融合频谱与时间特征，提升结构健康监测精度与稳定性。
Hybrid Spectro-Temporal Fusion Framework for Structural Health Monitoring
- 结合到达时间间隔与频谱特征，捕捉振动的精细与粗粒度动态。
- 在0.008时间分辨率下，深度学习模型性能显著优于传统方法。
- 相比基线方法，结果更稳定，准确率更高，适合工程可靠性评估。
结构健康监测通过分析工程系统的振动响应确保结构安全。本文提出一种谱-时序对齐框架与混合谱-时序融合框架，将到达时间间隔描述符与谱特征结合，以捕捉振动的细粒度和粗粒度动态。基于LDS V406电磁激振器采集的数据实验表明，所提出的谱-时序表示显著优于传统输入形式。结果表明，当时间分辨率Δτ为0.02时，传统机器学习模型表现更优；而Δτ为0.008时，深度学习架构性能潜力被充分释放。除分类准确率外，基于均值、标准差、变异系数及平衡分数等压缩指标的综合稳定性分析显示，该混合框架在准确率与变异性上均显著优于基线与仅对齐方法。总体而言，该框架为基于振动的结构健康监测提供了鲁棒、准确且可靠的新方案。
原文摘要 · Abstract (English)
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.
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