The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 10, 2019

Filed:

Oct. 19, 2016
Applicant:

United Technologies Corporation, Farmington, CT (US);

Inventors:

Gregory S. Hagen, Glastonbury, CT (US);

Yiqing Lin, Glastonbury, CT (US);

Ozgur Erdinc, Coventry, CT (US);

Michael J. Giering, Bolton, CT (US);

Alexander I. Khibnik, Glastonbury, CT (US);

Assignee:

UNITED TECHNOLOGIES CORPORATION, Farmington, CT (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 23/02 (2006.01); F01D 21/00 (2006.01); F01D 25/16 (2006.01); F01D 25/18 (2006.01); G01N 33/28 (2006.01); G05B 13/02 (2006.01); F16N 29/00 (2006.01); G06N 20/00 (2019.01); F16H 57/04 (2010.01);
U.S. Cl.
CPC ...
G05B 23/0289 (2013.01); F01D 21/003 (2013.01); F01D 25/16 (2013.01); F01D 25/18 (2013.01); F16N 29/00 (2013.01); G01N 33/2835 (2013.01); G01N 33/2858 (2013.01); G05B 13/0265 (2013.01); G05B 23/0205 (2013.01); F05D 2220/32 (2013.01); F16H 57/0405 (2013.01); G06N 20/00 (2019.01);
Abstract

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.


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