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.

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 points suffer greater traction and compression forces in working conditions, and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using machine learning. Delamination was induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule-Walker model is employed for feature extraction, and Akaike's information criterion method for feature selection. The classifiers are quadratic discriminant analysis, k-nearest neighbors, decision trees, and neural network multilayer perceptron. The confusion matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: recall, specificity, precision, and F-score.

a b s t r a c tParabolic trough concentrators are the most widely deployed type of solar thermal power plant. The majority of parabolic trough plants operate up to 400 C. However, recent technological advances involving molten salts instead of oil as working fluid the maximum operating temperature can exceed 550 C. CSP plants face several technical problems related to the structural integrity and inspection of critical components such as the solar receivers and insulated piping of the coolant system. The inspection of the absorber tube is very difficult as it is covered by a cermet coating and placed inside a glass envelope under vacuum. Volumetric solar receivers are used in solar tower designs enabling increased operational temperature and plant efficiency. However, volumetric solar receiver designs inherently pose a challenging inspection problem for maintenance engineers due to their very complex geometry and characteristics of the materials employed in their manufacturing. In addition, the rest of the coolant system is insulated to minimise heat losses and therefore it cannot be inspected unless the insulation has been removed beforehand. This paper discusses the non-destructive evaluation techniques that can be employed to inspect solar receivers and insulated pipes as well as relevant research and development work in this field.

Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliability Engineering & System Safety.