Development and Application of High-Sensitivity Wireless Smart Sensors for Decentralized Stochastic Modal Identification
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- Development and Application of High-Sensitivity Wireless Smart Sensors for Decentralized Stochastic Modal Identification
- Jo, Hongki; Sim, Sung-Han; Nagayama, Tomonori; Spencer, B. F., Jr.
- Ambient vibrations; Civil structure; Cost-effective means; Dense arrays; Experimental modal analysis; Global time synchronizations; High-sensitivity; High-sensitivity sensor; Low resolution; Modal identification; Natural excitations; Reference sensors; Smart sensor technology; Spectral estimate; Wired and wireless; Wireless smart sensors
- Issue Date
- ASCE-AMER SOC CIVIL ENGINEERS
- JOURNAL OF ENGINEERING MECHANICS-ASCE, v.138, no.6, pp.683 - 694
- State-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 highsensitivity 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.
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