Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection-Reference-Cited by-同舟云学术

Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

Published:2011-07 Issue:5 Volume:25 Page:1849-1875
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Schlechtingen Meik,Ferreira Santos Ilmar

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference12 articles.

1. Online wind turbine fault detection through automated SCADA data analysis;Zaher;Wind Energy,2009

2. T.W. Verbruggen, Wind turbine operation & maintenance based on condition monitoring. ECN-C-03-047, 2003.

3. SIMAP: intelligent system for predictive maintenance application to the health condition monitoring of a windturbine gearbox;Sanz-Bobi;Computers in Industry,2006

4. A multi-agent fault detection system for wind turbine defect recognition and diagnosis;Zaher;Proceedings of IEEE Lausanne Powertech,2007

5. Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation;Shuhui;Journal of Solar Energy Engineering,2001

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