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A hierarchical multistage holistic model for acoustic emission source monitoring in composites
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DOI：10.1088/1361-665X/ad8409.png)
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Abstract
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This paper introduces a multistage smart structural health monitoring (SHM) model for carbon-fibre composites, with a focus on multiple types of acoustic emission (AE) source localization;
classification. The SHM model uses time-frequency data from various AE events (such as tool drops, impact,;
artificial debonding) across different zones of a composite structure. The SHM strategy demonstrates a robust smart monitoring of composites with high accuracy. Further, a hypothesis testing has been carried out that supports the superiority of a 2-stage identification process, revealing statistically significant higher accuracy;
confidence intervals across all zones;
AE source types. This research establishes a novel framework for solving a hierarchical multistage holistic damage source identification problem, offering robustness in identifying various damage scenarios;
quantifying associated prediction uncertainties.
Keywords:
acoustic emission
composites
deep learning
smart monitoring
uncertainty quantification
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