Autonomous Decentralized System Identification by Markov Parameter Estimation Using Distributed Smart Wireless Sensor Networks
Publication: Journal of Engineering Mechanics
Volume 138, Issue 5
Abstract
Decentralized data processing has the benefit of improving wireless monitoring system scalability, reducing the amount of wireless communications, and reducing overall power consumption. In this study, a system identification strategy for single-input multi-output (SIMO) subspace system identification is proposed based on Markov parameters. The method is specifically customized for embedment within the decentralized computational framework of a wireless sensor network. By using the computational resources of wireless sensors, individual sensor nodes perform local data processing to identify the Markov parameters of a structural system. The data storage and wireless communication requirements of Markov parameters are significantly less than that required by the original raw data, resulting in the preservation of scarce system resources such as communication bandwidth and battery power. Then, the estimated Markov parameters are wirelessly communicated to a wireless sensor network base station where the global structural properties are assembled by execution of the eigensystem realization algorithm, an indirect subspace system identification method. The proposed strategy is evaluated using input-output and output-only data recorded during dynamic testing of a cantilevered balcony in a historic building (Hill Auditorium, Ann Arbor, MI).
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Acknowledgments
The authors would like to gratefully acknowledge the generous support offered by the National Science Foundation under Grant CMMI-0726812 (Program Manager: Dr. S. C. Liu). Additional support was provided by the NIST Technology Innovation Program (Contract 70NANB9H9008). The authors would also like to thank Dr. Andrew Zimmerman, Mr. Kurt Thoma and Mr. Sean O’Connor (University of Michigan) for assistance during the experimental phase at Hill Auditorium.
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 138 • Issue 5 • May 2012
Pages: 478 - 490
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Oct 28, 2010
Accepted: Nov 28, 2011
Published online: Dec 1, 2011
Published in print: May 1, 2012
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