Defect Detection Techniques in Wind Turbine Blades

Wind turbine blade integrity is critical to the safe and efficient generation of renewable energy. Defects such as surface cracks, delamination, erosion and internal voids can arise from operational stress, environmental exposure and material fatigue. Traditional inspection methods include manual visual assessment, acoustic emission, vibration analysis, fibre Bragg grating sensors and ultrasound scanning. More recently, high-resolution imaging—using drones or ground-based cameras—combined with automated image analysis has emerged as a powerful approach. Deep learning frameworks now enable real-time detection and classification of surface damage, while multispectral and thermal imaging reveal subsurface anomalies. Hybrid systems that fuse data from multiple sensors and apply advanced feature-fusion architectures offer greater accuracy across varying scales of defect. The global rise of offshore and onshore wind farms has driven rapid advances in both hardware and algorithmic techniques, enabling predictive maintenance regimes that reduce downtime and operational costs.

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Defect Detection Techniques in Wind Turbine Blades publication trend

The graph below shows the total number of articles in defect detection techniques in wind turbine blades across all publications each year (not limited to Nature Index journals).

Technical terms

Convolutional Block Attention Module (CBAM): A neural network component that sequentially applies channel and spatial attention to enhance feature representation in convolutional layers.

Bidirectional Feature Pyramid Network (BiFPN): A feature-fusion architecture that enables efficient top–down and bottom–up pathway connections for multiscale object detection.

Vision Transformer (ViT): An image recognition model that applies transformer blocks to sequences of image patches, capturing long-range dependencies.

Ensemble learning: A technique that combines predictions from multiple models to improve overall accuracy and robustness.

Thermal imaging: A method of capturing infrared emissions to visualise temperature differences, useful for detecting subsurface or heat-related anomalies.

YOLOv8: A real-time object detection algorithm based on convolutional neural networks, optimised for speed and accuracy in detecting multiple defect types.

References

1. Research on an Intelligent Identification Method for Wind Turbine Blade Damage Based on CBAM-BiFPN-YOLOV8. Processes (2024).
2. Data Fusion and Ensemble Learning for Advanced Anomaly Detection Using Multi-Spectral RGB and Thermal Imaging of Small Wind Turbine Blades. Energies (2024).
3. Detection of Defects in Wind Turbin Blade Based on Cascaded Adaptive Hybrid Attention Network. IEEE Access (2024).