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Sensors 2015, 15(3), 5627-5648; doi:10.3390/s150305627

An SVM-Based Solution for Fault Detection in Wind Turbines

1
Department of Civil Engineering, University of Burgos, C/ Francisco de Vitoria s/n, Burgos 09006, Spain
2
CARTIF Foundation, Parque Tecnológico de Boecillo, Boecillo 47151, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 22 January 2015 / Revised: 17 February 2015 / Accepted: 25 February 2015 / Published: 9 March 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [993 KB, uploaded 9 March 2015]   |  

Abstract

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets. View Full-Text
Keywords: fault diagnosis; neural networks; support vector machines; wind turbines fault diagnosis; neural networks; support vector machines; wind turbines
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Santos, P.; Villa, L.F.; Reñones, A.; Bustillo, A.; Maudes, J. An SVM-Based Solution for Fault Detection in Wind Turbines. Sensors 2015, 15, 5627-5648.

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