Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade.

Area of Science:

• Structural Health Monitoring
• Mechanical Engineering
• Materials Science

Background:

• Wind turbine blade integrity is crucial for operational efficiency and safety.
• Early detection of blade damage prevents catastrophic failures and reduces economic losses.
• Existing damage detection methods may have limitations in accuracy and scope.

Purpose of the Study:

• To develop and validate a novel non-baseline damage detection method for wind turbine blades.
• To utilize Fiber Bragg Gratings (FBG) for enhanced structural health monitoring.
• To improve the accuracy and reliability of wind turbine blade damage detection.

Main Methods:

• A new non-baseline damage detection method employing Fiber Bragg Gratings (FBG) is proposed.
• Chi-square distribution is used as a damage-sensitive feature for local decision-making.
• Feature Information Fusion (FIF) is applied for global decision-making and information optimization.
• A 13.2 m wind turbine blade with a distributed strain sensor system was used for validation.
• The Strain Energy Method (SEM) was employed to demonstrate the method's advantages.

Main Results:

• The Chi-square distribution proved effective as a damage-sensitive feature.
• The Feature Information Fusion (FIF) method enabled accurate global damage detection.
• Experimental validation on a 13.2 m blade confirmed the method's feasibility.
• The proposed method demonstrated encouraging results in detecting wind turbine blade damage.

Conclusions:

• The developed non-baseline damage detection method using FBG is effective for wind turbine blades.
• The combination of Chi-square distribution and FIF provides a robust approach to damage detection.
• This method offers a promising solution for improving wind turbine operational safety and reliability.