Options
A data-driven approach for scour detection around monopile-supported offshore wind turbines using Naive Bayes classification
Date Issued
2024-05-01
Date Available
2024-09-02T16:19:03Z
Abstract
This paper proposes a novel data-driven framework for scour detection around offshore wind turbines (OWTs), where damage features are derived from wind and wave-induced acceleration signals collected along the tower. A numerical model of the NREL 5 MW wind turbine, which considers aerodynamic and hydrodynamic loading with soil-structure interaction (SSI) and servo-dynamics, is developed. The model is used to simulate the acceleration responses along the tower for a healthy structure, and a structure affected by progressive scour. A data segmentation process is initially performed on the collected data, which is followed by a feature selection scheme based on the analysis-of-variance (ANOVA) algorithm, to eliminate irrelevant characteristics from the time domain feature set of responses. The proposed framework consists of two main components: (a) offline training, and (b) real-time classification. The acceleration responses collected from the healthy structure and the structure subjected to three different damage scenarios (different scour depths) and under various load conditions, are used in the offline training mode. The selected feature vector from the feature extraction process is used as input to a Naive Bayes classifier (NBC) algorithm to train the model. In the real-time classification, a prediction of the scour depth affecting the structure is performed using a new dataset simulated from unseen load cases and scour conditions of the OWT. The results show that the model trained in the offline stage can predict the scour depth in the real-time monitoring stage with performance measures over approximately 94%.
Sponsorship
Irish Research Council
Science Foundation Ireland
Publisher
Elsevier
Journal
Marine Structures
Volume
95
Language
English
Status of Item
Peer reviewed
ISSN
0951-8339
This item is made available under a Creative Commons License
File(s)
Owning collection
Scopus© citations
2
Acquisition Date
Sep 13, 2024
Sep 13, 2024
Views
16
Acquisition Date
Sep 14, 2024
Sep 14, 2024
Downloads
9
Acquisition Date
Sep 14, 2024
Sep 14, 2024