Researchers from the University of Groningen have developed a low-cost, scalable method for detecting faults in wind turbine blades using 3D printed models, vibration analysis, and machine learning. The study demonstrates how scaled replicas of the NREL 5MW blade, fabricated with PLA, can be used to simulate damage scenarios and accurately classify structural faults using support vector machines and k-nearest neighbors with over 94% accuracy.
Wind turbine blades endure continuous mechanical stress and harsh environmental conditions, making early damage detection crucial to ensure structural integrity and reduce maintenance costs. Traditional inspection methods are often costly and labor-intensive. In this study, the researchers used a Bambu Lab 3D printer to fabricate a 300 mm scaled version of the NREL 5MW blade, introducing five types of crack-like damages in critical regions such as the root, mid-span, and transition zones.
To evaluate the structural impact of the faults, the team conducted Finite Element Method (FEM) simulations and validated the results through experimental modal analysis using a hammer test setup. The resonance frequencies for vibration modes 3, 4, and 6 were found to be particularly sensitive to structural anomalies. Frequency shifts of up to 3 Hz were observed in these modes when compared to healthy blades.
Feature extraction and machine learning
The researchers extracted features from the time and frequency domains, selecting those with the highest statistical significance through ANOVA testing. These features were then used to train several machine learning models, including Random Forest, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes classifiers. Among them, KNN and SVM achieved the highest classification accuracy, exceeding 94%.
By combining 3D printing, simulation, and machine learning, the study offers a reproducible and cost-effective method for structural health monitoring of wind turbine blades. The team plans to expand the methodology to multi-blade systems and more complex fault configurations, aiming to integrate it into real-time monitoring systems for predictive maintenance.
3D printing in wind energy research
This study aligns with recent developments in wind energy research, including NREL’s MADE3D project, which explores how additive manufacturing can enhance the structural and aerodynamic performance of turbine blades.
Additionally, 3D printing is increasingly being used to extend the lifecycle of wind turbine components. In one recent case, a decommissioned wind turbine blade was repurposed into a modular footbridge using additive manufacturing. The project showcased how reused blade material and 3D printed connectors can form sustainable civil infrastructure, further highlighting the intersection of wind energy and digital fabrication.
As additive technologies gain traction for large-format wind components, research into digital tools for fault detection and design optimization is becoming increasingly relevant to the renewable energy sector
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Featured image shows blade geometry of the scaled NREL 5MW blade. Image via University of Groningen.
