Compensation of Temperature Effects on Lamb Waves Using Mode Decomposition and a Nonlinear Model

Area of Science:

• Structural Health Monitoring
• Non-Destructive Testing
• Wave Propagation

Background:

• Lamb-wave-based structural health monitoring (SHM) relies on comparing baseline signals with inspection signals.
• Temperature fluctuations significantly impact wave propagation, degrading the performance of traditional baseline comparison methods.
• Existing approximate temperature compensation methods are insufficient for large temperature variations or long propagation distances.

Purpose of the Study:

• To develop and validate a novel data-driven temperature compensation algorithm for Lamb-wave SHM.
• To improve the accuracy of damage detection in structures subjected to significant temperature changes.
• To outperform conventional baseline signal stretch (BSS) methods in temperature compensation.

Main Methods:

• A data-driven approach is proposed to compensate for temperature effects on different Lamb-wave mode components separately.
• Experimental signals from aluminum and composite panels were used for performance evaluation.
• The proposed method's performance was compared against the widely used baseline signal stretch (BSS) algorithm.

Main Results:

• The proposed data-driven method demonstrates superior performance compared to the BSS algorithm, particularly under large temperature differences.
• The temperature compensation algorithm effectively preserves signal integrity despite varying thermal conditions.
• Compensated signals enabled accurate reconstruction of anomaly maps for damaged composite structures.

Conclusions:

• The developed data-driven temperature compensation algorithm offers a significant advancement for Lamb-wave SHM.
• This method enhances the reliability and accuracy of structural health monitoring in environments with substantial temperature fluctuations.
• The validated approach provides a robust solution for inspecting large structures under challenging operational conditions.