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Dirt and Mud Detection and Diagnosis on a Wind Turbine Blade employing Guided Waves and Supervised Learning Classifiers
(Elsevier, 2018)
Dirt and mud on wind turbine blades (WTB) reduce productivity and can generate stops and downtimes. A condition monitoring system based on non-destructive tests by ultrasonic waves was used to analyse it. This paper employs ...
Linear and Nonlinear Features and Machine Learning for Wind Turbine Blade Ice Detection and Diagnosis
(Elsevier, 2018-08)
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 ...
Artificial Intelligence for Concentrated Solar Plant Maintenance Management
(Springer Singapore, 2016-08-24)
Concentrated Solar Power (CSP) is an alternative to the conventional energy
sources which has had significant advances nowadays. A proper predictive
maintenance program for the absorber pipes is required to detect defects ...
Machine Learning and Neural Network for Maintenance Management
(Springer, 2017-06-29)
The paper presents a novel approach that allows to optimize the ultrasonic wave sensors for a condition monitoring system employing. It can detect and diagnosis different faults with a signal, such as delamination, mud or ...
“A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers”
(2015)
The objective of this paper is to demonstrate a novel signal processing for detection, identification and flaw sizing of structural
damage using ultrasonic testing with Electromagnetic Acoustic Transducers (EMATs). Damage ...
OptiWindSeaPower: Gestión Integral Óptima de Parques Eólicos Offshore Mediante Nuevos Modelos Matemáticos (1º parte)
(Asociación Española de Ensayos No Destructivos, 2009-03)
Las políticas de Unión Europea en energía y medio ambiente están dirigidas a impulsar y desarrollar plataformas eólicas offshore. Ello hace que el sistema eléctrico español vaya a depender, cada vez más, de este tipo de ...
Machine Learning and Neural Network for Maintenance Management
(Springer, 2017-06-29)
A novel Non-Destructive Test (NDT) is presented in this paper. It employs a radiometric sensor that measures the infrared emissivity of the solar panel surface embedded in an unmanned aerial vehicle. The measurements ...
Maintenance Management based on Machine Learning and Nonlinear Features in Wind Turbines
(Elsevier, 2020-02)
Delamination is a common problem in wind turbine blades, creating stress concentration areas that can lead to the partial or complete rupture of the blade. This paper presents a novel delamination classification approach ...
OptiWindSeaPower: Gestión Integral Óptima de Parques Eólicos Offshore Mediante Nuevos Modelos Matemáticos (2ª parte)
(Asociación Española de Ensayos no Destructivos, 2019-10)
En el artículo “OptiWindSeaPower: Gestión Integral Óptima de Parques Eólicos Offshore Mediante Nuevos Modelos Matemáticos” [1], revista AEND número 86, se presentaba el sistema de monitorización desarrollado en el laboratorio ...
Machine Learning for Wind Turbine Blades Maintenance Management
(MDPI, 2017)
Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These ...