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Substructure Vibration NARX Neural Network Approach for Statistical Damage Inference.
- Source :
- Journal of Engineering Mechanics; Jun2013, Vol. 139 Issue 6, p737-747, 11p, 2 Diagrams, 5 Charts, 7 Graphs
- Publication Year :
- 2013
-
Abstract
- A damage detection approach is developed using nonlinear autoregressive with exogenous inputs (NARX) neural networks and a statistical inference technique. Within a large spatially extended dynamic system, an instrumented local substructure may be represented by a neural network, to predict the dynamic response of a given sensor from that of its neighbors. Without change in the system properties, the network prediction error will follow a stable statistical distribution. To infer damage, change in the prediction error variance as evaluated by the statistical inference standard test is utilized as a sensitive indicator. Validation of the described procedure is undertaken using two experimental data sets (from the Los Alamos National Laboratory in Los Alamos, NM). Reduced stiffness and nonlinear response of a mass-spring system is documented in the first set, while joint damage in a frame structure is explored in the second. Favorable results are obtained in both cases with linear/nonlinear and single/multidamage patterns. Overall, the proposed framework may be particularly efficient for large spatially extended sensor network situations, where local condition assessment may be conducted based on the response of a few neighboring sensors. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 07339399
- Volume :
- 139
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Journal of Engineering Mechanics
- Publication Type :
- Academic Journal
- Accession number :
- 87499294
- Full Text :
- https://doi.org/10.1061/(ASCE)EM.1943-7889.0000363