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Single-Sensor Impact Source Localization Method for Anisotropic Glass Fiber Composite Wind Turbine Blades
Liping Huang1, Kai Lu1, Liang Zeng2
1School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|July 30, 2025
Summary
A new deep learning method accurately locates wind turbine blade impacts using a single sensor. This cost-effective approach achieves 96.9% accuracy, improving structural health monitoring and reducing costs compared to traditional multi-sensor systems.
Area of Science:
- Engineering
- Materials Science
- Artificial Intelligence
Background:
- Wind turbine blades face various impacts (bird strikes, lightning, hail) during operation.
- Accurate impact source localization is crucial for effective structural health monitoring (SHM).
- Conventional localization methods often require complex multi-sensor arrays and anisotropy compensation.
Purpose of the Study:
- To propose a novel single-sensor method for localizing impact sources on wind turbine blades.
- To leverage deep learning to transform impact localization into a classification task.
- To reduce the cost and complexity of SHM systems for wind turbines.
Main Methods:
- Developed a deep learning framework for impact source localization.
- Treated localization as a classification problem, avoiding anisotropy compensation.
- Utilized the inherent anisotropic effects of blade material and geometry for single-sensor localization.
Main Results:
- Achieved a high localization accuracy of 96.9% with a single sensor.
- Demonstrated the effectiveness of the deep learning approach in classifying impact locations.
- Significantly reduced the cost compared to traditional multi-sensor array schemes.
Conclusions:
- The proposed single-sensor method offers a cost-effective and accurate solution for wind turbine blade impact detection.
- Deep learning effectively addresses the challenges of impact localization in SHM.
- This approach enhances the reliability and economic viability of wind turbine structural health monitoring.
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