Deep learning-assisted structural health monitoring: acoustic emission analysis and domain adaptation with intelligent fiber optic signal processing

Structural health monitoring aims to detect damage progression in materials. This study focuses on categorizing crack stages, a critical aspect of monitoring structural integrity. By leveraging acoustic emission (AE) monitoring, cracks can be analyzed in a data-driven manner. However, applying AE analysis poses several challenges, including discrepancies between simulated AE data from models and experimental data from the field, as well as class imbalance in crack progression data, with a scarcity of late-stage data. To bridge the gap between theory and experiments, our approach employs domain adaptation to synchronize simulated and actual AE data. The model learns robust domain-invariant features through meticulous experimentation across training epochs. Quantitative analysis of the model's performance provides key insights. F1 scores vary with feature counts, and domain adaptation outperforms by 20% on highly imbalanced datasets. This emphasizes the model's adaptability for precise crack classification, even with underrepresented damage classes. In summary, this study advances structural health monitoring by offering a solid AE analysis approach. Core contributions include reconciling simulated and experimental data discrepancies, tackling class imbalance, optimizing feature extraction, and demonstrating robust crack stage categorization. The insights gained highlight the merits of domain adaptation and data-driven AE analysis for predicting crack progression.