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A hybrid system identification methodology for wireless structural health monitoring systems based on dynamic substructuring
Publikationstyp
Conference Paper
Date Issued
2016-03
Sprache
English
Author(s)
Smarsly, Kay
Volume
9803
Article Number
98033F
Citation
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems (2016)
Contribution to Conference
Publisher DOI
Scopus ID
System identification has been employed in numerous structural health monitoring (SHM) applications. Traditional system identification methods usually rely on centralized processing of structural response data to extract information on structural parameters. However, in wireless SHM systems the centralized processing of structural response data introduces a significant communication bottleneck. Exploiting the merits of decentralization and on-board processing power of wireless SHM systems, many system identification methods have been successfully implemented in wireless sensor networks. While several system identification approaches for wireless SHM systems have been proposed, little attention has been paid to obtaining information on the physical parameters (e.g. stiffness, damping) of the monitored structure. This paper presents a hybrid system identification methodology suitable for wireless sensor networks based on the principles of component mode synthesis (dynamic substructuring). A numerical model of the monitored structure is embedded into the wireless sensor nodes in a distributed manner, i.e. The entire model is segmented into sub-models, each embedded into one sensor node corresponding to the substructure the sensor node is assigned to. The parameters of each sub-model are estimated by extracting local mode shapes and by applying the equations of the Craig-Bampton method on dynamic substructuring. The proposed methodology is validated in a laboratory test conducted on a four-story frame structure to demonstrate the ability of the methodology to yield accurate estimates of stiffness parameters. Finally, the test results are discussed and an outlook on future research directions is provided.
Subjects
Dynamic substructuring.
Embedded numerical models
Structural health monitoring
System identification
Wireless sensor networks