Simulation-Based Classification; a Model-Order-Reduction Approach for Structural Health Monitoring
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11831_2016_9185_ReferencePDF.pdf
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1.94 MB
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Author(s)
Yano, M.
Taddei, Tommaso
Penn, James Douglass
Patera, Anthony T
Date Issued
August 2016
Journal
Archives of Computational Methods in Engineering
Publisher
Springer Netherlands
Citation
Taddei, T., et al. “Simulation-Based Classification; a Model-Order-Reduction Approach for Structural Health Monitoring.” Archives of Computational Methods in Engineering, vol. 25, no. 1, Jan. 2018, pp. 23–45.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s11831-016-9185-0
