/02

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.