Defect detection and classification of offshore wind turbine rotor blades

With increasing demand for electricity, wind turbines have gained significant attention from the public. Offshore wind power generation has emerged as a popular choice due to its potential to reduce power transmission losses and minimal impact on humans and organisms. However, it also presents chall...

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Bibliographic Details
Published in:Nondestructive testing and evaluation Vol. 39; no. 4; pp. 954 - 975
Main Authors: Deng, Liwei, Liu, Shanshan, Shi, Wei, Xu, Jiazhong
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 18.05.2024
Taylor & Francis Ltd
Subjects:
Algorithms
Classification
Clustering
Condition monitoring
deep learning
Defects
Electric power demand
Electricity distribution
Feature extraction
K-means
Machine learning
Offshore energy sources
Particle swarm optimization
PSO algorithms
Rotor blades
Rotor blades (turbomachinery)
S-U-net
Transmission loss
Turbines
VGG16 network
Wind power generation
Wind turbine rotor blades
Wind turbines
ISSN:1058-9759, 1477-2671
Online Access:Get full text
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Summary:With increasing demand for electricity, wind turbines have gained significant attention from the public. Offshore wind power generation has emerged as a popular choice due to its potential to reduce power transmission losses and minimal impact on humans and organisms. However, it also presents challenges in detecting defects in Wind Turbine Rotor Blades (WTRB) and repairing them. To address this issue, this paper proposes an improved wavelet-based S-U-Net network to denoise WTRB images followed by a weakly supervised CNN method for removing background parts that could affect defective feature extraction. Defective features are then extracted using VGG16 network on a deep learning server platform, while an enhanced Particle Swarm Optimization (PSO) algorithm combined with K-means is used to classify defect features of WTRBs. Experimental results demonstrate that classification accuracy of unlabelled blade defect datasets improved significantly from 62.6% using only Kmeans clustering method up to 96.4% using our proposed algorithm approach. This study applies improved PSO and Kmeans algorithms towards offshore Wind Turbine Rotor Blade condition monitoring with precise detection test results enabling early-stage detection of Wind Turbine Rotor Blades defects leading to timely repairs.
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ISSN:1058-9759
1477-2671
DOI:10.1080/10589759.2023.2234554