Matthew F Lemmon

US Patent:

20150159867, Jun 11, 2015

Filed:

Dec 5, 2013

Appl. No.:

14/097549

Inventors:

- Schenectady NY, US
Matthew Francis Lemmon - Marietta GA, US
Subrat Nanda - Houston TX, US
Jonathan David White - Grand Bay AL, US

Assignee:

General Electric Company - Schenectady NY

International Classification:

F23M 11/00
G01M 15/14
F23N 1/00

Abstract:

A system for assessing combustor health during operation of the combustor includes a combustor, a sensor configured to sense combustion dynamics pressure data from the combustor, and a computing device that is in communication with the sensor and configured to receive the combustion dynamics pressure data from the sensor. The computing device is programed to extract a feature from the combustion dynamics pressure data and generate feature values for the feature over a period of time. The computing device is also programmed to generate a cumulative form of the feature that is based on the feature values over a time series and to compare the cumulative form to a historical cumulative form.

US Patent:

20150160096, Jun 11, 2015

Filed:

Dec 5, 2013

Appl. No.:

14/097540

Inventors:

- Schenectady NY, US
Matthew Francis Lemmon - Marietta GA, US
Subrat Nanda - Houston TX, US
Jonathan David White - Grand Bay AL, US
Achalesh Kumar Pandey - Greenville SC, US

Assignee:

General Electric Company - Schenectady NY

International Classification:

G01M 15/14
F23R 3/00
F23M 11/00

Abstract:

A system for detecting an at-fault combustor includes a sensor that is configured to sense combustion dynamics pressure data from the combustor and a computing device that is in electronic communication with the sensor and configured to receive the combustion dynamics pressure data from the sensor. The computing device is programmed to convert the combustion dynamics pressure data into a frequency spectrum, segment the frequency spectrum into a plurality of frequency intervals, extract a feature from the frequency spectrum, generate feature values for the feature within a corresponding frequency interval over a period of time, and to store the feature values to generate a historical database. The computing device is further programmed to execute a machine learning algorithm using the historical database of the feature values to train the computing device to recognize feature behavior that is indicative of an at-fault combustor.