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

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Schaller, Melanie, Schlör, Daniel, Hotho, Andreas
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.06.2024
Subjects
Anomalies
Change detection
Complex numbers
Complexity
Damage detection
Effectiveness
Filter design (mathematics)
Graph neural networks
Material properties
Neural networks
Singular value decomposition
Structural behavior
Structural integrity
Vibration
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Summary: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.
ISSN:2331-8422