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:
May. 14, 2019

Filed:

Nov. 23, 2016
Applicant:

Abb Schweiz Ag, Baden, CH;

Inventors:

Rongrong Yu, Beijing, CN;

Niya Chen, Beijing, CN;

Yao Chen, Beijing, CN;

Assignee:

ABB Schweiz AG, Baden, CH;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
F03D 17/00 (2016.01); G06N 99/00 (2019.01);
U.S. Cl.
CPC ...
F03D 17/00 (2016.05); G06N 99/005 (2013.01); F05B 2260/80 (2013.01); F05B 2260/83 (2013.01); F05B 2260/84 (2013.01);
Abstract

The present application includes wind turbine condition monitoring method and system. The method includes: acquiring historical SCADA data, and wind turbine reports corresponding to the historical SCADA data; training an overall model for overall diagnosing the wind turbine, and training different individual models for analyzing different components of the wind turbine based on the historical SCADA data and the corresponding wind turbine report, by establishing relationship between the historical SCADA data and the wind turbine report; acquiring real time SCADA data, inputting the real time SCADA data to the trained overall model, obtaining the health condition of the wind turbine from the trained overall model, and performing individual diagnosing step if the trained overall model determines wind turbine as defective status; inputting the real time SCADA data to the trained individual model corresponding to the defective component, and obtaining the fault details of the defective component from the trained individual model corresponding to the defective component.


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