Fully convolutional networks for structural health monitoring through multivariate time series classification
Abstract We propose a novel approach to structural health monitoring (SHM), aiming at the automatic identification of damage-sensitive features from data acquired through pervasive sensor systems. Damage detection and localization are formulated as classification problems, and tackled through fully...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
SpringerOpen
2020-09-01
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Series: | Advanced Modeling and Simulation in Engineering Sciences |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s40323-020-00174-1 |