ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal Structural Behavior

TL;DR

ModeConv introduces a new convolutional layer for temporal graph neural networks that enhances anomaly detection in structural vibrations by efficiently analyzing eigenmode changes, outperforming traditional spectral methods.

Contribution

The paper presents ModeConv, a novel SVD-based convolutional layer tailored for temporal graph neural networks to improve structural anomaly detection.

Findings

ModeConv reduces computational runtime significantly.

It effectively captures eigenmode changes for anomaly detection.

The method outperforms traditional spectral graph convolutions.

Abstract

External influences such as traffic and environmental factors induce vibrations in structures, leading to material degradation over time. These vibrations result in cracks due to the material's lack of plasticity compromising structural integrity. Detecting such damage requires the installation of vibration sensors to capture the internal dynamics. However, distinguishing relevant eigenmodes from external noise necessitates the use of Deep Learning models. The detection of changes in eigenmodes can be used to anticipate these shifts in material properties and to discern between normal and anomalous structural behavior. Eigenmodes, representing characteristic vibration patterns, provide insights into structural dynamics and deviations from expected states. Thus, we propose ModeConv to automatically capture and analyze changes in eigenmodes, facilitating effective anomaly detection in structures and material properties. In the conducted experiments, ModeConv demonstrates computational efficiency improvements, resulting in reduced runtime for model calculations. The novel ModeConv neural network layer is tailored for temporal graph neural networks, in which every node represents one sensor. ModeConv employs a singular value decomposition based convolutional filter design for complex numbers and leverages modal transformation in lieu of Fourier or Laplace transformations in spectral graph convolutions. We include a mathematical complexity analysis illustrating the runtime reduction.