An Acoustic Emission Beamforming Localization Method for Continuous and Burst Signals With Dispersion Curves Compensation

When an object experiences external impacts or internal damage such as cracks, it generates a transient elastic wave known as an acoustic emission (AE) signal. According to the characteristics of AE signals, they can be classified into burst and continuous signals. Current acoustic localization methods mostly target single-type signals. When both types of signals coexist, they interfere with each other, increasing the difficulty of localization. To address the impact of signal aliasing on localization, this article introduces a beamforming localization method with dispersion curve compensation. First, the signal types are identified by using kurtosis thresholds. When both types exist simultaneously, an adaptive energy threshold method is employed to determine the arrival and end times of burst signals, separating them from continuous signals to reconstruct the signals. Post reconstruction, filter bands are selected based on energy distribution, and multiple beamforming is conducted on the filtered acoustic signals with a velocity change of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10~{m}$ </tex-math></inline-formula>/s, combining energies to achieve source localization. Experimental results demonstrate that when both types coexist, the maximum localization error for burst sources is 3.6 cm with an average error of 2.04 cm, while for continuous sources, the maximum localization error is 2 cm with an average error of 1.32 cm. This shows that the beamforming localization method with dispersion curves compensation can effectively localize both burst-type and continuous-type acoustic sources simultaneously.

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Publication Details

Journal
IEEE Sensors Journal
Published
2025-03-04
DOI
https://doi.org/10.1109/jsen.2025.3544238
Primary Topic
Speech and Audio Processing
Type
article
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article

An Acoustic Emission Beamforming Localization Method for Continuous and Burst Signals With Dispersion Curves Compensation

Yu Feng, Xiaobo Rui, Zhoumo Zeng, Lei Qi et al.
IEEE Sensors Journal
Speech and Audio Processing
article

An Acoustic Emission Beamforming Localization Method for Continuous and Burst Signals With Dispersion Curves Compensation

Yu Feng, Xiaobo Rui, Zhoumo Zeng, Lei Qi, Yu Zhang, Lixin Xu, S. L. Niu
article en

Abstract

When an object experiences external impacts or internal damage such as cracks, it generates a transient elastic wave known as an acoustic emission (AE) signal. According to the characteristics of AE signals, they can be classified into burst and continuous signals. Current acoustic localization methods mostly target single-type signals. When both types of signals coexist, they interfere with each other, increasing the difficulty of localization. To address the impact of signal aliasing on localization, this article introduces a beamforming localization method with dispersion curve compensation. First, the signal types are identified by using kurtosis thresholds. When both types exist simultaneously, an adaptive energy threshold method is employed to determine the arrival and end times of burst signals, separating them from continuous signals to reconstruct the signals. Post reconstruction, filter bands are selected based on energy distribution, and multiple beamforming is conducted on the filtered acoustic signals with a velocity change of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10~{m}$ </tex-math></inline-formula>/s, combining energies to achieve source localization. Experimental results demonstrate that when both types coexist, the maximum localization error for burst sources is 3.6 cm with an average error of 2.04 cm, while for continuous sources, the maximum localization error is 2 cm with an average error of 1.32 cm. This shows that the beamforming localization method with dispersion curves compensation can effectively localize both burst-type and continuous-type acoustic sources simultaneously.

IEEE Sensors JournalVol. 25(8)
Tianjin University (CN), China Academy of Space Technology (CN)
Climate action
Openalex Percentile: Top 96%
Speech and Audio Processing
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