TY - JOUR 
A1 - Arcos Jiménez, Alfredo
AU - García Márquez, Fausto Pedro
AU - Borja Moraleda, Victoria
AU - Gómez Muñoz, Carlos Quiterio
T1 - Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis
Y1 - 2019
SN - 0960-1481
UR - http://hdl.handle.net/11268/7399
AB - The mass of ice on wind turbines blades is one of the main problems that energy companies have in cold
climates. This paper presents a novel approach to detect and classify ice thickness based on pattern
recognition through guided ultrasonic waves and Machine Learning. To successfully achieve a supervised
classification, it is necessary to employ a method that allows the correct extraction and selection of
features of the ultrasonic signal. The main novelty in this work is that the approach considers four feature
extraction methods to validate the results, grouped by linear (AutoRegressive (AR) and Principal
Component Analysis) and nonlinear (nonlinear-AR eXogenous and Hierarchical Non-Linear Principal
Component Analysis), and feature selection is done by Neighbourhood Component Analysis. A supervised
classification was performed through Machine Learning with twenty classifiers such as Decision
tree, Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, and Ensemble Classifiers.
Finally, an evaluation of the classifiers was done in single frequency and multi-frequency modes,
obtaining accurate results.
KW - Redes de neuronas artificiales
KW - Ultrasonidos
KW - Recursos energéticos
KW - Investigación aplicada
LA - eng
ER -