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Title: Wind turbine fault detection using acoustic, vibration, and electrical signals

Abstract

Systems and methods for detecting faults are provided. A method for determining a fault condition for a component of a drivetrain in a wind turbine can include receiving an acoustic signal from an acoustic signal measuring device. The method can further include receiving a vibration signal from a vibration signal measuring device. The method can further include analyzing the acoustic signal to determine an analyzed acoustic signal. The method can further include analyzing the vibration signal to determine an analyzed vibration signal. The method can further include determining a fault condition for the component based at least in part on the analyzed acoustic signal and analyzed vibration signal. The fault condition can further be determined based at least in part on an analyzed electrical signal.

Inventors:
; ; ;
Issue Date:
Research Org.:
General Electric Co., Schenectady, NY (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1600356
Patent Number(s):
10495693
Application Number:
15/610,992
Assignee:
General Electric Company (Schenectady, NY)
Patent Classifications (CPCs):
F - MECHANICAL ENGINEERING F03 - MACHINES OR ENGINES FOR LIQUIDS F03D - WIND MOTORS
F - MECHANICAL ENGINEERING F05 - INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04 F05B - INDEXING SCHEME RELATING TO MACHINES OR ENGINES OTHER THAN NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, TO WIND MOTORS, TO NON-POSITIVE DISPLACEMENT PUMPS, AND TO GENERATING COMBUSTION PRODUCTS OF HIGH PRESSURE OR HIGH VELOCITY
DOE Contract Number:  
EE0006802
Resource Type:
Patent
Resource Relation:
Patent File Date: 06/01/2017
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY

Citation Formats

Unnikrishnan, Jayakrishnan, He, Lijun, Matthews, Brett Alexander, and Hao, Liwei. Wind turbine fault detection using acoustic, vibration, and electrical signals. United States: N. p., 2019. Web.
Unnikrishnan, Jayakrishnan, He, Lijun, Matthews, Brett Alexander, & Hao, Liwei. Wind turbine fault detection using acoustic, vibration, and electrical signals. United States.
Unnikrishnan, Jayakrishnan, He, Lijun, Matthews, Brett Alexander, and Hao, Liwei. Tue . "Wind turbine fault detection using acoustic, vibration, and electrical signals". United States. https://www.osti.gov/servlets/purl/1600356.
@article{osti_1600356,
title = {Wind turbine fault detection using acoustic, vibration, and electrical signals},
author = {Unnikrishnan, Jayakrishnan and He, Lijun and Matthews, Brett Alexander and Hao, Liwei},
abstractNote = {Systems and methods for detecting faults are provided. A method for determining a fault condition for a component of a drivetrain in a wind turbine can include receiving an acoustic signal from an acoustic signal measuring device. The method can further include receiving a vibration signal from a vibration signal measuring device. The method can further include analyzing the acoustic signal to determine an analyzed acoustic signal. The method can further include analyzing the vibration signal to determine an analyzed vibration signal. The method can further include determining a fault condition for the component based at least in part on the analyzed acoustic signal and analyzed vibration signal. The fault condition can further be determined based at least in part on an analyzed electrical signal.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {12}
}

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