Experimental Verification of Decentralized Approach for Modal Identification Based on Wireless Smart Sensor Network


Article Preview

This paper provides an experimental verification of decentralized approach for modal test and analysis of a 30 meters long railway overpass bridge. 11 Imote2 smart sensor nodes were implemented on the WSSN. In order to compare the identification precision of different topologies, acceleration responses were obtained under centralized and 3 different decentralized topologies. Local modal parameters were estimated by NExT/ERA within each local group; true modes were then distinguished from spurious modes by EMAC and finite-element analysis. In order to estimate global mode shape, a least square method was used for calculating the normalization factor. Then the global mode shapes were determined by normalization factors and local mode shapes. The result demonstrates that the more overlapping nodes in each group, the more accurate the global mode shape will be; the decentralized approach is workable for modal test of large-scale bridge.



Advanced Materials Research (Volumes 291-294)

Edited by:

Yungang Li, Pengcheng Wang, Liqun Ai, Xiaoming Sang and Jinglong Bu




X. J. Ye et al., "Experimental Verification of Decentralized Approach for Modal Identification Based on Wireless Smart Sensor Network", Advanced Materials Research, Vols. 291-294, pp. 3-11, 2011

Online since:

July 2011




[1] Huibin Li, Quan Qin, Liangzhong Qian, C. K Lau. Time Domain Modal Identification of Tsing Ma Suspension Bridge[C]. Proc. of IMAC 19, Kissimmee, Florida, USA, 2001, 1585-1591.

[2] Spencer BF Jr, Ruiz-Sandoval M, Krata N. Smart sensing technology: opportunities and challenges[J]. Journal of Structural Control and Health Monitoring 2004; 11: 349–368.

DOI: https://doi.org/10.1002/stc.48

[3] Straser E.G. & Kiremidjian A.S. A Modular Visual Approach to Damage Monitoring for Civil Structures[C]. Proceedings of SPIE, Smart Structures and Materials. 1996, Vol. 2719: 112~122.

[4] J.P. Lynch, K.H. Law, E. G. Straser. The Development of a Wireless Modular Health Monitoring System for Civil Structures[C]. Proceedings of the MCEER Mitigation of Earthquake Disaster by Advanced Technologies (MEDAT-2) Workshop, Las Vegas, NV, USA, 2000, November 30-31.

[5] J. P Lynch, A Sundararajan, K. H . Law, et al. Field validation of a wireless structural monitoring system on the Alamosa Canyon Bridge[C]. Proceeding of SPIE, Smart Structures and Materials, San Diego, CA, USA, 2003, Vol. 5057, 267 -278.

DOI: https://doi.org/10.1117/12.482712

[6] Crossbow Technology. http: /www. xbow. com.

[7] Shinae Jang, Hongki Jo, Soojin Cho, et al. Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation[J]. Journal of Smart Structures and systems, Vol. 6, No. 5-6(2010) 439-459.

DOI: https://doi.org/10.12989/sss.2010.6.5_6.439

[8] Gao Y, Spencer BF Jr, Ruiz-Sandoval M. Distributed computing strategy for structural health monitoring[J]. Journal of Structural Control and Health Monitoring 2006; 13(1): 488-507.

DOI: https://doi.org/10.1002/stc.117

[9] Zimmerman A T, Shiraishi M, Swartz R A, et al. Automated modal parameter estimation by parallel processing within wireless monitoring systems [ J ]. Journal of Infrastructure Systems, 2008, 14 (1) : 1022113.

DOI: https://doi.org/10.1061/(asce)1076-0342(2008)14:1(102)

[10] Farrar, C.R. and James, G.H., System identification from ambient vibration measurements on a bridge[J]. Journal of Sound and Vibration, 1997; 205(1): 1-18.

DOI: https://doi.org/10.1006/jsvi.1997.0977

[11] J.N. Juang and R. S. Pappa. An Eigensystem Realization Algorithm (ERA) for Modal Parameter Identification and Model Reduction[J]. Journal of Guidance, Control, and Dynamics. 1985, 8(5): 620-627.

DOI: https://doi.org/10.2514/3.20031

[12] Sim S. H, Spencer B. F Jr, Zhang M, et al. Automated Decentralized Modal Analysis using Smart Sensors[J]. Journal of Structural Control and Health Monitoring, 2009, DOI: 10. 1002/stc. 348.