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Related Concept Videos

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...

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Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Cross-Sensor Consistency-Guided Dual-Spectrum Fusion for Offshore Wind Turbine Blade Defect Diagnosis and Risk

Yukun Wang1, Chenhao Sun1, Ruifeng Liao2

  • 1State Key Laboratory of Disaster Prevention and Reduction for Power Grid, Changsha University of Science and Technology, Changsha 410114, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new dual-spectrum fusion framework to improve offshore wind turbine blade defect detection. The method enhances reliability and accuracy in diagnosing defects in harsh marine environments.

Keywords:
cross-sensor consistencydefect diagnosisdual-spectrum imagingoffshore wind turbine bladerisk grading

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Area of Science:

  • Engineering
  • Materials Science
  • Renewable Energy

Background:

  • Offshore wind turbine blades face harsh marine conditions, leading to small, hard-to-detect defects.
  • Current single-sensor methods and basic fusion techniques are inadequate for reliable defect diagnosis.

Purpose of the Study:

  • To propose a novel cross-sensor consistency-guided dual-spectrum fusion (CG-DSF) framework.
  • To enhance defect diagnosis and risk assessment for offshore wind turbine blades.

Main Methods:

  • Acquired visible-light and infrared thermal images using UAV-mounted sensors.
  • Developed sensor-specific branches for feature extraction and aligned features at the token level.
  • Implemented a reliability-aware fusion strategy guided by cross-sensor consistency.

Main Results:

  • The CG-DSF framework demonstrated significant advantages in identifying blade defects.
  • The method effectively suppresses low-quality or conflicting sensor data.
  • Achieved a unified and reliable defect representation for risk assessment.

Conclusions:

  • The proposed CG-DSF framework offers a feasible solution for intelligent operation and maintenance of offshore wind assets.
  • This approach improves proactive condition monitoring in complex marine environments.
  • Enhances the reliability of defect identification for offshore wind turbine blades.