Computationally Efficient Structural Health Monitoring Using Graph Signal Processing

Abstract

Structural health monitoring (SHM) of bridges is crucial for ensuring safety and long-term durability, however, standard damage-detection algorithms are computationally intensive. This paper proposes a computationally-efficient algorithm based on graph signal processing (GSP) to leverage the underlying network structure in the data. Under the assumption that damages impact both spatial and temporal structures of the sensors data, the algorithm combines spatial and temporal information from accelerometers by computing the smoothness of graph signals expanded along time. The Kullback-Leibler (KL) divergence is used as dissimilarity metric to distinguish between healthy condition and presence of a damage, while Tukey's method for outliers removal and sequential detection via exponential Weighted moving average (EWMA) are then employed for performance improvement. The proposed GSP-based SHM system is appealing in terms of simplicity and low-complexity, and is also suitable for realtime monitoring. The effectiveness in terms of detection performance is validated both on synthetically-generated data and real-world measurements.