MyJournals Home  

RSS FeedsSensors, Vol. 20, Pages 1835: Structural Health Monitoring for Jacket-Type Offshore Wind Turbines: Experimental Proof of Concept (Sensors)


26 march 2020 15:00:59

Sensors, Vol. 20, Pages 1835: Structural Health Monitoring for Jacket-Type Offshore Wind Turbines: Experimental Proof of Concept (Sensors)

Structural health monitoring for offshore wind turbines is imperative. Offshore wind energy is progressively attained at greater water depths, beyond 30 m, where jacket foundations are presently the best solution to cope with the harsh environment (extreme sites with poor soil conditions). Structural integrity is of key importance in these underwater structures. In this work, a methodology for the diagnosis of structural damage in jacket-type foundations is stated. The method is based on the criterion that any damage or structural change produces variations in the vibrational response of the structure. Most studies in this area are, primarily, focused on the case of measurable input excitation and vibration response signals. Nevertheless, in this paper it is assumed that the only available excitation, the wind, is not measurable. Therefore, using vibration-response-only accelerometer information, a data-driven approach is developed following the next steps: (i) the wind is simulated as a Gaussian white noise and the accelerometer data are collected; (ii) the data are pre-processed using group-reshape and column-scaling; (iii) principal component analysis is used for both linear dimensionality reduction and feature extraction; finally, (iv) two different machine-learning algorithms, k nearest neighbor (k-NN) and quadratic-kernel support vector machine (SVM), are tested as classifiers. The overall accuracy is estimated by 5-fold cross-validation. The proposed approach is experimentally validated in a laboratory small-scale structure. The results manifest the reliability of the stated fault diagnosis method being the best performance given by the SVM classifier. Digg Facebook Google StumbleUpon Twitter
18 viewsCategory: Chemistry, Physics
Sensors, Vol. 20, Pages 1836: Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud (Sensors)
Sensors, Vol. 20, Pages 1834: Experimental Study on Glaze Icing Detection of 110 kV Composite Insulators Using Fiber Bragg Gratings (Sensors)
blog comments powered by Disqus
The latest issues of all your favorite science journals on one page


Register | Retrieve



Use these buttons to bookmark us: Digg Facebook Google StumbleUpon Twitter

Valid HTML 4.01 Transitional
Copyright © 2008 - 2020 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Travel Photos Nachrichten Indigonet Finances Leer Mandarijn