Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoring

TL;DR

This paper introduces Time-Vertex Machine Learning (TVML), a new framework combining graph signal processing, temporal analysis, and machine learning to optimize sensor placement in structural health monitoring, reducing costs while maintaining detection accuracy.

Contribution

The paper presents a novel TVML framework that integrates GSP, temporal data, and machine learning for interpretable and efficient sensor placement in SHM.

Findings

Effective sensor placement for damage detection

Improved graph signal reconstruction accuracy

Robustness to structural changes

Abstract

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.