TVML融合时空分析，智能选点降低传感器冗余。
Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoring
- 结合图信号与时间序列分析，识别关键传感节点
- 在两座桥梁数据上实现损伤检测与信号重建
- 适合需要降本增效的大型结构监测场景
结构健康监测（SHM）对保障基础设施安全至关重要。随着传感器网络规模扩大，如何在不降低监测质量的前提下减少部署成本成为关键挑战。传统方法虽利用传感器间的空间相关性，但常忽略结构行为的时间动态特性。为此，本文提出时间-顶点机器学习（TVML）框架，融合图信号处理（GSP）、时域分析与机器学习，通过识别代表性节点来最小化冗余并保留关键信息，实现可解释且高效的传感器部署。在两个桥梁数据集上评估了其在损伤检测与时变图信号重建任务中的表现，结果表明该方法能显著提升SHM系统的鲁棒性、适应性与效率。
原文摘要 · Abstract (English)
Structural Health Monitoring (SHM) plays a crucial role in maintaining the safety and resilience of infrastructure. As sensor networks grow in scale and complexity, identifying the most informative sensors becomes essential to reduce deployment costs without compromising monitoring quality. While Graph Signal Processing (GSP) has shown promise by leveraging spatial correlations among sensor nodes, conventional approaches often overlook the temporal dynamics of structural behavior. To overcome this limitation, we propose Time-Vertex Machine Learning (TVML), a novel framework that integrates GSP, time-domain analysis, and machine learning to enable interpretable and efficient sensor placement by identifying representative nodes that minimize redundancy while preserving critical information. We evaluate the proposed approach on two bridge datasets for damage detection and time-varying graph signal reconstruction tasks. The results demonstrate the effectiveness of our approach in enhancing SHM systems by providing a robust, adaptive, and efficient solution for sensor placement.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。