Simulation-Based Classification; a Model-Order-Reduction Approach for Structural Health Monitoring

Simulation-Based Classification; a Model-Order-Reduction Approach for Structural Health Monitoring We present a model-order-reduction approach to simulation-based classification, with particular application to structural health monitoring. The approach exploits (1) synthetic results obtained by repeated solution of a parametrized mathematical model for different values of the parameters, (2) machine-learning algorithms to generate a classifier that monitors the damage state of the system, and (3) a reduced basis method to reduce the computational burden associated with the model evaluations. Furthermore, we propose a mathematical formulation which integrates the partial differential equation model within the classification framework and clarifies the influence of model error on classification performance. We illustrate our approach and we demonstrate its effectiveness through the vehicle of a particular physical companion experiment, a harmonically excited microtruss. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Computational Methods in Engineering Springer Journals

Simulation-Based Classification; a Model-Order-Reduction Approach for Structural Health Monitoring

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Publisher
Springer Netherlands
Copyright
Copyright © 2016 by CIMNE, Barcelona, Spain
Subject
Engineering; Mathematical and Computational Engineering
ISSN
1134-3060
eISSN
1886-1784
D.O.I.
10.1007/s11831-016-9185-0
Publisher site
See Article on Publisher Site

Abstract

We present a model-order-reduction approach to simulation-based classification, with particular application to structural health monitoring. The approach exploits (1) synthetic results obtained by repeated solution of a parametrized mathematical model for different values of the parameters, (2) machine-learning algorithms to generate a classifier that monitors the damage state of the system, and (3) a reduced basis method to reduce the computational burden associated with the model evaluations. Furthermore, we propose a mathematical formulation which integrates the partial differential equation model within the classification framework and clarifies the influence of model error on classification performance. We illustrate our approach and we demonstrate its effectiveness through the vehicle of a particular physical companion experiment, a harmonically excited microtruss.

Journal

Archives of Computational Methods in EngineeringSpringer Journals

Published: Aug 5, 2016

References

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