Damage Identification in Composite Wind Turbine Blades Using Relative Natural Frequency Changes and Bayesian...

Area of Science:

• Engineering
• Materials Science
• Mechanical Engineering

Background:

• Structural health monitoring (SHM) is vital for wind turbine blade (WTB) reliability and efficiency.
• Traditional frequency-based methods struggle with composite WTB complexity and damage localization.
• Natural frequencies are global indicators sensitive to environmental changes, limiting local damage detection.

Purpose of the Study:

• To develop an advanced SHM method for composite WTBs.
• To overcome limitations of traditional frequency-based damage detection.
• To accurately locate damage in composite WTBs using a novel approach.

Main Methods:

• Introduced the Bayesian relative natural frequency change (B-RNFC) method.
• Analyzed natural frequency changes correlated with damage in composite beams and WTBs.
• Developed normalized RNFC curves and integrated them with Bayesian probability for damage mapping.
• Evaluated B-RNFC performance for single and multiple damage localization.

Main Results:

• The B-RNFC method demonstrated effective damage localization.
• Effective localization range for cantilever beams: 20-80% from the fixed end.
• Effective localization range for composite WTBs: 40-80% of blade length from the root.
• Successfully localized dual damages, including symmetric and mid-span scenarios.

Conclusions:

• The B-RNFC method offers a robust solution for SHM in composite WTBs.
• This approach enhances damage localization accuracy compared to traditional methods.
• The B-RNFC method contributes to improved WTB reliability and reduced maintenance costs.