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Acoustic emission source localization in thin metallic plates: A single-sensor approach based on multimodal edge
A Ebrahimkhanlou1, S Salamone1
1Smart Structures Research Laboratory (SSRL), Department of Civil Architectural and Environmental Engineering, The University of Texas at Austin, 10100 Burnet Rd, Bldg. 177, Austin, TX 78758, United States.
This study introduces a novel acoustic emission (AE) localization method for plates, using single-sensor edge reflections to pinpoint AE sources without blind spots. Promising results were achieved in aluminum plate tests.
Area of Science:
- Materials Science
- Structural Health Monitoring
- Acoustics
Background:
- Acoustic emission (AE) is a crucial technique for structural health monitoring.
- Accurate source localization is essential for effective AE analysis.
- Existing methods often struggle with complex boundary conditions or require multiple sensors.
Purpose of the Study:
- To develop a novel AE source localization technique for isotropic plates with reflecting boundaries.
- To enable AE source identification using a single sensor, eliminating blind spots.
- To validate the proposed method through experimental testing.
Main Methods:
- Utilizing continuous wavelet transform (CWT) and Lamb wave dispersion curves for distance estimation (modal acoustic emission).
- Developing an analytical model to simulate edge-reflected waves based on estimated distances.
- Employing waveform correlation between experimental and simulated data for source localization.
Main Results:
- Successful localization of acoustic emission sources on an aluminum plate.
- Demonstration of the method's ability to handle reflecting boundaries.
- Achieved promising localization accuracy, with reported statistics on localization errors.
Conclusions:
- The proposed single-sensor AE localization approach effectively utilizes multimodal edge reflections.
- This method offers a promising solution for AE source identification in plate-like structures with reflecting boundaries.
- The technique shows potential for enhanced structural health monitoring applications.
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