Now showing items 1-6 of 6
Linear and Nonlinear Features and Machine Learning for Wind Turbine Blade Ice Detection and Diagnosis
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 ...
A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing
Wind turbine blades are constantly submitted to different types of particles such as dirt, ice, etc., as well as all the different environmental parameters that affect the behaviour and efficiency of the energy generation ...
Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis
Icing blades require of advanced condition monitoring systems to reduce the failures and downtimes in Wind Turbine Blades (WTB). This paper presents a case study that combines ultrasonic techniques with Wavelet transforms ...
Calculus of the defect severity with EMATs by analyzing the attenuation curves of the guided waves
The aim of this paper is to develop a novel method to determine the severity of a damage in a thin plate. This paper presents a novel fault detection and diagnosis approach employing a new electromagnetic acoustic transducer, ...
Maintenance Management based on Machine Learning and Nonlinear Features in Wind Turbines
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 ...
Machine Learning for Wind Turbine Blades Maintenance Management
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 ...