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Shock and Vibration
Volume 2016 (2016), Article ID 6423587, 18 pages
Research Article

Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

1Condition and Structural Health Monitoring, TWI Ltd, Cambridge CB21 6AL, UK
2School of Engineering and Design, Brunel University, Uxbridge UB8 3PH, UK

Received 15 March 2016; Revised 20 June 2016; Accepted 11 July 2016

Academic Editor: Lu Chen

Copyright © 2016 A. Romero et al. 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.


Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.