Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration

• URL: http://arxiv.org/abs/2308.11644v1
• Date: Thu, 17 Aug 2023 00:41:50 GMT
• Title: Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration
• Authors: Yang Yu, Han Chen
• Abstract summary: This paper introduces a novel deep learning algorithm tailored for the complexities inherent in multimodal vibration signals prevalent in Structural Health Monitoring (SHM) By amalgamating convolutional and recurrent architectures, the algorithm adeptly captures both localized and prolonged structural behaviors. Our results showcase significant improvements in predictive accuracy, early damage detection, and adaptability across multiple SHM scenarios.
• Score: 9.144905626316534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structural Health Monitoring (SHM) plays an indispensable role in ensuring the longevity and safety of infrastructure. With the rapid growth of sensor technology, the volume of data generated from various structures has seen an unprecedented surge, bringing forth challenges in efficient analysis and interpretation. This paper introduces a novel deep learning algorithm tailored for the complexities inherent in multimodal vibration signals prevalent in SHM. By amalgamating convolutional and recurrent architectures, the algorithm adeptly captures both localized and prolonged structural behaviors. The pivotal integration of attention mechanisms further enhances the model's capability, allowing it to discern and prioritize salient structural responses from extraneous noise. Our results showcase significant improvements in predictive accuracy, early damage detection, and adaptability across multiple SHM scenarios. In light of the critical nature of SHM, the proposed approach not only offers a robust analytical tool but also paves the way for more transparent and interpretable AI-driven SHM solutions. Future prospects include real-time processing, integration with external environmental factors, and a deeper emphasis on model interpretability.

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