Fully convolutional networks for structural health monitoring through multivariate time series classification

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...

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Bibliographic Details
Main Authors: Luca Rosafalco, Andrea Manzoni, Stefano Mariani, Alberto Corigliano
Format: Article
Language:English
Published: SpringerOpen 2020-09-01
Series:Advanced Modeling and Simulation in Engineering Sciences
Subjects:
Structural health monitoring
Fully convolutional networks
Damage localization
Time series analysis
Deep learning
Online Access:http://link.springer.com/article/10.1186/s40323-020-00174-1

Internet

http://link.springer.com/article/10.1186/s40323-020-00174-1

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