Hybrid Spectro-Temporal Fusion Framework for Structural Health Monitoring
For structural health monitoring practitioners, this work offers a more robust and accurate vibration analysis method, though it is an incremental improvement over existing approaches.
The paper proposes a hybrid spectro-temporal fusion framework for structural health monitoring that integrates arrival-time interval descriptors with spectral features. Experiments on shaker data show the framework achieves higher accuracy and lower variability compared to baselines, with temporal resolution of 0.008 benefiting deep learning models.
Structural health monitoring plays a critical role in ensuring structural safety by analyzing vibration responses from engineering systems. This paper proposes a Spectro-Temporal Alignment framework and a Hybrid Spectro-Temporal Fusion framework that integrate arrival-time interval descriptors with spectral features to capture both fine-scale and coarse-scale vibration dynamics. Experiments conducted on data collected from an LDS V406 electrodynamic shaker demonstrate that the proposed spectro-temporal representations significantly outperform conventional input formulations. The results indicate that a temporal resolution (Δτ) of 0.008 of 0.02 favors traditional machine learning models, whereas a finer resolution (Δτ) of 0.008 effectively unlocks the performance potential of deep learning architectures. Beyond classification accuracy, a comprehensive stability analysis based on condensed indices, including mean performance, standard deviation, coefficient of variation, and balanced score, shows that the proposed hybrid framework consistently achieves higher accuracy with substantially lower variability compared to baseline and alignment-only approaches. Overall, these results demonstrate that the proposed framework provides a robust, accurate, and reliable solution for vibration-based structural health monitoring.