Development and Application of High-Sensitivity Wireless Smart Sensors for Decentralized Stochastic Modal Identification

AbstractState-of-the-art smart sensor technology enables deployment of dense arrays of sensors, which is critical for structural health monitoring (SHM) of complicated and large-scale civil structures. Despite recent successful implementation of various wireless smart sensor networks (WSSNs) for ful...

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Veröffentlicht in:Journal of engineering mechanics 2012-06, Vol.138 (6), p.683-694
Hauptverfasser: Jo, Hongki, Sim, Sung-Han, Nagayama, Tomonori, Spencer, B. F
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Sprache:eng
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Zusammenfassung:AbstractState-of-the-art smart sensor technology enables deployment of dense arrays of sensors, which is critical for structural health monitoring (SHM) of complicated and large-scale civil structures. Despite recent successful implementation of various wireless smart sensor networks (WSSNs) for full-scale SHM, the low-cost micro-electro-mechanical systems (MEMS) sensors commonly used in smart sensors cannot readily measure low-level ambient vibrations because of their relatively low resolution. Combined use of conventional wired high-sensitivity sensors with low-cost wireless smart sensors has been shown to provide improved spectral estimates of response that can lead to improved experimental modal analysis. However, such a heterogeneous network of wired and wireless sensors requires central collection of an enormous amount of raw data and off-network processing to achieve global time synchronization; consequently, many of the advantages of WSSNs for SHM are lost. In this paper, the development of a new high-sensitivity accelerometer board (SHM-H) for the Imote2 wireless smart sensor (WSS) platform is presented. The use of a small number of these high-sensitivity WSSs, composed of the SHM-H and Imote2, as reference sensors in the Natural Excitation Technique—based decentralized WSSN strategy is explored and is shown to provide a cost-effective means of improving modal feature extraction in the decentralized WSSN for SHM.
ISSN:0733-9399
1943-7889
DOI:10.1061/(ASCE)EM.1943-7889.0000352