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Wind turbine fault detection and classification by means of image texture analysis

Author
Ruiz, M.; Mujica, L.E.; Alferez, E.; Acho, L.; Tutivén, C.; Vidal, Y.; Rodellar, J.; Pozo, F.
Type of activity
Journal article
Journal
Mechanical systems and signal processing
Date of publication
2018-07
Volume
107
First page
149
Last page
167
DOI
https://doi.org/10.1016/j.ymssp.2017.12.035 Open in new window
Project funding
Characterization and automatic classification of leukemic cells by means of digital image processing and pattern recognition for diagnosis support
DPI2017-82930-C2-1-R DESARROLLO Y VALIDACION DE SISTEMAS DE MONITORIZACION INTELIGENTE, ESTRATEGIAS DE CONTROL DEL PITCH Y DE AMORTIGUACION ESTRUCTURAL PARA AEROGENERADORES OFFSHOREFLOTANTES
Design of advanced control strategies and fault detection for complex mechatronic systems
Development and validation of failure detection and design of fault-tolerant control strategies with application in offshore wind energy plants
Repository
http://hdl.handle.net/2117/114770 Open in new window
Abstract
The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly offshore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of offshore wind turbines is approximately 15-35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to different faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and cha...
Citation
Ruiz, M., Mujica, L.E., Alferez, E., Acho, L., Tutivén, C., Vidal, Y., Rodellar, J., Pozo, F. Wind turbine fault detection and classification by means of image texture analysis. "Mechanical systems and signal processing", Juliol 2018, vol. 107, p. 149-167.
Keywords
fault classification, fault detection, texture analysis., wind turbine
Group of research
CoDAlab - Control, Dynamics and Applications

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