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Fault detection of wind turbine sensors using artificial neural networks
Ayse Gokcen Kavaz
*
,
Burak Barutcu
*
Corresponding author for this work
Energy Institute
Istanbul Technical University
Research output
:
Contribution to journal
›
Article
›
peer-review
40
Citations (Scopus)
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Keyphrases
Artificial Neural Network
100%
Wind Turbine
100%
Fault Detection
100%
Calibration Drift
100%
Sensor Measurement
66%
Sensor Fault
66%
Health Status
33%
High Performance
33%
Temperature Sensor
33%
Detection Method
33%
System State
33%
Condition Monitoring
33%
Fault Detection Algorithm
33%
Nonlinear Characteristics
33%
Output Rate
33%
Sensor Calibration
33%
Data Acquisition System
33%
Isolation Method
33%
Measurement Characteristics
33%
Supervisory Control
33%
Sensor Validation
33%
Low Output
33%
Faulty Behavior
33%
Measurement Validation
33%
Nonlinear Environment
33%
Engineering
Using Sensor
100%
Compressed Air Motors
100%
Artificial Neural Network
100%
Sensor Measurement
66%
System State
33%
Drive System
33%
Condition Monitoring
33%
Supervisory Control and Data Acquisition System
33%
Thermal Sensor
33%
Detection Algorithm
33%
State of Health
33%
Earth and Planetary Sciences
Compressed Air Motors
100%
Fault detection
100%
Artificial Neural Network
100%
Sensor Calibration
33%
Data Acquisition
33%
Temperature Sensor
33%
Sensor Validation
33%
Chemical Engineering
Neural Network
100%
Condition Monitoring
50%