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Graph Neural Network-Based Measurement Inference on Irregular Sensor Geometries

Martin Ben Ahmed; Niklas Wilming; Martin Atzmueller
In: 2023 IEEE 21st International Conference on Industrial Informatics (INDIN). IEEE International Conference on Industrial Informatics (INDIN), Pages 1-8, IEEE, 2023.


In general, Graph Neural Networks (GNNs) enable modeling and learning in the context of complex data like graphs and time series – which is typically difficult for standard Deep Learning approaches. This paper proposes an approach for modeling a prediction task using irregularly sampled spatio-temporal sensor data via GNNs. Specifically, we present a method for modeling spatio-temporal sensor data represented as graphs, and a more convenient image representation enabling standard convolutional deep learning. By mapping the irregularly sampled graph to a regular graph representation, we can then integrate temporal sensor information with spatial information. In our experimentation, we demonstrate the efficacy of the proposed approach in inspection contexts of oil and gas pipelines.