Substructure Vibration NARX Neural Network Approach for Statistical Damage Inference
Publication: Journal of Engineering Mechanics
Volume 139, Issue 6
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.
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Acknowledgments
Support for this research was provided by the U.S. National Science Foundation, under ITR Grant No. 0205720. This support is gratefully acknowledged. The authors also wish to thank the California Institute for Telecommunications and Information Technology (UCSD Calit2, http://www.calit2.net) for providing fellowship support to this project. Finally, the experimental data sets studied in this paper were provided by Dr. Francois Hemez and Dr. Charles Farrar at Los Alamos National Laboratory and Professor Hoon Sohn at Carnegie Mellon University. The authors are most grateful for this valuable contribution.
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Published In
Journal of Engineering Mechanics
Volume 139 • Issue 6 • June 2013
Pages: 737 - 747
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Sep 28, 2010
Accepted: Dec 5, 2011
Published online: Dec 8, 2011
Published in print: Jun 1, 2013
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