A Computational Framework for Modeling Complex Sensor Network Data Using
Graph Signal Processing and Graph Neural Networks in Structural Health
Monitoring
- URL: http://arxiv.org/abs/2105.05316v1
- Date: Sat, 1 May 2021 10:45:57 GMT
- Title: A Computational Framework for Modeling Complex Sensor Network Data Using
Graph Signal Processing and Graph Neural Networks in Structural Health
Monitoring
- Authors: Stefan Bloemheuvel, Jurgen van den Hoogen, Martin Atzmueller
- Abstract summary: We present a framework based on Complex Network Modeling, integrating Graph Signal Processing (GSP) and Graph Neural Network (GNN) approaches.
We focus on a prominent real-world structural health monitoring use case, i.e., modeling and analyzing sensor data (strain, vibration) of a large bridge in the Netherlands.
- Score: 0.7519872646378835
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Complex networks lend themselves to the modeling of multidimensional data,
such as relational and/or temporal data. In particular, when such complex data
and their inherent relationships need to be formalized, complex network
modeling and its resulting graph representations enable a wide range of
powerful options. In this paper, we target this - connected to specific machine
learning approaches on graphs for structural health monitoring on an analysis
and predictive (maintenance) perspective. Specifically, we present a framework
based on Complex Network Modeling, integrating Graph Signal Processing (GSP)
and Graph Neural Network (GNN) approaches. We demonstrate this framework in our
targeted application domain of Structural Health Monitoring (SHM). In
particular, we focus on a prominent real-world structural health monitoring use
case, i.e., modeling and analyzing sensor data (strain, vibration) of a large
bridge in the Netherlands. In our experiments, we show that GSP enables the
identification of the most important sensors, for which we investigate a set of
search and optimization approaches. Furthermore, GSP enables the detection of
specific graph signal patterns (mode shapes), capturing physical functional
properties of the sensors in the applied complex network. In addition, we show
the efficacy of applying GNNs for strain prediction on this kind of data.
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